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 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 …

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 …

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

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 …

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 …

Hydrogen energy derived through biomass gasification is considered as one of the most
sorted sustainable sources of renewable energy. This process enhances the H 2 production …

In the context of Industry 4.0, hydrogen gas is becoming more significant to energy
feedstocks in the world. The current work researches a novel artificial smart model for …

Gasification is a highly promising thermochemical process that shows considerable potential
for the efficient conversion of waste biomass into syngas. The assessment of the feasibility …