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Enregistrement W2161868054

A Life Cycle Inventory of existing biomass import chains for "green" electricity production

2003· article· en· W2161868054 sur OpenAlexaboutno aff
Kay Damen, André Faaij

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueEnvironmental Impact and Sustainability
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBiomass (ecology)SustainabilityCoalEnvironmental scienceLife-cycle assessmentElectricityLife cycle inventoryProduction (economics)Supply chainWaste managementPalm kernelBusinessAgricultural engineeringEngineeringAgricultural sciencePalm oilEconomicsMarketingAgronomyEcology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Essent Energie, a Dutch utility company, recently initiated the import of clean biomass for co-firing purposes in its coal plants. Key reasons for import are the fact that the availability of biomass with good co-firing properties in the Netherlands is limited and imported biomass can be competitive with biomass available in the Netherlands. In order to verify whether a certain biomass source meets formulated sustainability criteria, Essent Energie strives to create a certification system for biomass import. This study is set up to support the creation of such a certification system, by performing a Life Cycle Inventory (LCI) on biomass import chains. An LCI describes and quantifies the inputs (resources) and outputs (emissions) for each component of the biomass import chain, from biomass production to conversion. In doing so, the environmental performance can be determined. Objective The main objective of this study is to carry out a LCI on 2 existing biomass import chains to provide a basis for judging the overall environmental impact of biomass import and its application as fuel in coal plants to generate electricity by co-firing. Approach In this study, the import of wood pellets from Canada and palm kernel shells (PKS) from Malaysia are considered, 2 existing biomass import chains. The biomass is cofired in the Amer 9 unit, a 600 MWe coal fired power plant. For several components of the chain, case specific data provided by actual companies involved in the biomass import chain were used. If no such data were available, data from scientific publications and LCA databases were used. A mass and energy balance is set up to calculate the net avoided primary energy and the emissions of the most important greenhouse gasses, NOx, SO2, particulates and several heavy metals are quantified. The energy use and emissions related to biomass import and co-firing are compared to several reference situations for electricity/heat production (a coal plant in the Netherlands and the average Dutch fuel mix), in which the biomass fate when it would not have been used for energy purposes is accounted for as well. Also the use of biomass in the country where it is produced in stand-alone combustion systems is considered. Finally, the net avoided primary energy and emissions of biomass import and co-firing is compared to the reference systems and the use of biomass as fuel in the country where it is produced. Results As can be concluded from figure A-1 and A-2, biomass import and co-firing in coal fired plants in the Netherlands is an efficient way to reduce fossil fuel use and greenhouse gasses in comparison to power production from 100% coal or the average Dutch fuel mix. The emission of SO2, particulates (figure A-3) and heavy metals (figure A-4) of biomass co-firing are also lower in comparison to emissions caused by power production from fossil fuels. This is mainly explained by the fact that coal mining and transport to the Netherlands is an energy consuming process causing high emissions of especially CH4, SO2, particulates and heavy metals. Also the avoided emissions of CH4 caused by decomposition of wood residues at landfills in Canada and CH4, N2O, SO2 and particulate emissions caused by palm kernel shells burning in the open air in Malaysia contribute to the positive impact of biomass import and cofiring. According to the results of this study, biomass import and co-firing has some less desired impacts as well. NOx emissions (figure A-3) might increase when importing and co-firing of wood pellet. Co-firing the biomass sources considered in this study will also lead to an increase in heavy metal content of the ash, due to the high quantities of mainly Mn in both wood pellets and palm kernel shells. This could hamper the return of the ash to the country where the biomass was produced. Ash contains significant quantities of nutrients required for biomass growth, so it would be desirable to recycle the ash to the forest in Canada or to the palm oil plantations in Malaysia. net avoided primary energy

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,030
Score d'incertitude au seuil0,059

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0030,002
Études des sciences et des technologies0,0000,000
Communication savante0,0020,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0050,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,028
Tête enseignante GPT0,272
Écart entre enseignants0,244 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations26
Publié2003
Routes d'admission1
Résumé présentoui

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