Data-Driven Machine Learning Applications for Biofuel Production and Environmental Remediation
A special issue of Environments (ISSN 2076-3298).
Deadline for manuscript submissions: closed (1 December 2023) | Viewed by 503
Special Issue Editor
Special Issue Information
Dear Colleagues,
Data-driven machine learning is becoming increasingly relevant in several research areas. It can be adopted for the optimization of biofuel production processes and the screening of suitable materials for environmental remediation. Machine learning can be used to analyze large amounts of data and make predictions or decisions based on these data. In both energy production and environmental remediation, machine learning can be used to improve the efficiency and effectiveness of these processes. By analyzing large amounts of data, machine learning can identify patterns and make predictions that can be used to optimize performance and reduce costs. Additionally, machine learning can be used to identify new opportunities for innovation and improve the design of new technologies. This Special Issue will focus on different studies that apply data-driven machine learning for biofuel production and environmental remediation. Studies related to catalyst screening with machine learning, thermochemical or biological process optimization, prediction of materials performance with machine learning, as well as the application of interpretable machine learning for studying material properties are welcomed.
Dr. Jude A. Okolie
Guest Editor
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Keywords
- machine learning
- artificial intelligence
- thermochemical processes
- biological processes
- biochar
- wastewater
- emerging pollutants
- biogas
- hydrogen
- biofuels
- bioenergy
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