Applying machine learning to predict torrefaction and pyrolysis activation energy based on biomass characteristics and heating conditions
Bibliographic record
Abstract
Pyrolysis of biomass is a complex process involving many reactions and components. Many experiments have been carried out to determine the kinetics of pyrolysis and low-temperature torrefaction. In this paper, the multiple linear regression and random forest regression methods were used to predict the activation energy of biomass pyrolysis and torrefaction as determined by Kissinger-Akahira-Sunose (KAS) and Flynn-Wall-Ozawa (FWO) methods. The input variables were biomass characteristics, heating conditions and conversion. The hyper-parameters of the model were optimized by grid search and 5-fold cross validation. In all cases performed in this study, the prediction accuracy of the random forest models was found to be acceptable with R 2 > 0.83 and RMSE < 0.04. The contribution of each variable to the activation energy was analyzed by Pearson analysis, feature importance analysis and partial dependence analysis. This study recommended that machine learning can be applied for predicting kinetic parameters of the thermochemical process, providing a new tool for understanding, simulating and evaluating biomass pyrolysis and torrefaction processes. • Activation energy of biomass pyrolysis were predicted by machine learning. • Random forest method gave a better performance than linear regression method. • Effects of input variables on activation energy were analyzed. • Pyrolysis activation energy was mostly affected by conversion and fixed carbon. • Torrefaction activation energy was mostly affected by carbon and moisture contents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".