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Modeling supercritical water gasification of municipal waste: Machine learning and data augmentation approaches
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DOI：10.1016/j.supflu.2025.106872.png)
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Abstract
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This study presents a comprehensive machine learning framework for modeling supercritical water gasification (SCWG) of organic fraction of municipal solid waste (OFMSW) to optimize syngas production. A systematic experimental campaign investigated 16 conditions across 400-450 degrees C, 10-60 min reaction times, and OFMSW/H2O ratios of 0.050-0.085, yielding gas compositions with H-2 concentrations of 12-44 %, CO2 of 24-79 %, CO of 2.8-15.7 %, and CH4 of 0-22 %. To address limited experimental data challenges, a SCWG-tailored Monte Carlo augmentation with compositional closure, measurement-uncertainty modeling, and leakage-free cross-validation was implemented to address tiny-n multi-output composition prediction, incorporating experimentally characterized uncertainties and generating augmented datasets of 10 x and 100 x the original size while enforcing physical feasibility (non-negativity, unit-sum, and range bounds). Random Forest (RF) and Gradient Boosting (GB) algorithms were employed for multi-output modeling of gas yield and composition. Under physically constrained augmentation and cross-validation, high in-domain accuracy is achieved across the explored operating window with R-2 values increasing from 0.445 +/- 0.327 (RF) and 0.549 +/- 0.324 (GB) on original data to > 0.96 for major components on 100 x augmented datasets. Process optimization identified distinct optimal conditions: hydrogen fuel production (450 degrees C, 16 min, yielding 39.4 % H-2 with 77.6 % gas yield) and Fischer-Tropsch synthesis (450 degrees C, 10 min, achieving H-2/CO ratio of 2.13 with 63.6 % gas yield). The framework successfully bridges limited experimental data and reliable process optimization, providing validated methodology for advancing SCWG technology toward industrial implementation. Independent validation on new waste batches and configurations will be required to establish broader generalization.
Keywords:
Supercritical water gasification
Machine learning
Data augmentation
Municipal solid waste
Syngas optimization
Waste valorization
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