Hydrogen production from the steam gasification of hydrochar: A multi-output machine learning approach integrated with metaheuristic algorithms
Bibliographic record
Abstract
Steam gasification of hydrochar requires high-temperature reactors, making the process costly and time-consuming. This study proposes soft computing approaches as efficient alternatives for predicting multiple output (syngas concentration, H 2 /CO ratio and heating value based on hydrochar composition and steam gasification operating conditions. Four Machine learning (ML) models enhanced with metaheuristic optimization techniques (Genetic Algorithms (GA) and Particle Swarm Optimization (PSO)) were trained on experimental data to predict syngas composition, H 2 /CO and higher heating value. Among the models, Gaussian Process Regression (GPR-GA (Coefficient of Determination, R 2 = 0.83, root mean square error, RMSE = 4.19) and GPR-PSO (R 2 = 0.82, RMSE = 4.3)) showed superior performance for hydrogen concentration prediction accuracy. Two-way Partial dependence plots and SHAP show that Steam to biomass ratio (S/B) and higher heating value has a significant effect on hydrogen concentration. Moreover, the optimal steam to biomass ratio and temperature should be in the range of 2.5–3 and 750–850 °C for highest predicted hydrogen concentration. To facilitate practical applications, graphical user interface (GUI) was developed using the best-performing ML model. GUI allows users to predict syngas compositions in real time and eliminates the need for extensive experimentation, providing a user-friendly platform to optimize syngas production while ensuring efficiency and reliability. • ML models optimized for accurate syngas composition prediction and analysis. • H 2 concentration increases with carbon, steam-to-biomass ratio, and temperature. • GPR-GA and GPR-PSO models outperformed others in hydrogen prediction accuracy. • Developed a Graphical user interface for real-time syngas composition predictions.
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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 | 0.001 | 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".