Hydrogen production optimization from sewage sludge supercritical gasification process using machine learning methods integrated with genetic algorithm

Hydrogen production from the supercritical water gasification (SCWG) of sewage sludge (SS) is a sustainable and efficient process. However, the challenging and intricate task for the experimental technique is to find out the correlation between proximate, ultimate analysis and gasification conditions with H-2 production. This process is complicated, expensive and requires many experimental techniques. To accurately predict and analyze the effect of input parameters on SCWG of SS process economically, an efficient model must be developed. The novelty of this study includes the consideration of four different machine learning (ML) methods integrated with Genetic Algorithm for the prediction, analysis, and evaluation of Hydrogen yield from the supercritical water gasification of sewage sludge. The ML methods included Support Vector Machine, Ensembled Tree, Gaussian Process Regression, and Artificial Neural Network. The results suggests that GPR is favored for predicting Hydrogen yield (Coefficient of determination (R-2) = 0.997, Root Mean Square Error (RMSE) = 0.093, and is highly recommended for dealing with complex variable-target correlation. On the other hand, the performance of Support Vector Machine (SVM) was poor with R-2 = 0.761 and RMSE = 2.479. The R-2 and RMSE for Ensembled Tree (ET) and Artificial Neural Network (ANN) was 0.994, 0.560 and 0.943, 1.521 respectively. The partial dependence plot shows that temperature, moisture content and pressure are among the effective parameters of SCWG. Furthermore, optimization techniques such as genetic algorithms are incorporated to optimize H-2 production by tuning the ML hyperparameters. Additionally, a Graphical User Interface was developed by utilizing the optimized GPR model for ease in computing H-2 yield. The optimum ML method integrated with GA will be beneficial for researcher to predict the H(2 )yield for the experimental work. (C) 2022 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.