Machine learning prediction of hydrochar properties using ash-based biomass classification with optimized models and interpretability analysis
Bibliographic record
Abstract
The growing demand for sustainable energy solutions has intensified research on biomass valorization into carbon rich hydrochar via hydrothermal carbonization (HTC). However, optimizing hydrochar fuel properties (Fuel Ratio, H/C, O/C) remains challenging due to the complex interplay of feedstock composition and process conditions. This study presents a machine learning (ML) driven approach to predict hydrochar properties by categorizing biomass into low, medium and high ash content groups. An integrated framework combining different ML models and metaheuristic optimization techniques was developed to establish robust predictive capabilities. The results show that the GPR model achieved a testing R 2 > 0.72 and RMSE < 0.1365 across all ash categories. Feature selection highlighted temperature, time, and biomass composition (Carbon, Oxygen, Volatile Matter, Fixed Carbon) as important predictors, demonstrating the effectiveness of ML with hyperparameter optimization for accurate hydrochar properties prediction. Partial dependence plots further explained nonlinear relationships between inputs and outputs. A user-friendly Graphical User Interface was developed, enabling real-time hydrochar properties prediction, closely aligning with experimental data. This work demonstrates that ML models, coupled with optimization techniques, can effectively guide HTC process optimization for tailored hydrochar production, bridging the gap between data-driven insights and practical applications in renewable 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 | 0.000 | 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 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".