Prediction of Catalytic Hydrogen Generation by Water–Gas Shift Reaction Using a Neural Network Approach

Hydrogen (H₂) is an environmentally-safe power source and its demands is continuously growing worldwide. The most important approach for its generation is water–gas shift (WGS) reaction through various catalysts. This work investigates feasibility of neural network method named Multilayer Perceptron Neural Network (MLP-NN) to estimate CO conversion in WGS reactions based on different active phase compositions and various supports. The approach considers the intrinsic parameters of the catalyst to estimate reaction performance. This research investigates the most influential variables by conducting a sensitivity analysis study on the predictions of the implemented method. The results of the modeling study revealed that the MLP-NN method can accurately approximate the experimental CO conversion values. The sensitivity analysis study revealed temperature and H₂ feed concentration are the most crucial parameters on the reaction performance. The reliability of neural network methods is proved such as the MLP-NN to accurately estimate the CO conversion values in WGS reaction. Graphical Abstract: [Figure not available: see fulltext.]