Data-driven interpretation, comparison and optimization of hydrogen production from supercritical water gasification of biomass and polymer waste: Applying ensemble and differential evolution in machine learning algorithms

Supercritical water gasification (SCWG) has been utilized for producing hydrogen from organic wastes, which is associated with sustainable development. Nevertheless, identifying the optimal SCWG conditions and appropriate catalysts for diverse waste materials to yield hydrogen-rich syngas is consistently a laborious and costly endeavor. The current research presents a comprehensive framework that combines machine learning models (Decision Tree, Ensembled Learning Tree, Support Vector Machine, and Gradient Boost Regression) with Differential Evolution Optimization (DEO) to predict and optimize hydrogen production from Supercritical Water Gasification (SCWG) using 24 different biomass characteristics and process parameters.The findings indicate that ELA-DEO emerges as the preferred method for forecasting hydrogen yield (R-test(2) = 0.95, RMSETest = 0.091), demonstrating its effectiveness in handling intricate variable-target relationships. Conversely, Support Vector Machine (SVM) displayed weaker performance with an R-Test(2) of 0.73 and RMSETest of 0.116. In the SHAP feature importance analysis, temperature, catalyst amount, catalyst type, hydrogen and oxygen were highlighted as important parameters affecting the process. In addition, by fine-tuning the machine learning hyperparameters, the DEO optimization method was used to maximize the production of H-2. The optimized ELA-DEO model was used to identify the ideal features and conditions applicable to our experimental setup. Based on these findings, a recommended biomass table was finally formulated, which was validated by the ELA-DEO model. According to our laboratory results, Dunaliella salina produced 12 mmol of hydrogen with a 35% selectivity. Black liquor with 3% (wt) wood has the capacity to produce 17.21 mmol of hydrogen. Also, when crude glycerol was reacted with RuCl3, 18.7 mmol of hydrogen were generated. However, Dunaliella salina produced a 64% mole fraction of total gas output, which matched the maximum value predicted by our ELA-DEO models.