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

Gasification technology often requires the use of modeling approaches to incorporate
several intermediate reactions in a complex nature. These traditional models are …

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 …

Recently, artificial intelligence models have been developed to simulate the biomass
gasification systems. The extant research models use different input features, such as …

Artificial neural networks and artificial intelligence based regression techniques have been
recently applied to various gasification processes. Although these techniques obtain …

Over the past two decades, the use of machine learning (ML) methods to model biomass
and waste gasification/pyrolysis has increased rapidly. Only 70 papers were published in …

Abstract Machine learning has been regarded as a promising method to better model
thermochemical processes such as gasification. However, their black box nature can limit …

Gasification of organic waste represents one of the most effective valorization pathways for
renewable energy and resources recovery, while this process can be affected by multi …

This study aimed to utilize machine learning algorithems combined with feature reduction for
predicting pyrolytic gas yield and compositions based on pyrolysis conditions and biomass …

Abstract Machine learning methods have recently shown a broad application prospect in
biomass gasification modeling. However, a significant drawback of the machine learning …