This study aimed to investigate the application of radial basis function (RBF) and multilayer
perceptron (MLP) artificial neural networks for modeling hydrogen production by co …

The main aim of this study is subject of thermochemical conversion process data into
computational modelling. Especially, prediction of hydrogen gas from the pyrolysis of waste …

This study relates to predicting hydrogen production using deep learning models. The co-
gasification of biomass and plastics dataset used gasification temperature, particle size of …

Hydrogen production from co-gasification of biomass and plastics are predicted using
Machine Learning Algorithms, eg, Decision tree and Ensemble methods. Independent …

An artificial neural network (ANN) is a biologically inspired computational technique that
imitates the behavior and learning process of the human brain. In this study, ANN technique …

Quality syngas production with higher moles of hydrogen and methane are the primary
objective of gasification process which is dependent upon the process parameters and …

Artificial neural networks (ANNs) and genetic algorithms (GA) are considered among the
latest tools that are used to solve complicated problems that cannot be solved by …

In order to accurately anticipate the proficiency of downdraft biomass gasification linked with
a water–gas shift unit to produce biohydrogen, a model based on an artificial neural network …

In this study, a robust model using bootstrapped aggregated neural network (BANN) was
developed for optimising operating conditions of a two-stage gasification for high carbon …

Energy generation from renewable and carbon-neutral biomass is significant in the context
of a sustainable energy framework. Hydrogen can be conveniently extracted from biomass …