A Life Cycle Inventory of existing biomass import chains for "green" electricity production
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".