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.
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All Science Journal Classification (ASJC) codes
- Chemical Engineering(all)
- Industrial and Manufacturing Engineering