DEVELOPMENT OF A MSW GASIFICATION MODEL FOR FLEXIBLE INTEGRATION INTO A MFA-LCA FRAMEWORK
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 it