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Pyrolysis

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Artificial neural networks for the prediction of biochar yield: A comparative study of metaheuristic algorithms.

Bioresource technology
In this study, an integrated framework of artificial neural networks (ANNs) and metaheuristic algorithms have been developed for the prediction of biochar yield using biomass characteristics and pyrolysis process conditions. Comparative analysis of s...

RETRACTED: Chemistry-Informed Neural Networks modelling of lignocellulosic biomass pyrolysis.

Bioresource technology
This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the authors and the Editor-in-Chief. The article has reused text fr...

Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction.

Bioresource technology
In this study, Machine learning (ML) models integrated with genetic algorithm (GA) and particle swarm optimization (PSO) have been developed to predict, evaluate, and analyze biochar yield using biomass properties and process operating conditions. Co...

Prediction model for biochar energy potential based on biomass properties and pyrolysis conditions derived from rough set machine learning.

Environmental technology
Biochar is a high-carbon-content organic compound that has potential applications in the field of energy storage and conversion. It can be produced from a variety of biomass feedstocks such as plant-based, animal-based, and municipal waste at differe...

Prediction of NH and HCN yield from biomass fast pyrolysis: Machine learning modeling and evaluation.

The Science of the total environment
Rapid pyrolysis is a promising technique to convert biomass into fuel oil, where NO emission remains a substantial environmental risk. NH and HCN are top precursors for NO emission. In order to clarify their migration path and provide appropriate str...

Optimization of diesel engine performance and emission using waste plastic pyrolytic oil by ANN and its thermo-economic assessment.

Environmental science and pollution research international
The current study focuses on the engine performance and emission analysis of a 4-stroke compression ignition engine powered by waste plastic oil (WPO) obtained by the catalytic pyrolysis of medical plastic wastes. This is followed by their optimizati...

Machine learning prediction of contents of oxygenated components in bio-oil using extreme gradient boosting method under different pyrolysis conditions.

Bioresource technology
This work aims to develop a prediction model for the contents of oxygenated components in bio-oil based on machine learning according to different pyrolysis conditions and biomass characteristics. The prediction model was constructed using the extrem...

Understanding cellulose pyrolysis via ab initio deep learning potential field.

Bioresource technology
Comprehensive and dynamic studies of cellulose pyrolysis reaction mechanisms are crucial in designing experiments and processes with enhanced safety, efficiency, and sustainability. The details of the pyrolysis mechanism are not readily available fro...

Thermogravimetric experiments based prediction of biomass pyrolysis behavior: A comparison of typical machine learning regression models in Scikit-learn.

Marine pollution bulletin
A variety of machine learning (ML) models have been extensively utilized in predicting biomass pyrolysis owing to their prowess in deciphering complex non-linear relationships between inputs and outputs, but there is still a lack of consensus on the ...

Forecasting vaping health risks through neural network model prediction of flavour pyrolysis reactions.

Scientific reports
Vaping involves the heating of chemical solutions (e-liquids) to high temperatures prior to lung inhalation. A risk exists that these chemicals undergo thermal decomposition to new chemical entities, the composition and health implications of which a...