Artificial Intelligence Application in Bioethanol Production

Energy consumption from biofuels against fossil fuels over the past few years has increased. This is due to the availability of these resources for production of different forms of energy, and the environmental benefit in the utilization of these resources. Ethanol fuel production from biomass is a...

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Published in:International journal of energy research 2023-10, Vol.2023, p.1-8
Main Authors: Owusu, Winnie A., Marfo, Solomon A.
Format: Article
Language:English
Subjects:
Agricultural production
Algorithms
Alternative energy sources
Artificial intelligence
Artificial neural networks
Bacteria
Biodiesel fuels
Bioethanol
Biofuels
Biomass
Classification
Datasets
Design optimization
Efficiency
Energy consumption
Enzymes
Ethanol
Fermentation
Fossil fuels
Fuel production
Gasoline
Handling
Machine learning
Microorganisms
Modelling
Neural networks
Raw materials
Renewable resources
Statistical analysis
Sugarcane
Sustainability
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Marfo, Solomon A.
description Energy consumption from biofuels against fossil fuels over the past few years has increased. This is due to the availability of these resources for production of different forms of energy, and the environmental benefit in the utilization of these resources. Ethanol fuel production from biomass is a complex process of known challenges in the area of handling, optimizing, and future forecasting. The existence of modelling techniques like artificial intelligence (AI) is, therefore, necessary in the design, handling, and optimization of bioethanol production. The flexibility and high accuracy of artificial neural network (ANN), a machine learning technique, to solve intricate processes is beneficial in modelling pretreatment, fermentation, and conversion stages of a bioethanol production system. This paper reviews various AI techniques in bioethanol production giving emphasis on published articles in the past decade.
doi_str_mv 10.1155/2023/7844835
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subjects Agricultural production
Algorithms
Alternative energy sources
Artificial intelligence
Artificial neural networks
Bacteria
Biodiesel fuels
Bioethanol
Biofuels
Biomass
Classification
Datasets
Design optimization
Efficiency
Energy consumption
Enzymes
Ethanol
Fermentation
Fossil fuels
Fuel production
Gasoline
Handling
Machine learning
Microorganisms
Modelling
Neural networks
Raw materials
Renewable resources
Statistical analysis
Sugarcane
Sustainability
title Artificial Intelligence Application in Bioethanol Production
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