Performance analysis and artificial intelligence modeling for enhanced hydrogen production by catalytic bio-alcohol reforming in a membrane-assisted reactor

Due to the significance of green processing and artificial intelligence (AI) modeling, herein, we proposed a mathematical and an AI model to enhance green hydrogen production from bio-alcohols in a membrane -assisted reactor. A sensitivity analysis for the effective variables was conducted in terms of conversion, thermal behavior, pressure drop, and hydrogen distribution. A single-layer perceptron network with tan-sig + trainlm functions and eight neurons yielded a 0.72 % error (mean squared error (MSE) = 2.69, R2= 0.99994) in the bio-methanol reformer, while the same functions with nine neurons presented a 0.22 % error (MSE = 0.32, R2 = 1.00000) in the bio-ethanol reformer. Lastly, a multi-objective optimiza-tion was performed to determine the optimum operating conditions, which enhanced the hydrogen pro-duction in the bio-methanol (yMeOH = 0.4 and 0.204 mol/h at 517 K and 6 bar) and bio-ethanol (yEtOH = 0.3 and 1.8 mol/h at 823 K and 6 bar) reforming.(c) 2022 Published by Elsevier Ltd.

Automated summary

In this study, a mathematical and an AI model were proposed to enhance green hydrogen production from bio-alcohols in a membrane-assisted reactor. A sensitivity analysis was conducted for effective variables, and a single-layer perceptron network with specific functions and neurons achieved low errors in bio-methanol and bio-ethanol reformers. Multi-objective optimization was performed to determine optimal operating conditions, resulting in improved hydrogen production.