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

ABSTRACT: 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 Le

SUBMITTER: Mahmoudi M 

PROVIDER: S-EPMC11480082 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature