Predictive modeling of membrane reactor efficiency using advanced artificial neural networks for green hydrogen production

• Published in: Scientific reports Vol. 14; no. 1; pp. 24211 - 16
• Main Authors: Mahmoudi, Mehrdad, Ghaemi, Ahad, Rahbar Kelishami, Ahmad, Movahedirad, Salman
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 16.10.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/898; 639/638/675; Algorithms; Artificial neural network; Carbon sources; Clean energy; Correlation coefficient; Efficiency; Green hydrogen; Humanities and Social Sciences; Hydrogen; Hydrogen production; Machine learning; Membrane Reactor; Membrane reactors; multidisciplinary; Neural networks; Reactors; Science; Science (multidisciplinary); Statistical analysis; Statistical models; Sustainable production; Membrane Reactor; Artificial neural network; Hydrogen production; Machine learning
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-75068-y

The imperative to decarbonize the energy sector has prompted substantial advancements in clean electricity generation, with hydrogen emerging as a promising low-carbon energy carrier. While hydrogen synthesis from renewable sources is crucial, challenges persist, necessitating innovative approaches for efficient and sustainable production. This study leverages diverse artificial neural network (ANN) models to assess and predict system efficiency based on key operational variables in membrane reactor systems. The multilayered perceptron (MLP) and radial basis function (RBF) methodologies are employed, with the MLP models optimized across twelve training algorithms and eight activation functions, exploring up to three hidden layers with variable neuron counts. The MLP model, utilizing the Levenberg-Marquard training algorithm and Tangent-Sigmoid activation function, achieved a high correlation coefficient (R 2 ) of 0.9975 for training and 0.9962 for testing, and a mean squared error (MSE) of 0.00425 for training and 0.23951 for testing, indicating precise and accurate efficiency predictions. The Log-Sigmoid activation function also performed well, with R² values of 0.9971 (training) and 0.9961 (testing), and MSE values of 0.004086 (training) and 0.17694 (testing). Optimization of the RBF network identified the best performance with a spread parameter of 1 and 35 neurons, although the MLP model demonstrated superior accuracy and reduced computational time. Statistical analysis, encompassing correlation coefficient, mean squared error, Root Mean Squared error, absolute average deviation, absolute average relative deviation, and runtime, confirms the network’s consistent and accurate estimation of system efficiency across various input variables. The study highlights that applying tansig and logsig activation functions, configured with neuron counts of 20, 17, 6 and 23, 20, 2 at the first, second and third hidden layers, respectively, offers enhanced accuracy and reliability. The MLP model’s high performance underscores its potential to identify optimal conditions for H 2 generation based on system efficiency, thereby advancing membrane reactor technology for hydrogen production.