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 …

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 …

Hydrogen production for clean energy is gaining a foothold, notably through the gasification
of biomass. Machine learning aids in its accurate production predictions, yet its opaque …

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 application of machine learning (ML) in the prediction of hydrogen (H2) production has
proven to be an efficient tool for enhancing production capacity. However, while the reported …

Gasification, a highly efficient method, is under extensive investigation due to its potential to
convert biomass and plastic waste into eco-friendly energy sources and valuable fuels …

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 …

This project aims to predict biomass gasification products using machine learning algorithms
and evaluate their performance. Four algorithms, namely Decision Tree, Random Forest, K …

This study assesses renewable hydrogen production via gasification of residual biomass,
using artificial neural networks (ANNs) for predictive modeling. The process uses residues …