Disse datasettene fantes i WP før import

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NORSUS (3.6)

Alle tags

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process:

Alle prosesser

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            [ProjectId] => 42857b44-1699-43c3-9de7-92a8c9e144f6
            [Name] => Wood chips, beech; wet, manual harvest, hardwood forestry; at forest road
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                    [Name] => Wood chips
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                    [Name_BaseName] => Wood chips, beech; wet, manual harvest, hardwood forestry; at forest road
                    [GeneralComment] => ##General Information##
##Product name##
Wood chips, beech; wet, manual harvest, hardwood forestry; at forest road
##Reference flow##
1 kg DM
##System boundaries##
The dataset represents manual harvest of beech in France. The dataset covers stand establishment (production of seedlings in an unheated greenhouse and planting), tending and cleaning, thinning, manual final harvest and chipping. Construction and maintenance of infrastructure, like machinery and forest roads, are included. The system ends with wood chips at forest road. The activities over one rotation period are included.
The activity starts with site preparation assuming establishment of the forest via planting, including seedling production and covers all process related to forest management, including site preparation, planting, tending, young growth tending, clearing, thinning, and harvesting operations including the processing of wood fuel to chips, bundles and chopped wood (logs for energy) over one rotation period. It also covers the maintenance and construction of forest roads. This activity ends with the assortments at the forest road and includes eventual drying before transportation.
##Wastes and end-of-life##
##Biogenic carbon##
Biogenic carbon included as resource input from nature (uptake of carbon dioxide in air)
##Use advice of the dataset##
The dataset should be used to analyze the impact from beech wood chips that originate from thinning operations in France. Measured as dry mass
##Technological representativeness##
##Technology description##
For the chipping in the stand, a productivity of 25 m3 bulked/PMH is assumed (based on Cremer & Velazquesz). For wood chips chipped in the stand, a productivity of the forwarding of 70 Sm3 bulked/PMH with a diesel consumption of 9.5 l/h of the forwarder is assumed. 24.4% thinning (mechanical) and 76.6% (manual) final harvest
##Technology Quality level##Fair
##Geographical representativeness##
##Location## France (FR)
##Geographical representativeness description##
The dataset is based on dataset on harvest of hardwood in Germany from Wernet et al. (2016). The dataset has been adjusted to reflect beech forests, and beech forest management in France. The adjustments include changes in the harvesting methods to mirror the relative share of manual harvesting vs mechanized harvesters in the eastern parts on France, the allocation between clear-cut and thinning, and the area used for different practices (Pelletier, 2017).
##Geographical Quality level##
Fair
##Time related representativeness##
##Reference year##
2017
##Time representativeness description## Data represent 2017 for the specifications of French beech management while original ecoinvent data are largely based on a data source from 2009. Data are still considered representative due to small developments in forest management and harvest operations.
##Time Quality Level##
Fair
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Allocation based on mass.
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
All known inputs are included.
##Infrastructure/capital goods##
Construction and maintenance of roads and machinery are included
##Data treatment and extrapolations principles##
In communication with French Institute of Technology for Forest-based and Furniture Sectors (FCBA), it was recommended to use the ecoinvent (v3.6) dataset for beech from Germany as representative for beech from the eastern parts of France. The ecoinvent database provides well documented process data and is widely used in Europe. The data set “hardwood forestry, beech, sustainable forest management” was selected. This dataset covers the production and harvesting of 1 m3 of stemwood, beech, solid, under bark, plus the relative share of energy wood from slash from sustainable forest management as the prevailing management practices in Germany (Wernet et al., 2016). Changes were made to allocation principles, harvesting regimes, and area used to reflect French beech forestry (Pelletier, 2017).
##Data collection period##
2017 - 2021
##Administrative Information##
##Data set generator##
Andreas Brekke
##Access and use restrictions##
Open
##References##
Cremer, T. and B. Velazques-Marti (2007): Evaluation of two harvesting systems for the supply of woodchips in Norway spurce forest affected by bark beetle. Croation Journal of Forest Engineering, 28(2): 145-155.
Pelletier, C. (2017). Analyse environnementale et économique des filières bois-énergie. Université de Lorraine, Retrieved from https://tel.archives-ouvertes.fr/tel-01765854/document (2017LORR0331)
Wernet, G., Bauer, C., Steubing, B., Reinhard, J., Moreno-Ruiz, E., & Weidema, B. (2016). The ecoinvent database version 3 (part I): overview and methodology. The International Journal of Life Cycle Assessment, 21(9), 1218-1230. doi:10.1007/s11367-016-1087-8
##
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##Product name## Microfibrillated cellulose, Exilva Piano; without water; at plant; in 10% solution state
##Reference flow## 1 kg dry product
##System boundaries## Production of microfibrillated cellulose (MFC) of the Exilva Piano grade, in a 10% concentration at the Exilva industrial MFC plant in Sarpsborg, Norway. The functional unit is 1 kg dry product at factory gate, without packaging.
The modelling of the Exilva plant has been part of work package 7 (WP7) of the H2020 BBI JU Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). The modelling of the Borregaard biorefinery and the Exilva site have been documented in the following reports: Modahl and Soldal (2015) and Modahl, Brekke, Valente and Soldal (2016). The 2011 status of the specialty cellulose was published as a scientific paper by Modahl, Brekke and Valente (2015). Exilva MFC comes in four grades: Piano Light, Piano, Forte and Forte Plus, and all qualities are produced both as a 2% suspension and a 10% paste. 

Modahl, I.S. and Soldal, E. (2015): The 2015 LCA of products from the wood-based biorefinery at Borregaard, Sarpsborg. Results for cellulose, ethanols, lignosulfonates, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Ostfold Research, OR 11.15, April 2016.
Modahl, I.S., Brekke, A. and Valente, C. (2015): Environmental assessment of chemical products from a Norwegian biorefinery. Journal of Cleaner Production 94 (2015) 247-259.
Modahl, I.S., Brekke, A., Valente, C., and Soldal, E. (2016): E-LCA and S-LCA of the Exilva MFC process. AR 10.16 Ostfold Research.
Deliverable report D7.2 from work package 7 of the H2020 Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). Confidential. November 2016. ##Wastes and end-of-life## ##Biogenic carbon## ##Use advice of the dataset## MFC has many different application areas where different characteristics are emphasised. If the dataset is used for comparing Exilva MFC to other materials for the same purpose, special attention should be directed to product formulations and amounts. Necessary data to perform toxicity assessments has not been collected. Data are given for the active substance of the product. It is, however, sold and used with relative large amounts of water. Hence, make sure you calculate the correct amount of active substance. An example: you are transporting 1 kg of 2% Exilva. The amount of active substance is 20g. To calculate the correct transport volume, you will need to transport 20g of active substance and 980g water. ##Technological representativeness## ##Technology description## Production of the specialty cellulose, which is the main raw material for the MFC, takes place in an advanced biorefinery which also produces lignosulfonates, bioethanol, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Further processing of the specialty cellulose to MFC is made in a full-scale industrial plant built in 2016 (Exilva site). The main raw material of the biorefinery is Norway Spruce from Norway and Sweden. Specific data has been used for all activities at the Borregaard biorefinery and Exilva sites. Data for the biorefinery are from 2015 and data for the Exilva plant are from 2016 and 2018. The Ecoinvent database (version 3.4 allocation cut-off by classification) has been used for background processes. The biorefinery and the Exilva plant have been modelled on a detailed level, avoiding allocation to a large extent. Energy allocation has been used when necessary. An attributional approach has been used. To be in line with the EPDs made for the biorefinery's products (including the specialty cellulose), the following system boundaries have been applied: Norwegian electricity production mix has been used for electricity used in Norway, emissions from combustion of waste for heat production at the biorefinery are not allocated the user of the heat, rather the producer of the waste, and emissions from combustion of waste oil and biogas have been allocated the user. Infrastructure is included (in contrast to the EPDs, where infrastructure in the form of production equipment with an expected lifetime over three years, buildings and capital goods are not included except for energy carriers). The LCA software SimaPro (version 8.2.3) was used to model the system. ##Technology Quality level## Very good ##Geographical representativeness## ##Location## Norway ##Geographical representativeness description## Data for the biorefinery is collected from, and specific for, the given biorefinery in Sarpsborg, Norway. ##Geographical Quality level## Very good ##Time related representativeness## ##Reference year## 2016 ##Time representativeness description## Data for the Exilva plant is from 2016, and for the biorefinery data for the major flows are from 2015 (specialty cellulose as raw material). Data used are based on annual numbers. For the biorefinery these numbers could change somewhat from year to year but the burdens per kg of specialty cellulose would not vary much. Changes in the energy carrier mix of the biorefinery could, however, alter resource use, emissions and waste generation. For the Exilva plant (microfibrillation of specialty cellulose), flows per kg of product will be quite constant. ##Time Quality Level## Very good ##Methodological appropriateness and consistency## ##LCI method principle## Attributional ##LCI allocation methods## ##Other comments on methods approaches## The biorefinery (producing specialty cellulose as a raw material for the microfibrillated cellulose) and the Exilva plant have been modelled on a detailed level, largely avoiding the need for allocation. Energy allocation has been used when necessary. For steam and hot water, the enthalpy has been used. Infrastructure is included. Emissions from combustion of waste for heat production at the biorefinery are not allocated to the user of the heat, rather to the producer of the waste. 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Energy consumption is dependent on the residence time. Utiliztion of max capacity assumed. Residence time is changed in the parameters. 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The functional unit is 1 kg dry product at factory gate, without packaging. The modelling of the Exilva plant has been part of work package 7 (WP7) of the H2020 BBI JU Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). The modelling of the Borregaard biorefinery and the Exilva site have been documented in the following reports: Modahl and Soldal (2015) and Modahl, Brekke, Valente and Soldal (2016). The 2011 status of the specialty cellulose was published as a scientific paper by Modahl, Brekke and Valente (2015). Exilva MFC comes in four grades: Piano Light, Piano, Forte and Forte Plus, and all qualities are produced both as a 2% suspension and a 10% paste.

Modahl, I.S. and Soldal, E. (2015): The 2015 LCA of products from the wood-based biorefinery at Borregaard, Sarpsborg. Results for cellulose, ethanols, lignosulfonates, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Ostfold Research, OR 11.15, April 2016.
Modahl, I.S., Brekke, A. and Valente, C. (2015): Environmental assessment of chemical products from a Norwegian biorefinery. Journal of Cleaner Production 94 (2015) 247-259.
Modahl, I.S., Brekke, A., Valente, C., and Soldal, E. (2016): E-LCA and S-LCA of the Exilva MFC process. AR 10.16 Ostfold Research.
Deliverable report D7.2 from work package 7 of the H2020 Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). Confidential. November 2016. ##Wastes and end-of-life## ##Biogenic carbon## ##Use advice of the dataset## MFC has many different application areas where different characteristics are emphasised. If the dataset is used for comparing Exilva MFC to other materials for the same purpose, special attention should be directed to product formulations and amounts. Necessary data to perform toxicity assessments has not been collected. Data are given for the active substance of the product. It is, however, sold and used with relative large amounts of water. Hence, make sure you calculate the correct amount of active substance. An example: you are transporting 1 kg of 2% Exilva. The amount of active substance is 20g. To calculate the correct transport volume, you will need to transport 20g of active substance and 980g water. ##Technological representativeness## ##Technology description## Production of the specialty cellulose, which is the main raw material for the MFC, takes place in an advanced biorefinery which also produces lignosulfonates, bioethanol, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Further processing of the specialty cellulose to MFC is made in a full-scale industrial plant built in 2016 (Exilva site). The main raw material of the biorefinery is Norway Spruce from Norway and Sweden. Specific data has been used for all activities at the Borregaard biorefinery and Exilva sites. Data for the biorefinery are from 2015 and data for the Exilva plant are from 2016 and 2018. The Ecoinvent database (version 3.4 allocation cut-off by classification) has been used for background processes. The biorefinery and the Exilva plant have been modelled on a detailed level, avoiding allocation to a large extent. Energy allocation has been used when necessary. An attributional approach has been used. To be in line with the EPDs made for the biorefinery's products (including the specialty cellulose), the following system boundaries have been applied: Norwegian electricity production mix has been used for electricity used in Norway, emissions from combustion of waste for heat production at the biorefinery are not allocated the user of the heat, rather the producer of the waste, and emissions from combustion of waste oil and biogas have been allocated the user. Infrastructure is included (in contrast to the EPDs, where infrastructure in the form of production equipment with an expected lifetime over three years, buildings and capital goods are not included except for energy carriers). The LCA software SimaPro (version 8.2.3) was used to model the system. ##Technology Quality level## Very good ##Geographical representativeness## ##Location## Norway ##Geographical representativeness description## Data for the biorefinery is collected from, and specific for, the given biorefinery in Sarpsborg, Norway. ##Geographical Quality level## Very good ##Time related representativeness## ##Reference year## 2016 ##Time representativeness description## Data for the Exilva plant is from 2016, and for the biorefinery data for the major flows are from 2015 (specialty cellulose as raw material). Data used are based on annual numbers. For the biorefinery these numbers could change somewhat from year to year but the burdens per kg of specialty cellulose would not vary much. Changes in the energy carrier mix of the biorefinery could, however, alter resource use, emissions and waste generation. For the Exilva plant (microfibrillation of specialty cellulose), flows per kg of product will be quite constant. ##Time Quality Level## Very good ##Methodological appropriateness and consistency## ##LCI method principle## Attributional ##LCI allocation methods## ##Other comments on methods approaches## The biorefinery (producing specialty cellulose as a raw material for the microfibrillated cellulose) and the Exilva plant have been modelled on a detailed level, largely avoiding the need for allocation. Energy allocation has been used when necessary. For steam and hot water, the enthalpy has been used. Infrastructure is included. Emissions from combustion of waste for heat production at the biorefinery are not allocated to the user of the heat, rather to the producer of the waste. Emissions from combustion of waste oil and biogas have been allocated to the user. ##Completeness## ##Cut off for mass or energy flows## No cut off in the foreground system (Exilva plant) and the biorefinery producing specialty cellulose as a raw material to the Exilva plant. ##Infrastructure/capital goods## ##Data treatment and extrapolations principles## ##Data collection period## 2008 - 2018 ##Administrative Information## ##Data set generator## Ingunn Saur Modahl ##Access and use restrictions## Free ## [Time_ReferenceYear] => 2021 [Time_DataSetValidUntil] => 2026 [Time_RepresentativenessDescription] => 5 years default [Geography_OperationSupplyOrProductionDescriptionOfRestrictions] => Restricted to specified geography [Geography_LocationOfOperationSupplyOrProduction] => NO ) [ModelingAndValidation] => Array ( [LCIMethodAndAllocation_TypeOfDataSet] => Unit_process_single_operation [LCIMethodAndAllocation_LCIMethodPrinciple] => Attributional [LCIMethodAndAllocation_LCIMethodApproaches] => Array ( [Not_applicable] => Not applicable ) [DataSourcesTreatmentAndRepresentativeness_ReferencesToDataSource] => Array ( [0] => Array ( [Id] => 9ba3ac1e-6797-4cc0-afd5-1b8f7bf28c6a [LibraryId] => 9ba3ac1e-6797-4cc0-afd5-1b8f7bf28c6a [Name] => ILCD Data Network - compliance (non-Process) ) ) [DataSourcesTreatmentAndRepresentativeness_PercentageSupplyOrProductionCovered] => 100 [Completeness_CompletenessProductModel] => No_statement [Completeness_ElementaryFlows] => Array ( ) [ComplianceDeclarations_Compliance] => Array ( [0] => Array ( [QualityCompliance] => Not_defined [NomenclatureCompliance] => Not_defined [MethodologicalCompliance] => Not_defined [ReviewCompliance] => Not_defined [DocumentationCompliance] => Not_defined [ReferenceToComplianceSystem] => Array ( [Id] => 9ba3ac1e-6797-4cc0-afd5-1b8f7bf28c6a [LibraryId] => 9ba3ac1e-6797-4cc0-afd5-1b8f7bf28c6a [Name] => ILCD Data Network - Entry-level ) [ApprovalOfOverallCompliance] => Not_defined ) ) ) [AdministrativeInformation] => Array ( [CommissionerAndGoal_ReferenceToCommissioner] => Array ( [0] => Array ( [Id] => f85f5371-0565-455c-a114-155dacbcb44d [LibraryId] => 7f556cf2-6175-4787-a115-9cd4bab0eb74 [Name] => Pieter Callewaert [Email] => pieter@norsus.no ) ) [DataGenerator_ReferenceToPersonOrEntityGeneratingTheDataSet] => Array ( [0] => Array ( [Id] => f85f5371-0565-455c-a114-155dacbcb44d [LibraryId] => 7f556cf2-6175-4787-a115-9cd4bab0eb74 [Name] => Pieter Callewaert [Email] => pieter@norsus.no ) ) [DataEntryBy_ReferenceToPersonOrEntityEnteringTheData] => Array ( [Id] => f85f5371-0565-455c-a114-155dacbcb44d [LibraryId] => 7f556cf2-6175-4787-a115-9cd4bab0eb74 [Name] => Pieter Callewaert [Email] => pieter@norsus.no ) [DataEntryBy_ReferenceToDataSetUseApproval] => Array ( [0] => Array ( [Id] => be34bbb0-b054-11db-abbd-0800200c9a66 [LibraryId] => be34bbb0-b054-11db-abbd-0800200c9a66 [Name] => No official approval by producer or operator ) ) [PublicationAndOwnership_DateOfLastRevision] => 2021-10-01T09:31:34.7167212 [PublicationAndOwnership_CopyRight] => 1 [PublicationAndOwnership_ReferenceToEntitiesWithExclusiveAccess] => Array ( [0] => Array ( [Id] => 8b3e08f0-740a-43b1-aa8d-bec56c5daf6e [LibraryId] => 8b3e08f0-740a-43b1-aa8d-bec56c5daf6e [Name] => FINAL USER ) ) [PublicationAndOwnership_LicenceType] => Other [DataEntryBy_TimeStamp] => 2021-10-01T09:31:34.7167212 [DataEntryBy_ReferenceToDataSetFormat] => Array ( [0] => Array ( [Id] => d92a1a12-2545-49e2-a585-55c259997756 [LibraryId] => d92a1a12-2545-49e2-a585-55c259997756 [Name] => ILCD Data Network - Entry-level ) ) [PublicationAndOwnership_ReferenceToOwnershipOfDataSet] => Array ( [Id] => f85f5371-0565-455c-a114-155dacbcb44d [LibraryId] => 7f556cf2-6175-4787-a115-9cd4bab0eb74 [Name] => Pieter Callewaert [Email] => pieter@norsus.no ) ) [Id] => 6bf776c4-c727-43b9-ab36-754773bd2e91 [CreatedBy] => f85f5371-0565-455c-a114-155dacbcb44d [ModifiedBy] => f85f5371-0565-455c-a114-155dacbcb44d [IsEditable] => 1 ) [4] => Array ( [ProjectId] => 42857b44-1699-43c3-9de7-92a8c9e144f6 [Name] => Microfibrillated cellulose, Exilva Forte; without water; at plant; in 2% solution state [GeographyId] => 10f1ec30-7d7e-11de-9ae2-0019e336be3a [Geography] => Array ( [Id] => 10f1ec30-7d7e-11de-9ae2-0019e336be3a [LibraryId] => 10f1ec30-7d7e-11de-9ae2-0019e336be3a [Name] => NO ) [ReferenceProductId] => 32c1870b-0080-4f3f-ac05-7afed89fbeb6 [ReferenceProduct] => Array ( [Id] => 32c1870b-0080-4f3f-ac05-7afed89fbeb6 [LibraryId] => 32c1870b-0080-4f3f-ac05-7afed89fbeb6 [Name] => Exilva Forte, without water, in 2% solution state; at plant; System ) [CreatedOn] => 2021-02-15T14:23:58.448232 [ModifiedOn] => 2021-04-22T11:20:25.3948522 [ProcessInformation] => Array ( [UUID] => 26e69496-2017-4e5f-bf6c-6e1dfb4d79d1 [Name_BaseName] => Microfibrillated cellulose, Exilva Forte; without water; at plant; in 2% solution state [GeneralComment] => ##General Information## ##Product name## Microfibrillated cellulose, Exilva Forte; without water; at plant; in 2% solution state ##Reference flow## 1 kg dry product ##System boundaries## Production of microfibrillated cellulose (MFC) of the Exilva Forte grade, in a 2% concentration at the Exilva industrial MFC plant in Sarpsborg, Norway. The functional unit is 1 kg dry product at factory gate, without packaging. The modelling of the Exilva plant has been part of work package 7 (WP7) of the H2020 BBI JU Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). The modelling of the Borregaard biorefinery and the Exilva site have been documented in the following reports: Modahl and Soldal (2015) and Modahl, Brekke, Valente and Soldal (2016). The 2011 status of the specialty cellulose was published as a scientific paper by Modahl, Brekke and Valente (2015). Exilva MFC comes in four grades: Piano Light, Piano, Forte and Forte Plus, and all qualities are produced both as a 2% suspension and a 10% paste.

Modahl, I.S. and Soldal, E. (2015): The 2015 LCA of products from the wood-based biorefinery at Borregaard, Sarpsborg. Results for cellulose, ethanols, lignosulfonates, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Ostfold Research, OR 11.15, April 2016.
Modahl, I.S., Brekke, A. and Valente, C. (2015): Environmental assessment of chemical products from a Norwegian biorefinery. Journal of Cleaner Production 94 (2015) 247-259.
Modahl, I.S., Brekke, A., Valente, C., and Soldal, E. (2016): E-LCA and S-LCA of the Exilva MFC process. AR 10.16 Ostfold Research.
Deliverable report D7.2 from work package 7 of the H2020 Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). Confidential. November 2016. ##Wastes and end-of-life## ##Biogenic carbon## ##Use advice of the dataset## MFC has many different application areas where different characteristics are emphasised. If the dataset is used for comparing Exilva MFC to other materials for the same purpose, special attention should be directed to product formulations and amounts. Necessary data to perform toxicity assessments has not been collected. Data are given for the active substance of the product. It is, however, sold and used with relative large amounts of water. Hence, make sure you calculate the correct amount of active substance. An example: you are transporting 1 kg of 2% Exilva. The amount of active substance is 20g. To calculate the correct transport volume, you will need to transport 20g of active substance and 980g water. ##Technological representativeness## ##Technology description## Production of the specialty cellulose, which is the main raw material for the MFC, takes place in an advanced biorefinery which also produces lignosulfonates, bioethanol, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Further processing of the specialty cellulose to MFC is made in a full-scale industrial plant built in 2016 (Exilva site). The main raw material of the biorefinery is Norway Spruce from Norway and Sweden. Specific data has been used for all activities at the Borregaard biorefinery and Exilva sites. Data for the biorefinery are from 2015 and data for the Exilva plant are from 2016 and 2018. The Ecoinvent database (version 3.4 allocation cut-off by classification) has been used for background processes. The biorefinery and the Exilva plant have been modelled on a detailed level, avoiding allocation to a large extent. Energy allocation has been used when necessary. An attributional approach has been used. To be in line with the EPDs made for the biorefinery's products (including the specialty cellulose), the following system boundaries have been applied: Norwegian electricity production mix has been used for electricity used in Norway, emissions from combustion of waste for heat production at the biorefinery are not allocated the user of the heat, rather the producer of the waste, and emissions from combustion of waste oil and biogas have been allocated the user. Infrastructure is included (in contrast to the EPDs, where infrastructure in the form of production equipment with an expected lifetime over three years, buildings and capital goods are not included except for energy carriers). The LCA software SimaPro (version 8.2.3) was used to model the system. ##Technology Quality level## Very good ##Geographical representativeness## ##Location## Norway ##Geographical representativeness description## Data for the biorefinery is collected from, and specific for, the given biorefinery in Sarpsborg, Norway. ##Geographical Quality level## Very good ##Time related representativeness## ##Reference year## 2016 ##Time representativeness description## Data for the Exilva plant is from 2016, and for the biorefinery data for the major flows are from 2015 (specialty cellulose as raw material). Data used are based on annual numbers. For the biorefinery these numbers could change somewhat from year to year but the burdens per kg of specialty cellulose would not vary much. Changes in the energy carrier mix of the biorefinery could, however, alter resource use, emissions and waste generation. For the Exilva plant (microfibrillation of specialty cellulose), flows per kg of product will be quite constant. ##Time Quality Level## Very good ##Methodological appropriateness and consistency## ##LCI method principle## Attributional ##LCI allocation methods## ##Other comments on methods approaches## The biorefinery (producing specialty cellulose as a raw material for the microfibrillated cellulose) and the Exilva plant have been modelled on a detailed level, largely avoiding the need for allocation. Energy allocation has been used when necessary. For steam and hot water, the enthalpy has been used. Infrastructure is included. Emissions from combustion of waste for heat production at the biorefinery are not allocated to the user of the heat, rather to the producer of the waste. Emissions from combustion of waste oil and biogas have been allocated to the user. ##Completeness## ##Cut off for mass or energy flows## No cut off in the foreground system (Exilva plant) and the biorefinery producing specialty cellulose as a raw material to the Exilva plant. ##Infrastructure/capital goods## ##Data treatment and extrapolations principles## ##Data collection period## 2008 - 2018 ##Administrative Information## ##Data set generator## Ingunn Saur Modahl ##Access and use restrictions## Free ## [Time_ReferenceYear] => 2021 [Time_DataSetValidUntil] => 2026 [Time_RepresentativenessDescription] => 5 years default [Geography_OperationSupplyOrProductionDescriptionOfRestrictions] => Restricted to specified geography [Geography_LocationOfOperationSupplyOrProduction] => NO ) [ModelingAndValidation] => Array ( [LCIMethodAndAllocation_TypeOfDataSet] => Unit_process_single_operation [LCIMethodAndAllocation_LCIMethodPrinciple] => Attributional [LCIMethodAndAllocation_LCIMethodApproaches] => Array ( [Not_applicable] => Not applicable ) [DataSourcesTreatmentAndRepresentativeness_ReferencesToDataSource] => Array ( [0] => Array ( [Id] => 9ba3ac1e-6797-4cc0-afd5-1b8f7bf28c6a [LibraryId] => 9ba3ac1e-6797-4cc0-afd5-1b8f7bf28c6a [Name] => ILCD Data Network - compliance (non-Process) ) ) [DataSourcesTreatmentAndRepresentativeness_PercentageSupplyOrProductionCovered] => 100 [Completeness_CompletenessProductModel] => No_statement [Completeness_ElementaryFlows] => Array ( ) [ComplianceDeclarations_Compliance] => Array ( [0] => Array ( [QualityCompliance] => Not_defined [NomenclatureCompliance] => Not_defined [MethodologicalCompliance] => Not_defined [ReviewCompliance] => Not_defined [DocumentationCompliance] => Not_defined [ReferenceToComplianceSystem] => Array ( [Id] => 9ba3ac1e-6797-4cc0-afd5-1b8f7bf28c6a [LibraryId] => 9ba3ac1e-6797-4cc0-afd5-1b8f7bf28c6a [Name] => ILCD Data Network - Entry-level ) [ApprovalOfOverallCompliance] => Not_defined ) ) ) [AdministrativeInformation] => Array ( [CommissionerAndGoal_ReferenceToCommissioner] => Array ( [0] => Array ( [Id] => f85f5371-0565-455c-a114-155dacbcb44d [LibraryId] => 7f556cf2-6175-4787-a115-9cd4bab0eb74 [Name] => Pieter Callewaert [Email] => pieter@norsus.no ) ) [DataGenerator_ReferenceToPersonOrEntityGeneratingTheDataSet] => Array ( [0] => Array ( [Id] => f85f5371-0565-455c-a114-155dacbcb44d [LibraryId] => 7f556cf2-6175-4787-a115-9cd4bab0eb74 [Name] => Pieter Callewaert [Email] => pieter@norsus.no ) ) [DataEntryBy_ReferenceToPersonOrEntityEnteringTheData] => Array ( [Id] => f85f5371-0565-455c-a114-155dacbcb44d [LibraryId] => 7f556cf2-6175-4787-a115-9cd4bab0eb74 [Name] => Pieter Callewaert [Email] => pieter@norsus.no ) [DataEntryBy_ReferenceToDataSetUseApproval] => Array ( [0] => Array ( [Id] => be34bbb0-b054-11db-abbd-0800200c9a66 [LibraryId] => be34bbb0-b054-11db-abbd-0800200c9a66 [Name] => No official approval by producer or operator ) ) [PublicationAndOwnership_DateOfLastRevision] => 2021-04-22T11:20:25.3948522 [PublicationAndOwnership_CopyRight] => 1 [PublicationAndOwnership_ReferenceToEntitiesWithExclusiveAccess] => Array ( [0] => Array ( [Id] => 8b3e08f0-740a-43b1-aa8d-bec56c5daf6e [LibraryId] => 8b3e08f0-740a-43b1-aa8d-bec56c5daf6e [Name] => FINAL USER ) ) [PublicationAndOwnership_LicenceType] => Other [DataEntryBy_TimeStamp] => 2021-04-22T11:20:25.3948522 [DataEntryBy_ReferenceToDataSetFormat] => Array ( [0] => Array ( [Id] => d92a1a12-2545-49e2-a585-55c259997756 [LibraryId] => d92a1a12-2545-49e2-a585-55c259997756 [Name] => ILCD Data Network - Entry-level ) ) [PublicationAndOwnership_ReferenceToOwnershipOfDataSet] => Array ( [Id] => f85f5371-0565-455c-a114-155dacbcb44d [LibraryId] => 7f556cf2-6175-4787-a115-9cd4bab0eb74 [Name] => Pieter Callewaert [Email] => pieter@norsus.no ) ) [Id] => c34f2397-e13d-47da-a834-95690094c251 [CreatedBy] => f85f5371-0565-455c-a114-155dacbcb44d [ModifiedBy] => f85f5371-0565-455c-a114-155dacbcb44d [IsEditable] => 1 ) [5] => Array ( [ProjectId] => 42857b44-1699-43c3-9de7-92a8c9e144f6 [Name] => Microfibrillated cellulose, Exilva Piano; without water; at plant; in 2% solution state [GeographyId] => 10f1ec30-7d7e-11de-9ae2-0019e336be3a [Geography] => Array ( [Id] => 10f1ec30-7d7e-11de-9ae2-0019e336be3a [LibraryId] => 10f1ec30-7d7e-11de-9ae2-0019e336be3a [Name] => NO ) [ReferenceProductId] => 9573dca6-0d8b-4558-9c30-f719728bf40f [ReferenceProduct] => Array ( [Id] => 9573dca6-0d8b-4558-9c30-f719728bf40f [LibraryId] => 9573dca6-0d8b-4558-9c30-f719728bf40f [Name] => Exilva Piano, without water, in 2% solution state; at plant; System ) [CreatedOn] => 2021-02-15T14:23:58.448232 [ModifiedOn] => 2021-10-01T09:31:40.1906292 [ProcessInformation] => Array ( [UUID] => 7d72f807-f59a-4906-a953-c82c09c2ac84 [Name_BaseName] => Microfibrillated cellulose, Exilva Piano; without water; at plant; in 2% solution state [GeneralComment] => ##General Information## ##Product name## Microfibrillated cellulose, Exilva Piano; without water; at plant; in 2% solution state ##Reference flow## 1kg dry product ##System boundaries## Production of microfibrillated cellulose (MFC) of the Exilva Piano grade, in a 2% concentration at the Exilva industrial MFC plant in Sarpsborg, Norway. The functional unit is 1 kg dry product at factory gate, without packaging. The modelling of the Exilva plant has been part of work package 7 (WP7) of the H2020 BBI JU Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). The modelling of the Borregaard biorefinery and the Exilva site have been documented in the following reports: Modahl and Soldal (2015) and Modahl, Brekke, Valente and Soldal (2016). The 2011 status of the specialty cellulose was published as a scientific paper by Modahl, Brekke and Valente (2015). Exilva MFC comes in four grades: Piano Light, Piano, Forte and Forte Plus, and all qualities are produced both as a 2% suspension and a 10% paste.

Modahl, I.S. and Soldal, E. (2015): The 2015 LCA of products from the wood-based biorefinery at Borregaard, Sarpsborg. Results for cellulose, ethanols, lignosulfonates, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Ostfold Research, OR 11.15, April 2016.
Modahl, I.S., Brekke, A. and Valente, C. (2015): Environmental assessment of chemical products from a Norwegian biorefinery. Journal of Cleaner Production 94 (2015) 247-259.
Modahl, I.S., Brekke, A., Valente, C., and Soldal, E. (2016): E-LCA and S-LCA of the Exilva MFC process. AR 10.16 Ostfold Research.
Deliverable report D7.2 from work package 7 of the H2020 Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). Confidential. November 2016. ##Wastes and end-of-life## ##Biogenic carbon## Test ##Use advice of the dataset## MFC has many different application areas where different characteristics are emphasised. If the dataset is used for comparing Exilva MFC to other materials for the same purpose, special attention should be directed to product formulations and amounts. Necessary data to perform toxicity assessments has not been collected. Data are given for the active substance of the product. It is, however, sold and used with relative large amounts of water. Hence, make sure you calculate the correct amount of active substance. An example: you are transporting 1 kg of 2% Exilva. The amount of active substance is 20g. To calculate the correct transport volume, you will need to transport 20g of active substance and 980g water. ##Technological representativeness## ##Technology description## Production of the specialty cellulose, which is the main raw material for the MFC, takes place in an advanced biorefinery which also produces lignosulfonates, bioethanol, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Further processing of the specialty cellulose to MFC is made in a full-scale industrial plant built in 2016 (Exilva site). The main raw material of the biorefinery is Norway Spruce from Norway and Sweden. Specific data has been used for all activities at the Borregaard biorefinery and Exilva sites. Data for the biorefinery are from 2015 and data for the Exilva plant are from 2016 and 2018. The Ecoinvent database (version 3.4 allocation cut-off by classification) has been used for background processes. The biorefinery and the Exilva plant have been modelled on a detailed level, avoiding allocation to a large extent. Energy allocation has been used when necessary. An attributional approach has been used. To be in line with the EPDs made for the biorefinery's products (including the specialty cellulose), the following system boundaries have been applied: Norwegian electricity production mix has been used for electricity used in Norway, emissions from combustion of waste for heat production at the biorefinery are not allocated the user of the heat, rather the producer of the waste, and emissions from combustion of waste oil and biogas have been allocated the user. Infrastructure is included (in contrast to the EPDs, where infrastructure in the form of production equipment with an expected lifetime over three years, buildings and capital goods are not included except for energy carriers). The LCA software SimaPro (version 8.2.3) was used to model the system. ##Technology Quality level## Very good ##Geographical representativeness## ##Location## Norway ##Geographical representativeness description## Data for the biorefinery is collected from, and specific for, the given biorefinery in Sarpsborg, Norway ##Geographical Quality level## Very good ##Time related representativeness## ##Reference year## 2016 ##Time representativeness description## Data for the Exilva plant is from 2016, and for the biorefinery data for the major flows are from 2015 (specialty cellulose as raw material). Data used are based on annual numbers. For the biorefinery these numbers could change somewhat from year to year but the burdens per kg of specialty cellulose would not vary much. Changes in the energy carrier mix of the biorefinery could, however, alter resource use, emissions and waste generation. For the Exilva plant (microfibrillation of specialty cellulose), flows per kg of product will be quite constant. ##Time Quality Level## Very good ##Methodological appropriateness and consistency## ##LCI method principle## Attributional ##LCI allocation methods## ##Other comments on methods approaches## The biorefinery (producing specialty cellulose as a raw material for the microfibrillated cellulose) and the Exilva plant have been modelled on a detailed level, largely avoiding the need for allocation. Energy allocation has been used when necessary. For steam and hot water, the enthalpy has been used. Infrastructure is included. Emissions from combustion of waste for heat production at the biorefinery are not allocated to the user of the heat, rather to the producer of the waste. Emissions from combustion of waste oil and biogas have been allocated to the user. ##Completeness## ##Cut off for mass or energy flows## No cut off in the foreground system (Exilva plant) and the biorefinery producing specialty cellulose as a raw material to the Exilva plant. ##Infrastructure/capital goods## ##Data treatment and extrapolations principles## ##Data collection period## 2008 – 2018 ##Administrative Information## ##Data set generator## Ingunn Saur Modahl ##Access and use restrictions## Free ## [Time_ReferenceYear] => 2021 [Time_DataSetValidUntil] => 2026 [Time_RepresentativenessDescription] => 5 years default [Geography_OperationSupplyOrProductionDescriptionOfRestrictions] => Restricted to specified geography [Geography_LocationOfOperationSupplyOrProduction] => NO ) [ModelingAndValidation] => Array ( [LCIMethodAndAllocation_TypeOfDataSet] => Unit_process_single_operation [LCIMethodAndAllocation_LCIMethodPrinciple] => Attributional [LCIMethodAndAllocation_LCIMethodApproaches] => Array ( [Not_applicable] => Not applicable ) [DataSourcesTreatmentAndRepresentativeness_ReferencesToDataSource] => Array ( [0] => Array ( [Id] => 9ba3ac1e-6797-4cc0-afd5-1b8f7bf28c6a [LibraryId] => 9ba3ac1e-6797-4cc0-afd5-1b8f7bf28c6a [Name] => ILCD Data Network - compliance (non-Process) ) ) [DataSourcesTreatmentAndRepresentativeness_PercentageSupplyOrProductionCovered] => 100 [Completeness_CompletenessProductModel] => No_statement [Completeness_ElementaryFlows] => Array ( ) [ComplianceDeclarations_Compliance] => Array ( [0] => Array ( [QualityCompliance] => Not_defined [NomenclatureCompliance] => Not_defined [MethodologicalCompliance] => Not_defined [ReviewCompliance] => Not_defined [DocumentationCompliance] => Not_defined [ReferenceToComplianceSystem] => Array ( [Id] => 9ba3ac1e-6797-4cc0-afd5-1b8f7bf28c6a [LibraryId] => 9ba3ac1e-6797-4cc0-afd5-1b8f7bf28c6a [Name] => ILCD Data Network - Entry-level ) [ApprovalOfOverallCompliance] => Not_defined ) ) ) [AdministrativeInformation] => Array ( [CommissionerAndGoal_ReferenceToCommissioner] => Array ( [0] => Array ( [Id] => f85f5371-0565-455c-a114-155dacbcb44d [LibraryId] => 7f556cf2-6175-4787-a115-9cd4bab0eb74 [Name] => Pieter Callewaert [Email] => pieter@norsus.no ) ) [DataGenerator_ReferenceToPersonOrEntityGeneratingTheDataSet] => Array ( [0] => Array ( [Id] => f85f5371-0565-455c-a114-155dacbcb44d [LibraryId] => 7f556cf2-6175-4787-a115-9cd4bab0eb74 [Name] => Pieter Callewaert [Email] => pieter@norsus.no ) ) [DataEntryBy_ReferenceToPersonOrEntityEnteringTheData] => Array ( [Id] => f85f5371-0565-455c-a114-155dacbcb44d [LibraryId] => 7f556cf2-6175-4787-a115-9cd4bab0eb74 [Name] => Pieter Callewaert [Email] => pieter@norsus.no ) [DataEntryBy_ReferenceToDataSetUseApproval] => Array ( [0] => Array ( [Id] => be34bbb0-b054-11db-abbd-0800200c9a66 [LibraryId] => be34bbb0-b054-11db-abbd-0800200c9a66 [Name] => No official approval by producer or operator ) ) [PublicationAndOwnership_DateOfLastRevision] => 2021-10-01T09:31:40.1906292 [PublicationAndOwnership_CopyRight] => 1 [PublicationAndOwnership_ReferenceToEntitiesWithExclusiveAccess] => Array ( [0] => Array ( [Id] => 8b3e08f0-740a-43b1-aa8d-bec56c5daf6e [LibraryId] => 8b3e08f0-740a-43b1-aa8d-bec56c5daf6e [Name] => FINAL USER ) ) [PublicationAndOwnership_LicenceType] => Other [DataEntryBy_TimeStamp] => 2021-10-01T09:31:40.1906292 [DataEntryBy_ReferenceToDataSetFormat] => Array ( [0] => Array ( [Id] => d92a1a12-2545-49e2-a585-55c259997756 [LibraryId] => d92a1a12-2545-49e2-a585-55c259997756 [Name] => ILCD Data Network - Entry-level ) ) [PublicationAndOwnership_ReferenceToOwnershipOfDataSet] => Array ( [Id] => f85f5371-0565-455c-a114-155dacbcb44d [LibraryId] => 7f556cf2-6175-4787-a115-9cd4bab0eb74 [Name] => Pieter Callewaert [Email] => pieter@norsus.no ) ) [Id] => b0cfcd10-204f-4591-9ff4-cef41ad6722f [CreatedBy] => f85f5371-0565-455c-a114-155dacbcb44d [ModifiedBy] => f85f5371-0565-455c-a114-155dacbcb44d [IsEditable] => 1 ) )

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Data Treteknisk

Alle tags

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process:

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                    [DataSourcesTreatmentAndRepresentativeness_PercentageSupplyOrProductionCovered] => 100
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Unit ID's

Array
(
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NORSUS (3.7)

Alle tags

Array
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    [adb0ee87-3235-42d3-9280-0e2afae4ce36] => Sector_Materials production / Metals and semimetals, Access_open
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    [ea1b83ad-9dcd-4b71-a41c-29119130307e] => Sector_Energy carriers and technologies / Heat and steam, Access_open
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    [82da2fc8-d940-45a8-93ea-82f623afbf32] => Sector_Energy carriers and technologies / Renewable fuels, Access_open
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    [07b92658-2b89-4de4-af5e-a28563d4246f] => Sector_Materials production / Wood, Access_open
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    [210761c5-c959-4f75-888b-fcc25e7fab99] => Sector_End-of-life treatment / Waste collection, Access_open
    [0aeed9ff-038c-4564-a9e7-ff3a95c272d8] => Sector_Energy carriers and technologies / Electricity, Access_open
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process:

Alle prosesser

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            [Name] => Low alloyed steel, casted, Primary and secondary production, at plant, 50.1% primary material, 49.9% recycled material
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                    [Name_BaseName] => Low alloyed steel, casted, Primary and secondary production, at plant, 50.1% primary material, 49.9% recycled material
                    [GeneralComment] => ##General Information##
##Product name##
Low alloyed steel, casted, Primary and secondary production, at plant, 50.1% primary material, 49.9% recycled material
##Reference flow##
1 kg
##System boundaries##
Cradle-to-gate. Starts with the production of raw material (pig iron) and ends with steel making process and casting with oxygen blast furnace and electric converter technologies.
##Wastes and end-of-life##
Processing waste is included, EoL waste of the steel not accounted for.
##Biogenic carbon##
Not relevant
##Use advice of the dataset##
This dataset should be used to model the use of steel produced in Poland and uses ecoinvent 3.7.1 processes in the background. More precisely,
• For both steel processing technologies (oxygen blast furnace and electric converter) and the raw material (pig iron) production, the ecoinvent processes have been adjusted to Polish heat and electricity inputs.
• The share of recycled and virgin materials is adjusted based on the use of technologies in the country (World steel association, 2019).
The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
The share of recycled and virgin materials is based on the use of production technologies in Poland (oxygen blast furnace used for recycled material and electric converter used for virgin material) based on statistics from World Steel Association (2021). For further description of the technologies, see the description of the ecoinvent datasets.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Poland
##Geographical representativeness description##
Global and RoW ecoinvent processes for steel production technologies have been adjusted to represent Polish production.
##Geographical Quality level##
Fair
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
Elements modified from the ecoinvent datasets are based on yearly World Steel published data, representative for 2020.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure included
##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21##
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            [CreatedOn] => 2021-09-22T08:38:50.3739825
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                    [GeneralComment] => ##General Information##
##Product name##
Heat, for district heating, Norwegian production mix, at plant
##Reference flow##
1 kWh
##System boundaries##
Production mix of district heat in Norway in 2020, based on information on energy carriers from Norsk Fjernvarme. The activity starts with acquisition of energy carriers, i.e., waste reception at gate, acquisition and transport of fossil oil and gas, harvesting and transport of woody biomass, and production, transformation from high to low voltage and transmission of electricity. The activity stops at district heat production facility (i.e. distribution is not included).
##Wastes and end-of-life##
##Biogenic carbon##
##Use advice of the dataset##
This dataset represents the district heat production mix in Norway in the year 2020. Data on heat production and share of different energy carriers are collected from www.fjernkontrollen.no. ecoinvent 3.7 allocation, cut-off by classification is used for the background data.
##Technological representativeness##
##Technology description##
Average technology for production and transmission of electricity in Norway. Heat from the other sources based on average European technologies. One technology is selected to represent each category of energy carriers. For fuel oil, light fuel oil is selected. For ambient heat, heat pump is selected. For bioenergy, wood logs combustion is used as representative technology.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Total heat production and share of sources are representative for Norway. Background data are representative for European technology.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
Annual production.
##Time Quality Level##
Very good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
Allocation cut-off by classification in background data.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure is included
##Data treatment and extrapolations principles##
The energy carriers are given by Norsk Fjernvarme. One technology is selected to represent each category of energy carriers. For fuel oil, light fuel oil is selected. For ambient heat, heat pump is selected. For bioenergy, wood logs combustion is used as representative technology.
##Data collection period##
2020
##Administrative Information##
##Data set generator##
Ellen Soldal
##Access and use restrictions##
Open
##References##
Norsk Fjernvarme (2021). Fjernkontrollen.no. [online] Available at https://www.fjernkontrollen.no/ Webpage. Access date: 27.08.2021.
##
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            [Name] => Electricity, Norwegian consumption mix, high voltage, to consumer
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                    [Name_BaseName] => Electricity, Norwegian consumption mix, high voltage, to consumer
                    [GeneralComment] => ##General Information##
##Product name##
Electricity, Norwegian consumption mix, high voltage, to consumer
##Reference flow##
1 kWh
##System boundaries##
Consumption mixes for high voltage electricity used in Norway in 2020, based on high voltage electricity mix given by NVE (2021) (Norwegian Water Resources and Energy Directorate). This high voltage mix includes electricity production in Norway and in the countries Norway exchanges electricity with (Denmark, Sweden, Netherlands, Finland and Russia).
Distribution network, direct emissions to air (sulfur hexafluoride, dinitrogen monoxide and methane) and electricity losses are accounted for, using ecoinvent data.
##Wastes and end-of-life##
Waste treatment is included in the background processes. Market processes for end-of-life treatment of infrastructure have been used. Cut-off modelling has been used, and no recycling credits have been included.
##Biogenic carbon##
Biological methane emissions from reservoirs have been included. These are reported as biogenic in the LCI.
##Use advice of the dataset##
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##Technological representativeness##
##Technology description##
Electricity from gas, coal, other fossil (assumed oil) and other renewable (assumed wood chips) have been assumed from combined heat and power plants. Hydro power imported from Sweden has been assumed as 100% run-of-river. Norwegian hydro power has been assumed as 76% reservoir and 24% run-of-river according to Silva and Modahl (2019).
##Technology Quality level##
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##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
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- Exported electricity from Norway is assumed produced in Norway.
- Import and export it is assumed that the electricity is crossing one country border only.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
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##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Not applicable in the foreground system. For the different electricity production processes (background processes), see ecoinvent 3.7.1 – allocation, cut-off.
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
100% is included in the foreground modelling. For the background processes (electricity produced by different technologies using different energy carriers, transformation and transmission network), see ecoinvent 3.7.1 – allocation, cut-off.
##Infrastructure/capital goods##
Infrastructure for dams, turbines, cables, equipment, buildings, roads etc is included in the background processes (electricity produced by different technologies using different energy carriers, transformation and distribution network).
##Data treatment and extrapolations principles##
The dataset is based on the high voltage electricity consumption mix given by NVE (2020), which is then transformed to medium and low voltage by using ecoinvent 3.7.1 – allocation, cut-off background processes.
##Data collection period##
2020
##Administrative Information##
##Data set generator##
Generated by Ingunn Saur Modahl
##Access and use restrictions##
##References##
NVE (2021): Hvor kommer strømmen fra? (Where does the electricity come from?) Norges vassdrags- og energidirektorat (Norwegian Water Resources and Energy Directorate), published 15.06.2020, updated 02.07.2021. Assessed 20.09.2021. Link: https://www.nve.no/energiforsyning/kraftproduksjon/hvor-kommer-strommen-fra/?ref=mainmenu#:~:text=Norge%20er%20en%20del%20av,hovedsak%20kom%20fra%20fornybare%20energikilder
Silva, M. and Modahl, I.S. (2019): The inventory and life cycle data for Norwegian hydroelectricity. Ostfold Research (now NORSUS), AR 01.19, public, May 2019 (based on AR 02.15 public memo). Link: https://norsus.no/publikasjon/the-inventory-and-life-cycle-data-for-norwegian-hydroelectricity/ ##
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The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
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##Technology Quality level##
Good
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##Location##
China
##Geographical representativeness description##
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##Geographical Quality level##
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##Reference year##
2020
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##LCI method principle##
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##Other comments on methods approaches##
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##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
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See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
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##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21 ##
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The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
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2020
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##Cut off for mass or energy flows##
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See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21 ##
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##Biogenic carbon##
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The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
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##Reference year##
2020
##Time representativeness description##
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##LCI method principle##
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##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
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##Cut off for mass or energy flows##
##Infrastructure/capital goods##
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##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21 ##
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##Biogenic carbon##
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The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
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##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Finland
##Geographical representativeness description##
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##Geographical Quality level##
Fair
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##Reference year##
2020
##Time representativeness description##
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##LCI method principle##
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##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
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##Cut off for mass or energy flows##
##Infrastructure/capital goods##
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See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21 ##
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Heat, for district heating, Norwegian production mix, without waste incineration or ambient heat, at plant
##Reference flow##
1 kWh
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##Wastes and end-of-life##
##Biogenic carbon##
##Use advice of the dataset##
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##Technological representativeness##
##Technology description##
Average technology for production and transmission of electricity in Norway. Heat from the other sources based on average European technologies. One technology is selected to represent each category of energy carriers. For fuel oil, light fuel oil is selected. For bioenergy, wood logs combustion is used as representative technology.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Total heat production and share of sources are representative for Norway. Background data are representative for European technology.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
Annual production.
##Time Quality Level##
Very good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
Allocation cut-off by classification in background data.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure is included
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##Data collection period##
2020
##Administrative Information##
##Data set generator##
Ellen Soldal
##Access and use restrictions##
Open
##References##
Norsk Fjernvarme (2021). Fjernkontrollen.no. [online] Available at https://www.fjernkontrollen.no/ Webpage. Access date: 27.08.2021.
##
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The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
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##Cut off for mass or energy flows##
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##Administrative Information##
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Irmeline de Sadeleer
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##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21 ##
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The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
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See documentation of the ecoinvent processes for further details about the modified datasets.
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Irmeline de Sadeleer
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World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21##
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##Wastes and end-of-life##
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##Biogenic carbon##
Not relevant
##Use advice of the dataset##
This dataset should be used to model the use of steel produced in Germany and uses ecoinvent 3.7.1 processes in the background. More precisely,
• For both steel processing technologies (oxygen blast furnace and electric converter) and the raw material (pig iron) production, the ecoinvent processes have been adjusted to German heat and electricity inputs.
• The share of recycled and virgin materials is adjusted based on the use of technologies in the country (World steel association, 2019).
The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
The share of recycled and virgin materials is based on the use of production technologies in Germany (oxygen blast furnace used for recycled material and electric converter used for virgin material) based on statistics from World Steel Association (2021). For further description of the technologies, see the description of the ecoinvent datasets.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Germany
##Geographical representativeness description##
Global and RoW ecoinvent processes for steel production technologies have been adjusted to represent German production.
##Geographical Quality level##
Fair
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
Elements modified from the ecoinvent datasets are based on yearly World Steel published data, representative for 2020.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure included
##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21 ##
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##Wastes and end-of-life##
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##Biogenic carbon##
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##Use advice of the dataset##
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• The share of recycled and virgin materials is adjusted based on the use of technologies in the country (World steel association, 2019).
The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
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##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Italy
##Geographical representativeness description##
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##Geographical Quality level##
Fair
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
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Good
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##LCI method principle##
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##LCI allocation methods##
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
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See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
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Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21 ##
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The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
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See documentation of the ecoinvent processes for further details about the modified datasets.
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See documentation of the ecoinvent processes for further details about the modified datasets.
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##Administrative Information##
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Irmeline de Sadeleer
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##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21 ##
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##Biogenic carbon##
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• The share of recycled and virgin materials is adjusted based on the use of technologies in the country (World steel association, 2019).
The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
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##Location##
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##Geographical representativeness description##
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##Geographical Quality level##
Fair
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
Elements modified from the ecoinvent datasets are based on yearly World Steel published data, representative for 2020.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
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##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21 ##
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##Biogenic carbon##
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The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
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##Technology Quality level##
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##Geographical representativeness##
##Location##
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##Geographical representativeness description##
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##Geographical Quality level##
Fair
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
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##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
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##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21 ##
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##Reference flow##
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##System boundaries##
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##Wastes and end-of-life##
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##Biogenic carbon##
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##Use advice of the dataset##
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##Technological representativeness##
##Technology description##
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##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
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- Imported electricity is assumed produced in the country from which it is imported.
- Exported electricity from Norway is assumed produced in Norway.
- Import and export it is assumed that the electricity is crossing one country border only.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
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##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Not applicable in the foreground system. For the different electricity production processes (background processes), see ecoinvent 3.7.1 – allocation, cut-off.
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
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##Infrastructure/capital goods##
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##Data treatment and extrapolations principles##
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##Data collection period##
2020
##Administrative Information##
##Data set generator##
Generated by Ingunn Saur Modahl
##Access and use restrictions##
##References##
NVE (2021): Hvor kommer strømmen fra? (Where does the electricity come from?) Norges vassdrags- og energidirektorat (Norwegian Water Resources and Energy Directorate), published 15.06.2020, updated 02.07.2021. Assessed 20.09.2021. Link: https://www.nve.no/energiforsyning/kraftproduksjon/hvor-kommer-strommen-fra/?ref=mainmenu#:~:text=Norge%20er%20en%20del%20av,hovedsak%20kom%20fra%20fornybare%20energikilder
Silva, M. and Modahl, I.S. (2019): The inventory and life cycle data for Norwegian hydroelectricity. Ostfold Research (now NORSUS), AR 01.19, public, May 2019 (based on AR 02.15 public memo). Link: https://norsus.no/publikasjon/the-inventory-and-life-cycle-data-for-norwegian-hydroelectricity/ ##
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##Reference flow##
1 kg
##System boundaries##
Cradle-to-gate. Starts with the production of raw material (pig iron) and ends with steel making process and casting with oxygen blast furnace and electric converter technologies.
##Wastes and end-of-life##
Processing waste is included, EoL waste of the steel not accounted for.
##Biogenic carbon##
Not relevant
##Use advice of the dataset##
This dataset should be used to model the use of steel produced in Italy and uses ecoinvent 3.7.1 processes in the background. More precisely,
• For both steel processing technologies (oxygen blast furnace and electric converter) and the raw material (pig iron) production, the ecoinvent processes have been adjusted to Italian heat and electricity inputs.
• The share of recycled and virgin materials is adjusted based on the use of technologies in the country (World steel association, 2019).
The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
The share of recycled and virgin materials is based on the use of production technologies in Italy (oxygen blast furnace used for recycled material and electric converter used for virgin material) based on statistics from World Steel Association (2021). For further description of the technologies, see the description of the ecoinvent datasets.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Italy
##Geographical representativeness description##
Global and RoW ecoinvent processes for steel production technologies have been adjusted to represent Italian production.
##Geographical Quality level##
Fair
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
Elements modified from the ecoinvent datasets are based on yearly World Steel published data, representative for 2020.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure included
##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21 ##
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            [Name] => Compressed upgraded biogas (biomethane/CBG); from food waste and manure; production, distribution, and use, minimum 97% methane
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            [CreatedOn] => 2021-10-22T08:28:08.1002028
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                    [Name_BaseName] => Compressed upgraded biogas (biomethane/CBG); from food waste and manure; production, distribution, and use, minimum 97% methane
                    [GeneralComment] => ##General Information##
##Product name##
Compressed upgraded biogas (biomethane/CBG); from food waste and manure; production, distribution, and use, minimum 97% methane
##Reference flow##
1 kWh
##System boundaries##
Includes pre-treatment of food waste at the biogas facility, anaerobic digestion, upgrading, distribution by truck and use of biogas in a combustion engine. Collection of food waste is not included in the dataset. Collection of manure is included in the system boundaries. Incineration and transport of reject from pre-treatment of food waste is included. No substitution of avoided products included.
##Wastes and end-of-life##
Includes transport and combustion of the pre-treatment reject.
##Biogenic carbon##
Biogenic methane and CO2 emissions are reported separately by in the LCI using biogenic substances.
##Use advice of the dataset##
This dataset should be used when modelling the use of biogas in transport applications. When using manure for biogas production, the storage time (and related emissions) of manure is reduced. This effect is not included in the dataset. Furthermore, this dataset focusses on the impact on climate change. One should therefore be cautious in using this dataset to analyse other impact categories, especially toxicity. The ecoinvent database (version 3.7.1 allocation cut-off by classification) has been used for background processes.
##Technological representativeness##
##Technology description##
1 kWh upgraded biogas from household waste and manure. As input material in the biogas facility, approximately 50% comes from household waste and 50% from manure sourced from surrounding farms. Production process: 1) Mechanical pretreatment to remove unwanted objects and reduction of particle size: screw press, grinder, hydro cyclone. 2) Sterilization process for 1 hour at 70 °C 3) Anaerobic digestion: Hydraulic retention time (HRT) 36.5 days. 4) Upgrading: water scrubber. Emissions from driving: EURO 5.
##Technology Quality level##
Very good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
The biogas facility is located in the Vestfold and Telemark county. Manure is transported from local farms.
##Geographical Quality level##
Very good
##Time related representativeness##
##Reference year##
2018
##Time representativeness description##
Report was published in 2020.
##Time Quality Level##
Very good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Processes that are common to biogas and biofertilizer from digestate (transport of manure, pre-treatment, incineration of pre-treatment reject, anaerobic digesting) are allocated between the end products based on dry mass. Treatment (incineration) of plastic in reject from pre-treatment is not included.
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure (biogas plant) included, based on literature data from Brogaard et al., 2015, Quantifying capital goods of organic waste treatment, Table 6. Service life of the biogas plant is assumed to be 30 years.
##Data treatment and extrapolations principles##
##Data collection period##
2018
##Administrative Information##
##Data set generator##
Kari-Anne Lyng, NORSUS
##Access and use restrictions##
Open
##References##
Lyng, K.-A., Saxegård, S., 2020, Livsløpsvurdering av produktene og tjenestene til Den Magiske Fabrikken. OR.23.20. NORSUS, Kråkerøy. Available from: https://norsus.no/publikasjon/livslopsvurdering-av-produktene-og-tjenestene-til-den-magiske-fabrikken/ (In Norwegian)
Brogaard, L. K., Petersen, P. H., Nielsen, P. D. & Christensen, T. H. (2015). Quantifying capital
goods for biological treatment of organic waste. Waste management & research: the journal
of the International Solid Wastes and Public Cleansing Association, ISWA, 33(2), 96-106.
https://doi.org/10.1177/0734242X14565212
##
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            [Name] => Low alloyed steel, aluzinc, galvanized, at plant, 43% zinc, 55% aluminium and 2% silicon, galvanized layer amounts to 6% of steel mass.
            [GeographyId] => 114ebb04-7d7e-11de-9ae2-0019e336be3a
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                    [GeneralComment] => ##General Information##
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Low alloyed steel, aluzinc, galvanized, at plant, 43% zinc, 55% aluminium and 2% silicon, galvanized layer amounts to 6% of steel mass.
##Reference flow##
1 kg
##System boundaries##
Cradle-to-gate. Starts with the production of raw material (pig iron) and ends with galvanization of steel parts.
##Wastes and end-of-life##
Processing waste is included, EoL waste of the steel not accounted for.
##Biogenic carbon##
Not relevant
##Use advice of the dataset##
This dataset should be used to model the use of steel produced in Poland and uses ecoinvent 3.7.1 processes in the background. More precisely,
• For both steel processing technologies (oxygen blast furnace and electric converter), the raw material (pig iron) production and the galvanization process, the ecoinvent processes have been adjusted to Polish heat and electricity inputs.
• The share of recycled and virgin materials is adjusted based on the use of technologies in the country (World steel association, 2021).
The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
The share of recycled and virgin materials is based on the use of production technologies in Poland (oxygen blast furnace used for recycled material and electric converter used for virgin material) based on statistics from World Steel Association (2021). For further description of the technologies, see the description of the ecoinvent datasets.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Poland
##Geographical representativeness description##
Global and RoW ecoinvent processes for steel production technologies adjusted to represent Polish production.
##Geographical Quality level##
Fair
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
Elements modified from the ecoinvent datasets are based on yearly World Steel published data, representative for 2020.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Allocation done by ecoinvent
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure included
##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21 ##
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            [Name] => Microfibrillated cellulose, Exilva Forte; without water, at plant, in 2% solution state
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                    [Name_BaseName] => Microfibrillated cellulose, Exilva Forte; without water, at plant, in 2% solution state
                    [GeneralComment] => ##General Information##
##Product name##
Microfibrillated cellulose, Exilva Forte; without water, at plant, in 2% solution state
##Reference flow## 1 kg dry product
##System boundaries## Production of microfibrillated cellulose (MFC) of the Exilva Forte grade, in a 2% concentration at the Exilva industrial MFC plant in Sarpsborg, Norway. The functional unit is 1 kg dry product at factory gate, without packaging.
The modelling of the Exilva plant has been part of work package 7 (WP7) of the H2020 BBI JU Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). The modelling of the Borregaard biorefinery and the Exilva site have been documented in the following reports: Modahl and Soldal (2015) and Modahl, Brekke, Valente and Soldal (2016). The 2011 status of the specialty cellulose was published as a scientific paper by Modahl, Brekke and Valente (2015). Exilva MFC comes in four grades: Piano Light, Piano, Forte and Forte Plus, and all qualities are produced both as a 2% suspension and a 10% paste.
##Wastes and end-of-life##
##Biogenic carbon##
##Use advice of the dataset## MFC has many different application areas where different characteristics are emphasised. If the dataset is used for comparing Exilva MFC to other materials for the same purpose, special attention should be directed to product formulations and amounts. Necessary data to perform toxicity assessments has not been collected. Data are given for the active substance of the product. It is, however, sold and used with relative large amounts of water. Hence, make sure you calculate the correct amount of active substance. An example: you are transporting 1 kg of 2% Exilva. The amount of active substance is 20g. To calculate the correct transport volume, you will need to transport 20g of active substance and 980g water.
##Technological representativeness##
##Technology description## Production of the specialty cellulose, which is the main raw material for the MFC, takes place in an advanced biorefinery which also produces lignosulfonates, bioethanol, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Further processing of the specialty cellulose to MFC is made in a full-scale industrial plant built in 2016 (Exilva site). The main raw material of the biorefinery is Norway Spruce from Norway and Sweden. Specific data has been used for all activities at the Borregaard biorefinery and Exilva sites. Data for the biorefinery are from 2015 and data for the Exilva plant are from 2016 and 2018. The Ecoinvent database (version 3.7.1 allocation cut-off by classification) has been used for background processes. The biorefinery and the Exilva plant have been modelled on a detailed level, avoiding allocation to a large extent. Energy allocation has been used when necessary. An attributional approach has been used. To be in line with the EPDs made for the biorefinery's products (including the specialty cellulose), the following system boundaries have been applied: Norwegian electricity production mix has been used for electricity used in Norway, emissions from combustion of waste for heat production at the biorefinery are not allocated the user of the heat, rather the producer of the waste, and emissions from combustion of waste oil and biogas have been allocated the user. Infrastructure is included (in contrast to the EPDs, where infrastructure in the form of production equipment with an expected lifetime over three years, buildings and capital goods are not included except for energy carriers). The LCA software SimaPro (version 9.2.0) was used to model the system.
##Technology Quality level## Very good
##Geographical representativeness##
##Location## Norway
##Geographical representativeness description## Data for the biorefinery is collected from, and specific for, the given biorefinery in Sarpsborg, Norway.
##Geographical Quality level## Very good
##Time related representativeness##
##Reference year## 2016
##Time representativeness description## Data for the Exilva plant is from 2016, and for the biorefinery data for the major flows are from 2015 (specialty cellulose as raw material). Data used are based on annual numbers. For the biorefinery these numbers could change somewhat from year to year but the burdens per kg of specialty cellulose would not vary much. Changes in the energy carrier mix of the biorefinery could, however, alter resource use, emissions and waste generation. For the Exilva plant (microfibrillation of specialty cellulose), flows per kg of product will be quite constant.
##Time Quality Level## Very good
##Methodological appropriateness and consistency##
##LCI method principle## Attributional
##LCI allocation methods##
##Other comments on methods approaches## The biorefinery (producing specialty cellulose as a raw material for the microfibrillated cellulose) and the Exilva plant have been modelled on a detailed level, largely avoiding the need for allocation. Energy allocation has been used when necessary. For steam and hot water, the enthalpy has been used. Infrastructure is included. Emissions from combustion of waste for heat production at the biorefinery are not allocated to the user of the heat, rather to the producer of the waste. Emissions from combustion of waste oil and biogas have been allocated to the user.
##Completeness##
##Cut off for mass or energy flows## No cut off in the foreground system (Exilva plant) and the biorefinery producing specialty cellulose as a raw material to the Exilva plant.
##Infrastructure/capital goods##
##Data treatment and extrapolations principles##
##Data collection period## 2008 - 2018
##Administrative Information##
##Data set generator## Ingunn Saur Modahl
##Access and use restrictions## Free
##References##
Modahl, I.S. and Soldal, E. (2015): The 2015 LCA of products from the wood-based biorefinery at Borregaard, Sarpsborg. Results for cellulose, ethanols, lignosulfonates, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Ostfold Research, OR 11.15, April 2016.
Modahl, I.S., Brekke, A. and Valente, C. (2015): Environmental assessment of chemical products from a Norwegian biorefinery. Journal of Cleaner Production 94 (2015) 247-259.
Modahl, I.S., Brekke, A., Valente, C., and Soldal, E. (2016): E-LCA and S-LCA of the Exilva MFC process. AR 10.16 Ostfold Research. Deliverable report D7.2 from work package 7 of the H2020 Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). Confidential. November 2016.
##
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            [Name] => Low alloyed steel, aluzinc, galvanized, at plant, 43% zinc, 55% aluminium and 2% silicon, galvanized layer amounts to 6% of steel mass.
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                    [GeneralComment] => ##General Information##
##Product name##
Low alloyed steel, aluzinc, galvanized, at plant, 43% zinc, 55% aluminium and 2% silicon, galvanized layer amounts to 6% of steel mass.
##Reference flow##
1 kg
##System boundaries##
Cradle-to-gate. Starts with the production of raw material (pig iron) and ends with galvanization of steel parts.
##Wastes and end-of-life##
Processing waste is included, EoL waste of the steel not accounted for.
##Biogenic carbon##
Not relevant
##Use advice of the dataset##
This dataset should be used to model the use of steel produced in Germany and uses ecoinvent 3.7.1 processes in the background. More precisely,
• For both steel processing technologies (oxygen blast furnace and electric converter), the raw material (pig iron) production and the galvanization process, the ecoinvent processes have been adjusted to german heat and electricity inputs.
• The share of recycled and virgin materials is adjusted based on the use of technologies in the country (World steel association, 2021).
The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
The share of recycled and virgin materials is based on the use of production technologies in Germany (oxygen blast furnace used for recycled material and electric converter used for virgin material) based on statistics from World Steel Association (2021). For further description of the technologies, see the description of the ecoinvent datasets.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Germany
##Geographical representativeness description##
Global and RoW ecoinvent processes for steel production technologies adjusted to represent german production.
##Geographical Quality level##
Fair
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
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##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Allocation done by ecoinvent
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure included
##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21 ##
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##Wastes and end-of-life##
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##Biogenic carbon##
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The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
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##Technology Quality level##
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##Location##
China
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##Geographical Quality level##
Fair
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
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##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure included
##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21 ##
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##Biogenic carbon##
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The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
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##Technology description##
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##Technology Quality level##
Good
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##Location##
China
##Geographical representativeness description##
Global and RoW ecoinvent processes for steel production technologies have been adjusted to represent Chinese production.
##Geographical Quality level##
Fair
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##Reference year##
2020
##Time representativeness description##
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##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure included
##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21
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            [Name] => Wood chips, beech; wet, manual harvest, hardwood forestry; at forest road
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                    [GeneralComment] => ##General Information##
##Product name##
Wood chips, beech; wet, manual harvest, hardwood forestry; at forest road
##Reference flow##
1 kg DM
##System boundaries##
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The activity starts with site preparation assuming establishment of the forest via planting, including seedling production and covers all process related to forest management, including site preparation, planting, tending, young growth tending, clearing, thinning, and harvesting operations including the processing of wood fuel to chips, bundles and chopped wood (logs for energy) over one rotation period. It also covers the maintenance and construction of forest roads. This activity ends with the assortments at the forest road and includes eventual drying before transportation.
##Wastes and end-of-life##
##Biogenic carbon##
Biogenic carbon included as resource input from nature (uptake of carbon dioxide in air)
##Use advice of the dataset##
The dataset should be used to analyze the impact from beech wood chips that originate from thinning operations in France. Measured as dry mass
##Technological representativeness##
##Technology description##
For the chipping in the stand, a productivity of 25 m3 bulked/PMH is assumed (based on Cremer & Velazquesz). For wood chips chipped in the stand, a productivity of the forwarding of 70 Sm3 bulked/PMH with a diesel consumption of 9.5 l/h of the forwarder is assumed. 24.4% thinning (mechanical) and 76.6% (manual) final harvest
##Technology Quality level##Fair
##Geographical representativeness##
##Location## France
##Geographical representativeness description##
The dataset is based on dataset on harvest of hardwood in Germany from Wernet et al. (2016). The dataset has been adjusted to reflect beech forests, and beech forest management in France. The adjustments include changes in the harvesting methods to mirror the relative share of manual harvesting vs mechanized harvesters in the eastern parts on France, the allocation between clear-cut and thinning, and the area used for different practices (Pelletier, 2017).
##Geographical Quality level##
Fair
##Time related representativeness##
##Reference year##
2017
##Time representativeness description## Data represent 2017 for the specifications of French beech management while original ecoinvent data are largely based on a data source from 2009. Data are still considered representative due to small developments in forest management and harvest operations.
##Time Quality Level##
Fair
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Allocation based on mass.
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
All known inputs are included.
##Infrastructure/capital goods##
Construction and maintenance of roads and machinery are included
##Data treatment and extrapolations principles##
In communication with French Institute of Technology for Forest-based and Furniture Sectors (FCBA), it was recommended to use the ecoinvent (v3.7.1) dataset for beech from Germany as representative for beech from the eastern parts of France. The ecoinvent database provides well documented process data and is widely used in Europe. The data set “hardwood forestry, beech, sustainable forest management” was selected. This dataset covers the production and harvesting of 1 m3 of stemwood, beech, solid, under bark, plus the relative share of energy wood from slash from sustainable forest management as the prevailing management practices in Germany (Wernet et al., 2016). Changes were made to allocation principles, harvesting regimes, and area used to reflect French beech forestry (Pelletier, 2017).
##Data collection period##
2017 - 2021
##Administrative Information##
##Data set generator##
Andreas Brekke
##Access and use restrictions##
Open
##References##
Cremer, T. and B. Velazques-Marti (2007): Evaluation of two harvesting systems for the supply of woodchips in Norway spurce forest affected by bark beetle. Croation Journal of Forest Engineering, 28(2): 145-155.
Pelletier, C. (2017). Analyse environnementale et économique des filières bois-énergie. Université de Lorraine, Retrieved from https://tel.archives-ouvertes.fr/tel-01765854/document (2017LORR0331)
Wernet, G., Bauer, C., Steubing, B., Reinhard, J., Moreno-Ruiz, E., & Weidema, B. (2016). The ecoinvent database version 3 (part I): overview and methodology. The International Journal of Life Cycle Assessment, 21(9), 1218-1230. doi:10.1007/s11367-016-1087-8
##
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Microfibrillated cellulose, Exilva Forte, without water, at plant, in 10% solution state
##Reference flow## 1 kg dry product
##System boundaries## Production of microfibrillated cellulose (MFC) of the Exilva Forte grade, in a 10% concentration at the Exilva industrial MFC plant in Sarpsborg, Norway. The functional unit is 1 kg dry product at factory gate, without packaging.
The modelling of the Exilva plant has been part of work package 7 (WP7) of the H2020 BBI JU Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). The modelling of the Borregaard biorefinery and the Exilva site have been documented in the following reports: Modahl and Soldal (2015) and Modahl, Brekke, Valente and Soldal (2016). The 2011 status of the specialty cellulose was published as a scientific paper by Modahl, Brekke and Valente (2015). Exilva MFC comes in four grades: Piano Light, Piano, Forte and Forte Plus, and all qualities are produced both as a 2% suspension and a 10% paste.
##Wastes and end-of-life##
##Biogenic carbon##
##Use advice of the dataset## MFC has many different application areas where different characteristics are emphasised. If the dataset is used for comparing Exilva MFC to other materials for the same purpose, special attention should be directed to product formulations and amounts. Necessary data to perform toxicity assessments has not been collected. Data are given for the active substance of the product. It is, however, sold and used with relative large amounts of water. Hence, make sure you calculate the correct amount of active substance. An example: you are transporting 1 kg of 2% Exilva. The amount of active substance is 20g. To calculate the correct transport volume, you will need to transport 20g of active substance and 980g water.
##Technological representativeness##
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##Technology Quality level## Very good
##Geographical representativeness##
##Location## Norway
##Geographical representativeness description## Data for the biorefinery is collected from, and specific for, the given biorefinery in Sarpsborg, Norway.
##Geographical Quality level## Very good
##Time related representativeness##
##Reference year## 2016
##Time representativeness description## Data for the Exilva plant is from 2016, and for the biorefinery data for the major flows are from 2015 (specialty cellulose as raw material). Data used are based on annual numbers. For the biorefinery these numbers could change somewhat from year to year but the burdens per kg of specialty cellulose would not vary much. Changes in the energy carrier mix of the biorefinery could, however, alter resource use, emissions and waste generation. For the Exilva plant (microfibrillation of specialty cellulose), flows per kg of product will be quite constant.
##Time Quality Level## Very good
##Methodological appropriateness and consistency##
##LCI method principle## Attributional
##LCI allocation methods##
##Other comments on methods approaches## The biorefinery (producing specialty cellulose as a raw material for the microfibrillated cellulose) and the Exilva plant have been modelled on a detailed level, largely avoiding the need for allocation. Energy allocation has been used when necessary. For steam and hot water, the enthalpy has been used. Infrastructure is included. Emissions from combustion of waste for heat production at the biorefinery are not allocated to the user of the heat, rather to the producer of the waste. Emissions from combustion of waste oil and biogas have been allocated to the user.
##Completeness##
##Cut off for mass or energy flows## No cut off in the foreground system (Exilva plant) and the biorefinery producing specialty cellulose as a raw material to the Exilva plant.
##Infrastructure/capital goods##
##Data treatment and extrapolations principles##
##Data collection period## 2008 - 2018
##Administrative Information##
##Data set generator## Ingunn Saur Modahl
##Access and use restrictions## Free
##References##
Modahl, I.S. and Soldal, E. (2015): The 2015 LCA of products from the wood-based biorefinery at Borregaard, Sarpsborg. Results for cellulose, ethanols, lignosulfonates, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Ostfold Research, OR 11.15, April 2016.
Modahl, I.S., Brekke, A. and Valente, C. (2015): Environmental assessment of chemical products from a Norwegian biorefinery. Journal of Cleaner Production 94 (2015) 247-259.
Modahl, I.S., Brekke, A., Valente, C., and Soldal, E. (2016): E-LCA and S-LCA of the Exilva MFC process. AR 10.16 Ostfold Research. Deliverable report D7.2 from work package 7 of the H2020 Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). Confidential. November 2016.
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Microfibrillated cellulose, Exilva Piano, without water, at plant, in 10% solution state
##Reference flow## 1 kg dry product
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The modelling of the Exilva plant has been part of work package 7 (WP7) of the H2020 BBI JU Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). The modelling of the Borregaard biorefinery and the Exilva site have been documented in the following reports: Modahl and Soldal (2015) and Modahl, Brekke, Valente and Soldal (2016). The 2011 status of the specialty cellulose was published as a scientific paper by Modahl, Brekke and Valente (2015). Exilva MFC comes in four grades: Piano Light, Piano, Forte and Forte Plus, and all qualities are produced both as a 2% suspension and a 10% paste.
##Wastes and end-of-life##
##Biogenic carbon##
##Use advice of the dataset## MFC has many different application areas where different characteristics are emphasised. If the dataset is used for comparing Exilva MFC to other materials for the same purpose, special attention should be directed to product formulations and amounts. Necessary data to perform toxicity assessments has not been collected. Data are given for the active substance of the product. It is, however, sold and used with relative large amounts of water. Hence, make sure you calculate the correct amount of active substance. An example: you are transporting 1 kg of 2% Exilva. The amount of active substance is 20g. To calculate the correct transport volume, you will need to transport 20g of active substance and 980g water.
##Technological representativeness##
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##Technology Quality level## Very good
##Geographical representativeness##
##Location## Norway
##Geographical representativeness description## Data for the biorefinery is collected from, and specific for, the given biorefinery in Sarpsborg, Norway.
##Geographical Quality level## Very good
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##Reference year## 2016
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##Time Quality Level## Very good
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##LCI allocation methods##
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##Completeness##
##Cut off for mass or energy flows## No cut off in the foreground system (Exilva plant) and the biorefinery producing specialty cellulose as a raw material to the Exilva plant.
##Infrastructure/capital goods##
##Data treatment and extrapolations principles##
##Data collection period## 2008 - 2018
##Administrative Information##
##Data set generator## Ingunn Saur Modahl
##Access and use restrictions## Free
##References##
Modahl, I.S. and Soldal, E. (2015): The 2015 LCA of products from the wood-based biorefinery at Borregaard, Sarpsborg. Results for cellulose, ethanols, lignosulfonates, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Ostfold Research, OR 11.15, April 2016.
Modahl, I.S., Brekke, A. and Valente, C. (2015): Environmental assessment of chemical products from a Norwegian biorefinery. Journal of Cleaner Production 94 (2015) 247-259.
Modahl, I.S., Brekke, A., Valente, C., and Soldal, E. (2016): E-LCA and S-LCA of the Exilva MFC process. AR 10.16 Ostfold Research. Deliverable report D7.2 from work package 7 of the H2020 Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). Confidential. November 2016.
##
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            [Name] => Microfibrillated cellulose, Exilva Piano, without water, at plant, in 2% solution state
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                    [Name_BaseName] => Microfibrillated cellulose, Exilva Piano, without water, at plant, in 2% solution state
                    [GeneralComment] => ##General Information##
##Product name## Microfibrillated cellulose, Exilva Piano, without water, at plant, in 2% solution state
##Reference flow## 1kg dry product
##System boundaries## Production of microfibrillated cellulose (MFC) of the Exilva Piano grade, in a 2% concentration at the Exilva industrial MFC plant in Sarpsborg, Norway. The functional unit is 1 kg dry product at factory gate, without packaging.
The modelling of the Exilva plant has been part of work package 7 (WP7) of the H2020 BBI JU Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). The modelling of the Borregaard biorefinery and the Exilva site have been documented in the following reports: Modahl and Soldal (2015) and Modahl, Brekke, Valente and Soldal (2016). The 2011 status of the specialty cellulose was published as a scientific paper by Modahl, Brekke and Valente (2015). Exilva MFC comes in four grades: Piano Light, Piano, Forte and Forte Plus, and all qualities are produced both as a 2% suspension and a 10% paste.
##Wastes and end-of-life##
##Biogenic carbon##
##Use advice of the dataset## MFC has many different application areas where different characteristics are emphasised. If the dataset is used for comparing Exilva MFC to other materials for the same purpose, special attention should be directed to product formulations and amounts. Necessary data to perform toxicity assessments has not been collected. Data are given for the active substance of the product. It is, however, sold and used with relative large amounts of water. Hence, make sure you calculate the correct amount of active substance. An example: you are transporting 1 kg of 2% Exilva. The amount of active substance is 20g. To calculate the correct transport volume, you will need to transport 20g of active substance and 980g water.
##Technological representativeness##
##Technology description## Production of the specialty cellulose, which is the main raw material for the MFC, takes place in an advanced biorefinery which also produces lignosulfonates, bioethanol, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Further processing of the specialty cellulose to MFC is made in a full-scale industrial plant built in 2016 (Exilva site). The main raw material of the biorefinery is Norway Spruce from Norway and Sweden. Specific data has been used for all activities at the Borregaard biorefinery and Exilva sites. Data for the biorefinery are from 2015 and data for the Exilva plant are from 2016 and 2018. The Ecoinvent database (version 3.7.1 allocation cut-off by classification) has been used for background processes. The biorefinery and the Exilva plant have been modelled on a detailed level, avoiding allocation to a large extent. Energy allocation has been used when necessary. An attributional approach has been used. To be in line with the EPDs made for the biorefinery's products (including the specialty cellulose), the following system boundaries have been applied: Norwegian electricity production mix has been used for electricity used in Norway, emissions from combustion of waste for heat production at the biorefinery are not allocated the user of the heat, rather the producer of the waste, and emissions from combustion of waste oil and biogas have been allocated the user. Infrastructure is included (in contrast to the EPDs, where infrastructure in the form of production equipment with an expected lifetime over three years, buildings and capital goods are not included except for energy carriers). The LCA software SimaPro (version 9.2.0) was used to model the system.
##Technology Quality level## Very good
##Geographical representativeness##
##Location## Norway
##Geographical representativeness description## Data for the biorefinery is collected from, and specific for, the given biorefinery in Sarpsborg, Norway
##Geographical Quality level## Very good
##Time related representativeness##
##Reference year## 2016
##Time representativeness description## Data for the Exilva plant is from 2016, and for the biorefinery data for the major flows are from 2015 (specialty cellulose as raw material). Data used are based on annual numbers. For the biorefinery these numbers could change somewhat from year to year but the burdens per kg of specialty cellulose would not vary much. Changes in the energy carrier mix of the biorefinery could, however, alter resource use, emissions and waste generation. For the Exilva plant (microfibrillation of specialty cellulose), flows per kg of product will be quite constant.
##Time Quality Level## Very good
##Methodological appropriateness and consistency##
##LCI method principle## Attributional
##LCI allocation methods##
##Other comments on methods approaches## The biorefinery (producing specialty cellulose as a raw material for the microfibrillated cellulose) and the Exilva plant have been modelled on a detailed level, largely avoiding the need for allocation. Energy allocation has been used when necessary. For steam and hot water, the enthalpy has been used. Infrastructure is included. Emissions from combustion of waste for heat production at the biorefinery are not allocated to the user of the heat, rather to the producer of the waste. Emissions from combustion of waste oil and biogas have been allocated to the user.
##Completeness##
##Cut off for mass or energy flows## No cut off in the foreground system (Exilva plant) and the biorefinery producing specialty cellulose as a raw material to the Exilva plant.
##Infrastructure/capital goods##
##Data treatment and extrapolations principles##
##Data collection period## 2008 – 2018
##Administrative Information##
##Data set generator## Ingunn Saur Modahl
##Access and use restrictions## Free
##References##
Modahl, I.S. and Soldal, E. (2015): The 2015 LCA of products from the wood-based biorefinery at Borregaard, Sarpsborg. Results for cellulose, ethanols, lignosulfonates, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Ostfold Research, OR 11.15, April 2016.
Modahl, I.S., Brekke, A. and Valente, C. (2015): Environmental assessment of chemical products from a Norwegian biorefinery. Journal of Cleaner Production 94 (2015) 247-259.
Modahl, I.S., Brekke, A., Valente, C., and Soldal, E. (2016): E-LCA and S-LCA of the Exilva MFC process. AR 10.16 Ostfold Research. Deliverable report D7.2 from work package 7 of the H2020 Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). Confidential. November 2016.
##
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                    [GeneralComment] => ##General Information##
##Product name##
Low alloyed steel, casted, Primary and secondary production, at plant, 67% primary material, 33% recycled material
##Reference flow##
1 kg
##System boundaries##
Cradle-to-gate. Starts with the production of raw material (pig iron) and ends with steel making process and casting with oxygen blast furnace and electric converter technologies.
##Wastes and end-of-life##
Processing waste is included, EoL waste of the steel not accounted for.
##Biogenic carbon##
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##Use advice of the dataset##
This dataset should be used to model the use of steel produced in Sweden and uses ecoinvent 3.7.1 processes in the background. More precisely,
• For both steel processing technologies (oxygen blast furnace and electric converter) and the raw material (pig iron) production, the ecoinvent processes have been adjusted to Swedish heat and electricity inputs.
• The share of recycled and virgin materials is adjusted based on the use of technologies in the country (World steel association, 2019).
The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
The share of recycled and virgin materials is based on the use of production technologies in Sweden (oxygen blast furnace used for recycled material and electric converter used for virgin material) based on statistics from World Steel Association (2021). For further description of the technologies, see the description of the ecoinvent datasets.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Sweden
##Geographical representativeness description##
Global and RoW ecoinvent processes for steel production technologies have been adjusted to represent Swedish production.
##Geographical Quality level##
Fair
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
Elements modified from the ecoinvent datasets are based on yearly World Steel published data, representative for 2020.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
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##LCI allocation methods##
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
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##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21 ##
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The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
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##LCI method principle##
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##LCI allocation methods##
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
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See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
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##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21 ##
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The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
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Irmeline de Sadeleer
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World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21 ##
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##Administrative Information##
##Data set generator##
Pieter Callewaert
##Access and use restrictions##
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##References##
Callewaert, P. (2017). Analysing the sustainability performance and critical improvement factors of urban municipal waste systems. (Masters). NTNU, Trondheim. Retrieved from http://hdl.handle.net/11250/2454900
##
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                    [Name_BaseName] => Electricity, Norwegian consumption mix, medium voltage, to consumer
                    [GeneralComment] => ##General Information##
##Product name##
Electricity, Norwegian consumption mix, medium voltage, to consumer
##Reference flow##
1 kWh
##System boundaries##
Consumption mixes for medium voltage electricity used in Norway in 2020, based on high voltage electricity mix given by NVE (2021) (Norwegian Water Resources and Energy Directorate). This high voltage mix includes electricity production in Norway and in the countries Norway exchanges electricity with (Denmark, Sweden, Netherlands, Finland and Russia).
Transformation from high to medium voltage is included. Distribution network, direct emissions to air (sulfur hexafluoride, dinitrogen monoxide and methane) and electricity losses are accounted for, using ecoinvent data.
##Wastes and end-of-life##
Waste treatment is included in the background processes. Market processes for end-of-life treatment of infrastructure have been used. Cut-off modelling has been used, and no recycling credits have been included.
##Biogenic carbon##
Biological methane emissions from reservoirs have been included. These are reported as biogenic in the LCI.
##Use advice of the dataset##
This dataset represents medium voltage electricity used at consumer in Norway. Users should be aware to choose the correct voltage level. Ecoinvent 3.7.1 has been used for the background processes.
##Technological representativeness##
##Technology description##
Electricity from gas, coal, other fossil (assumed oil) and other renewable (assumed wood chips) have been assumed from combined heat and power plants. Hydro power imported from Sweden has been assumed as 100% run-of-river. Norwegian hydro power has been assumed as 76% reservoir and 24% run-of-river according to Silva and Modahl (2019).
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Calculation of the high voltage electricity mix is based on the following by NVE (2020):
- Imported electricity is assumed produced in the country from which it is imported.
- Exported electricity from Norway is assumed produced in Norway.
- Import and export it is assumed that the electricity is crossing one country border only.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
To calculate the Norwegian high voltage mix, the net import and export for each country has been calculated by the hour throughout one year and then summed (NVE 2020). The electricity mix is specific for Norway in 2020, while the background processes (electricity produced by different technologies using different energy carriers, transformation and distribution network) are from ecoinvent 3.7.1 and may be older.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Not applicable in the foreground system. For the different electricity production processes (background processes), see ecoinvent 3.7.1 – allocation, cut-off.
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
100% is included in the foreground modelling. For the background processes (electricity produced by different technologies using different energy carriers, transformation and transmission network), see ecoinvent 3.7.1 – allocation, cut-off.
##Infrastructure/capital goods##
Infrastructure for dams, turbines, cables, equipment, buildings, roads etc is included in the background processes (electricity produced by different technologies using different energy carriers, transformation and distribution network).
##Data treatment and extrapolations principles##
The dataset is based on the high voltage electricity consumption mix given by NVE (2020), which is then transformed to medium and low voltage by using ecoinvent 3.7.1 – allocation, cut-off background processes.
##Data collection period##
2020
##Administrative Information##
##Data set generator##
Generated by Ingunn Saur Modahl
##Access and use restrictions##
##References##
NVE (2021): Hvor kommer strømmen fra? (Where does the electricity come from?) Norges vassdrags- og energidirektorat (Norwegian Water Resources and Energy Directorate), published 15.06.2020, updated 02.07.2021. Assessed 20.09.2021. Link: https://www.nve.no/energiforsyning/kraftproduksjon/hvor-kommer-strommen-fra/?ref=mainmenu#:~:text=Norge%20er%20en%20del%20av,hovedsak%20kom%20fra%20fornybare%20energikilder
Silva, M. and Modahl, I.S. (2019): The inventory and life cycle data for Norwegian hydroelectricity. Ostfold Research (now NORSUS), AR 01.19, public, May 2019 (based on AR 02.15 public memo). Link: https://norsus.no/publikasjon/the-inventory-and-life-cycle-data-for-norwegian-hydroelectricity/ ##
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this exists. 1738this exists. 1832this exists. 1740this exists. 1741this exists. 1742this exists. 1743this exists. 1744this exists. 1920this exists. 1745this exists. 1746this exists. 1747this exists. 1748this exists. 1749this exists. 1750this exists. 1751this exists. 1752this exists. 1753this exists. 1924this exists. 1754this exists. 1763this exists. 1755this exists. 1756this exists. 1757this exists. 1919this exists. 1764this exists. 1765this exists. 1766this exists. 1758this exists. 1759this exists. 1760this exists. 1761this exists. 1762Østfoldforskning

Alle tags

Array
(
    [01c964d9-b663-4893-80a4-d8b0f120ebb7] => Sector_End-of-life treatment / Material recycling
)
process:

Alle prosesser

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                    [Id] => 34dbbff8-88ce-11de-ad60-0019e336be3a
                    [LibraryId] => 34dbbff8-88ce-11de-ad60-0019e336be3a
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                    [LibraryId] => d10a1ddc-48b9-476c-9677-47eaf482466a
                    [Name] => HDPE granulate, from household waste, at plant, DE System
                )

            [CreatedOn] => 2021-02-19T08:31:43.4584743
            [ModifiedOn] => 2021-10-04T08:51:16.3502085
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                    [UUID] => 01c964d9-b663-4893-80a4-d8b0f120ebb7
                    [Name_BaseName] => HDPE granulate, from Norwegian household waste, at gate
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##Product name##
##Reference flow##
##System boundaries##
##Wastes and end-of-life##
##Biogenic carbon##
##Use advice of the dataset##
##Technological representativeness##
##Technology description##
##Technology Quality level##
##Geographical representativeness##
##Location##
##Geographical representativeness description##
##Geographical Quality level##
##Time related representativeness##
##Reference year##
##Time representativeness description##
##Time Quality Level##
##Methodological appropriateness and consistency##
##LCI method principle##
##LCI allocation methods##
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
##Data treatment and extrapolations principles##
##Data collection period##
##Administrative Information##
##Data set generator##
##Access and use restrictions##
##References##
##
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                    [Time_DataSetValidUntil] => 2026
                    [Time_RepresentativenessDescription] => 5 years default
                    [Geography_OperationSupplyOrProductionDescriptionOfRestrictions] => Restricted to specified geography
                    [Geography_LocationOfOperationSupplyOrProduction] => GLO
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            [ModelingAndValidation] => Array
                (
                    [LCIMethodAndAllocation_TypeOfDataSet] => Unit_process_single_operation
                    [LCIMethodAndAllocation_LCIMethodPrinciple] => Attributional
                    [LCIMethodAndAllocation_LCIMethodApproaches] => Array
                        (
                            [Not_applicable] => Not applicable
                        )

                    [DataSourcesTreatmentAndRepresentativeness_ReferencesToDataSource] => Array
                        (
                            [0] => Array
                                (
                                    [Id] => 9ba3ac1e-6797-4cc0-afd5-1b8f7bf28c6a
                                    [LibraryId] => 9ba3ac1e-6797-4cc0-afd5-1b8f7bf28c6a
                                    [Name] => ILCD Data Network - compliance (non-Process)
                                )

                        )

                    [DataSourcesTreatmentAndRepresentativeness_PercentageSupplyOrProductionCovered] => 100
                    [Completeness_CompletenessProductModel] => No_statement
                    [Completeness_ElementaryFlows] => Array
                        (
                        )

                    [ComplianceDeclarations_Compliance] => Array
                        (
                            [0] => Array
                                (
                                    [QualityCompliance] => Not_defined
                                    [NomenclatureCompliance] => Not_defined
                                    [MethodologicalCompliance] => Not_defined
                                    [ReviewCompliance] => Not_defined
                                    [DocumentationCompliance] => Not_defined
                                    [ReferenceToComplianceSystem] => Array
                                        (
                                            [Id] => 9ba3ac1e-6797-4cc0-afd5-1b8f7bf28c6a
                                            [LibraryId] => 9ba3ac1e-6797-4cc0-afd5-1b8f7bf28c6a
                                            [Name] => ILCD Data Network - Entry-level
                                        )

                                    [ApprovalOfOverallCompliance] => Not_defined
                                )

                        )

                )

            [AdministrativeInformation] => Array
                (
                    [CommissionerAndGoal_ReferenceToCommissioner] => Array
                        (
                            [0] => Array
                                (
                                    [Id] => f85f5371-0565-455c-a114-155dacbcb44d
                                    [LibraryId] => 7f556cf2-6175-4787-a115-9cd4bab0eb74
                                    [Name] => Pieter Callewaert
                                    [Email] => pieter@norsus.no
                                )

                        )

                    [DataGenerator_ReferenceToPersonOrEntityGeneratingTheDataSet] => Array
                        (
                            [0] => Array
                                (
                                    [Id] => f85f5371-0565-455c-a114-155dacbcb44d
                                    [LibraryId] => 7f556cf2-6175-4787-a115-9cd4bab0eb74
                                    [Name] => Pieter Callewaert
                                    [Email] => pieter@norsus.no
                                )

                        )

                    [DataEntryBy_ReferenceToPersonOrEntityEnteringTheData] => Array
                        (
                            [Id] => f85f5371-0565-455c-a114-155dacbcb44d
                            [LibraryId] => 7f556cf2-6175-4787-a115-9cd4bab0eb74
                            [Name] => Pieter Callewaert
                            [Email] => pieter@norsus.no
                        )

                    [DataEntryBy_ReferenceToDataSetUseApproval] => Array
                        (
                            [0] => Array
                                (
                                    [Id] => be34bbb0-b054-11db-abbd-0800200c9a66
                                    [LibraryId] => be34bbb0-b054-11db-abbd-0800200c9a66
                                    [Name] => No official approval by producer or operator
                                )

                        )

                    [PublicationAndOwnership_DateOfLastRevision] => 2021-10-04T08:51:16.3502085
                    [PublicationAndOwnership_CopyRight] => 1
                    [PublicationAndOwnership_ReferenceToEntitiesWithExclusiveAccess] => Array
                        (
                            [0] => Array
                                (
                                    [Id] => 8b3e08f0-740a-43b1-aa8d-bec56c5daf6e
                                    [LibraryId] => 8b3e08f0-740a-43b1-aa8d-bec56c5daf6e
                                    [Name] => FINAL USER
                                )

                        )

                    [PublicationAndOwnership_LicenceType] => Other
                    [DataEntryBy_TimeStamp] => 2021-10-04T08:51:16.3502085
                    [DataEntryBy_ReferenceToDataSetFormat] => Array
                        (
                            [0] => Array
                                (
                                    [Id] => d92a1a12-2545-49e2-a585-55c259997756
                                    [LibraryId] => d92a1a12-2545-49e2-a585-55c259997756
                                    [Name] => ILCD Data Network - Entry-level
                                )

                        )

                    [PublicationAndOwnership_ReferenceToOwnershipOfDataSet] => Array
                        (
                            [Id] => f85f5371-0565-455c-a114-155dacbcb44d
                            [LibraryId] => 7f556cf2-6175-4787-a115-9cd4bab0eb74
                            [Name] => Pieter Callewaert
                            [Email] => pieter@norsus.no
                        )

                )

            [Id] => 01c964d9-b663-4893-80a4-d8b0f120ebb7
            [CreatedBy] => f85f5371-0565-455c-a114-155dacbcb44d
            [ModifiedBy] => f85f5371-0565-455c-a114-155dacbcb44d
            [IsEditable] => 1
        )

)

Unit ID's

Array
(
    [0] => Array
        (
            [FlowInformation] => Array
                (
                    [FlowProperties] => Array
                        (
                            [0] => Array
                                (
                                    [Id] => 65afb7b5-93be-470b-8c68-f9c750db65d7
                                    [Name] => Mass
                                    [ReferenceToFlowPropertyDataSet] => Array
                                        (
                                            [Id] => 65afb7b5-93be-470b-8c68-f9c750db65d7
                                            [LibraryId] => 93a60a56-a3c8-11da-a746-0800200b9a66
                                            [Name] => Mass
                                        )

                                    [QuantityReference] => 1
                                )

                        )

                )

            [AdministrativeInformation] => Array
                (
                    [DataEntryBy_TimeStamp] => 2021-02-19T08:31:43.4584743
                    [DataEntryBy_ReferenceToDataSetFormat] => Array
                        (
                            [0] => Array
                                (
                                    [Id] => d92a1a12-2545-49e2-a585-55c259997756
                                    [LibraryId] => d92a1a12-2545-49e2-a585-55c259997756
                                    [Name] => ILCD Data Network - Entry-level
                                )

                        )

                    [PublicationAndOwnership_DataSetVersion] => 01.00.000
                    [PublicationAndOwnership_ReferenceToOwnershipOfDataSet] => Array
                        (
                            [Id] => f85f5371-0565-455c-a114-155dacbcb44d
                            [LibraryId] => 7f556cf2-6175-4787-a115-9cd4bab0eb74
                            [Name] => Pieter Callewaert
                            [Email] => pieter@norsus.no
                        )

                )

            [UnitId] => aab0228e-f2c0-4954-88c8-c9805b594a37
            [Unit] => Array
                (
                    [Id] => aab0228e-f2c0-4954-88c8-c9805b594a37
                    [LibraryId] => aab0228e-f2c0-4954-88c8-c9805b594a37
                    [Name] => kg
                )

            [Quantity] => Array
                (
                    [Id] => 48c8f2f6-81a2-4f69-947a-3401365c3008
                    [LibraryId] => 93a60a57-a4c8-11da-a746-0800200c9a66
                    [Name] => Mass
                )

            [ModelingAndValidation] => Array
                (
                    [TypeOfDataSet] => Product_flow
                )

            [Id] => d10a1ddc-48b9-476c-9677-47eaf482466a
            [CreatedBy] => f85f5371-0565-455c-a114-155dacbcb44d
            [ModifiedBy] => f85f5371-0565-455c-a114-155dacbcb44d
            [IsEditable] => 1
        )

)
		

NorEnviro er etablert i regi av NORSUS (tidligere Østfoldforskning) og NMBU og har som mål å øke bruk og gjenbruk av LCA-data. Dette gjennom å tilrettelegge for at LCA-data fra flere FOU- miljøer og bedrifter blir tilgjengeliggjort.

LCA-data blir brukt i LCA studier for å analysere miljøpåvirkninger av produkter, produktsystemer og tjenester. Dataene er strukturert på en bestemt måte slik at det kan bli brukt i LCA-software.

SØK I LCA-DATA

NorEnviro skal også være en arena som brukere og kunder av miljø- og ressursdata oppsøker for å finne frem til og få tilgang til data og kompetanse, og dermed gjøre det lettere å finne frem i jungelen av miljø- og ressursdata.

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Søk i LCA-Data