Estimation of catalytic hydrogen production through water-gas shift reaction using a machine learning technique

Abstract

ABSTRACT Hydrogen (H2) is an important and environmentally friendly energy source and the need for its usage is growing across the world. Water-Gas Shift (WGS) reaction is the key approach for hydrogen production which uses different catalysts with respect to conditions at which the reaction occurs. This research examines the applicability of a machine learning technique called Least Square Support Vector Machine (LSSVM) to predict the conversion of carbon monoxide (CO) in WGS reactions according to various compositions for active phase and different kinds of support compounds for catalysts. The implemented method considers the intrinsic catalyst variables to predict the performance of the reaction by using variables such as surface area, calcination time and temperature. The outcomes indicate that the LSSVM approach can precisely estimate the real CO conversion information with overall R2, AARD%, and RMSE values of 0.9996, 2.55, and 0.0069, respectively. This research shows the reliable performance of machine learning approaches such as the LSSVM technique for predicting better catalysts and process conditions for this important reaction which is of great importance in researches related to the environmental field.