DEVELOPMENT OF A MSW GASIFICATION MODEL FOR FLEXIBLE INTEGRATION INTO A MFA-LCA FRAMEWORK
Bibliographic record
Abstract
This paper presents the development of a comprehensive gasification module designed to be integrated in a MFA-LCA framework. From existing gasification models present in the literature, the most appropriate modelling strategy is selected and implemented into the module. This module needs to be able to capture the influence of input parameters, such as gasification reactor type, oxidizing agent, feedstock composition and operating conditions on the process outputs, including syngas yield, its composition and LHV, as well as tar and char contents. A typical gasification process is usually modelled in four steps: drying, pyrolysis, oxidation and reduction. Models representing each of these steps are presented in this paper. Since the type of gasification reactor is taken into account in the module, models for downdraft moving bed and bubbling fluidized bed reactor are also reviewed. The gasification module will be integrated into a MFA framework (VMR-Sys), which enables calculation of relevant gasifier feedstock parameters, such as moisture content, composition, properties and particle size distribution. Outputs from the module will also include elemental compositions obtained from VMR-Sys calculations. Finally, all outputs from the module will be used to build LCA-inventory data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".