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

Research

Title	Performance analysis and artificial intelligence modeling for enhanced hydrogen production by catalytic bio-alcohol reforming in a membrane-assisted reactor
Type	Article
Keywords	artificial intelligence, hydrogen production, catalytic reforming, bio-alcohol
Journal	CHEMICAL ENGINEERING SCIENCE
DOI	https://doi.org/10.1016/j.ces.2022.118432
Researchers	َAli Bakhtyari (First researcher) , Roghayeh Bardool (Second researcher) , Mohammad Reza Rahimpour (Third researcher) , Masoud Mofarahi (Fourth researcher) , Chang_Ha Lee (Fifth researcher)

Abstract

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 tansig + 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 optimization was performed to determine the optimum operating conditions, which enhanced the hydrogen production 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.