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

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 …

Prediction of clean hydrogen production via biomass gasification by supervised machine
learning algorithms was studied. Lab-scale gasification studies were performed in a steel …

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 …

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 …

Biomass gasification is a promising power generation process due to its ability to utilize
waste materials and similar renewable energy sources. Predicting the outcomes of this …

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 …

The quest to attain net-zero emissions has increased the drive for more renewable energy
potential from the co-gasification of biomass. The co-gasification of coconut shell and oil …

The thermochemical processes such as gasification and co-gasification of biomass and coal
are promising route for producing hydrogen-rich syngas. However, the process is …

Co-gasification of biomass and municipal solid waste (MSW) exhibits synergistic effects by
improving the quality of syngas while reducing environmental pollution from MSW. In this …