LAPSE:2020.0094
Published Article
LAPSE:2020.0094
Review of Anaerobic Digestion Modeling and Optimization Using Nature-Inspired Techniques
Anjali Ramachandran, Rabee Rustum, Adebayo J. Adeloye
January 19, 2020
Although it is a well-researched topic, the complexity, time for process stabilization, and economic factors related to anaerobic digestion call for simulation of the process offline with the help of computer models. Nature-inspired techniques are a recently developed branch of artificial intelligence wherein knowledge is transferred from natural systems to engineered systems. For soft computing applications, nature-inspired techniques have several advantages, including scope for parallel computing, dynamic behavior, and self-organization. This paper presents a comprehensive review of such techniques and their application in anaerobic digestion modeling. We compiled and synthetized the literature on the applications of nature-inspired techniques applied to anaerobic digestion. These techniques provide a balance between diversity and speed of arrival at the optimal solution, which has stimulated their use in anaerobic digestion modeling.
Keywords
anaerobic digestion, ant colony optimization, artificial neural network, firefly algorithm, Genetic Algorithm, nature-inspired techniques, Particle Swarm Optimization
Subject
Suggested Citation
Ramachandran A, Rustum R, Adeloye AJ. Review of Anaerobic Digestion Modeling and Optimization Using Nature-Inspired Techniques. (2020). LAPSE:2020.0094
Author Affiliations
Ramachandran A: Heriot Watt University, Dubai Campus, Dubai International Academic City, Dubai 294345, UAE
Rustum R: Heriot Watt University, Dubai Campus, Dubai International Academic City, Dubai 294345, UAE [ORCID]
Adeloye AJ: School of Energy, Geoscience, Infrastructure and Society (EGIS), Heriot-Watt University, Edinburgh EH14 4AS, UK [ORCID]
Journal Name
Processes
Volume
7
Issue
12
Article Number
E953
Year
2019
Publication Date
2019-12-13
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr7120953, Publication Type: Review
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LAPSE:2020.0094
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doi:10.3390/pr7120953
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Jan 19, 2020
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CC BY 4.0
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[v1] (Original Submission)
Jan 19, 2020
 
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Jan 19, 2020
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Original Submitter
Calvin Tsay
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