Prediction of catalytic hydrogen production through water-gas shift reaction using soft computing approach

Abstract

Hydrogen (H2) is known as an environmentally friendly and crucial source of energy and recently demands for this compound has increased globally. One of the main ways of production of this compound is the Water-Gas Shift (WGS) reaction which utilizes various catalysts based on the operating conditions of the process. This work presents the performance of a soft computing method named PSO-RBF to estimate carbon monoxide (CO) conversion in WGS reactions. In addition, decision tree analysis was also performed to extract the associated rules which provides the highest performance for the reaction (higher conversion of CO). Different active phase compositions and support types of catalysts were utilized for development of the PSO-RBF model. The developed model accounts for the intrinsic catalyst parameters for estimation of the reaction performance by including features such as surface area, calcination time and temperature. In addition, sensitivity analysis of the model predictions was also examined to identify useful patterns. The results showed that the PSO-RBF model can accurately predict the actual CO conversion data with overall R2, AARD%, and RMSE values of 0.9977, 3.93, and 0.0159, respectively. It was also observed that the decision tree model can successfully extract the rules and trends from the experimental data. The outcomes of the sensitivity analysis study revealed that the most influential parameters for the process are temperature and H2 composition in the feed stream. This work shows the capability of soft computing methods such as the PSO-RBF and decision tree approaches for estimating better catalysts and process conditions for this crucial reaction in the environmental field.