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 …

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 …

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 …

The world is actively looking for alternate energy sources to curb the growth of greenhouse
gas emissions produced by fossil fuel consumption. Amongst the various alternative energy …

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

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 …

Biomass can replace fossil fuels, reduce greenhouse gas emissions, and is a promising
renewable energy source. Co-production of multiple-products has been demonstrated …

Biomass is an excellent source of green energy with numerous benefits such as abundant
availability, net carbon zero, and renewable nature. However, the conventional methods of …

This research presents an innovative predictive modeling approach for estimating Hydrogen
and Nitrogen quantities in gasification processes, vital for converting carbonaceous …

A two-stage gasification has been proven as an effective and robust approach for converting
low-valued and/or highly heterogeneous materials ie waste, into hydrogen and/or syngas …