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

Abstract

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 H2 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 (R2) = 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 R2 = 0.761 and RMSE = 2.479. The R2 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 H2 production by tuning the ML hyperparameters. Additionally, a Graphical User Interface was developed by utilizing the optimized GPR model for ease in computing H2 yield.The optimum ML method integrated with GA will be beneficial for researcher to predict the H2 yield for the experimental work.