Retrofitting coal‐fired power plants with biomass co‐firing and carbon capture and storage for net zero carbon emission: A plant‐by‐plant assessment framework
Bibliographic record
Abstract
Abstract The targets of limiting global warming levels below 2°C or even 1.5°C set by Paris Agreement heavily rely on bioenergy with carbon capture and storage (BECCS), which can remove carbon dioxide in the atmosphere and achieve net zero greenhouse gas (GHG) emission. Biomass and coal co‐firing with CCS is one of BECCS technologies, as well as a pathway to achieve low carbon transformation and upgrading through retrofitting coal power plants. However, few studies have considered co‐firing ratio of biomass to coal based on each specific coal power plant's characteristic information such as location, installed capacity, resources allocation, and logistic transportation. Therefore, there is a need to understand whether it is worth retrofitting any individual coal power plant for the benefit of GHG emission reduction. It is also important to understand which power plant is suitable for retrofit and the associated co‐firing ratio. In order to fulfill this gap, this paper develops a framework to solve these questions, which mainly include three steps. First, biomass resources are assessed at 1 km spatial resolution with the help of the Geography Information Science method. Second, by setting biomass collection points and linear program model, resource allocation and supply chain for each power plants are complete. Third, is by assessing the life‐cycle emission for each power plant. In this study, Hubei Province in China is taken as the research area and study case. The main conclusions are as follows: (a) biomass co‐firing ratio for each CCS coal power plant to achieve carbon neutral is between 40% and 50%; (b) lower co‐firing ratio sometimes may obtain better carbon emission reduction benefits; (c) even the same installed capacity power plants should consider differentiated retrofit strategy according to their own characteristic. Based on the results and analysis above, retrofit suggestions for each power plant are made in the discussion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".