Human-centric ensemble AI for hydrothermal carbonization modeling and hydrochar properties prediction
Bibliographic record
Abstract
Hydrothermal carbonization (HTC) is a promising process for biomass valorization; however, optimizing HTC conditions and characterizing hydrochar through experimental methods remain costly and time-consuming. Artificial intelligence (AI) and its related machine learning (ML) techniques provide an efficient and cost-effective alternative, enabling efficient optimization and predictive analysis without extensive experimental tests. In this study, an analysis-ready database comprising 544 data points was constructed using the authors' previous research (41) and scattered data compiled from the literature (503). An ensemble of eight diversified machine learning (ML) models was developed using biomass-agnostic properties and process conditions to predict hydrochar properties including elemental analysis, proximate analysis, and hydrochar yield. Tailor-made decision fusion models were developed for each target by merging the outputs of the best-performing models. Furthermore, a set of interpretable machine learning (IML) and explainable AI (XAI) techniques, leveraging feature importance analysis and SHapley Additive exPlanations (SHAP) values, indicated that biomass fixed carbon (FC) content and process temperature are the most influencing features, which were then considered as inputs for the ensemble models. The decision fusion models achieved high accuracy for target prediction, surpassing the currently used models in the literature, with adjusted R² values ranging from 0.98 to 1.0. For outputs that are typically difficult to predict, such as hydrochar yield, the model achieved an adjusted R² of 0.98, representing over a 5% improvement compared to the best-performing model reported in the literature. All predictions were derived from white-box modeling by incorporating XAI into the ensemble-based learning, ensuring greater model transparency and interpretability.
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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".