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Crow Search Algorithm for Modelling an Anaerobic Digestion Process: Algorithm Parameter Influence

Author

Listed:
  • Olympia Roeva

    (Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 105, 1113 Sofia, Bulgaria)

  • Gergana Roeva

    (Department of Mechatronic Bio/Technological Systems, Institute of Robotics, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Block 2, 1113 Sofia, Bulgaria)

  • Elena Chorukova

    (Department Biotechnology—Bioremediation and Biofuels, The Stephan Angeloff Institute of Microbiology, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 26, 1113 Sofia, Bulgaria)

Abstract

Corn steep liquor is a waste product from the process of treating corn grain for starch extraction. It is used as a substrate in anaerobic digestion with simultaneous hydrogen and methane production in a cascade of two anaerobic bioreactors. For process research and optimisation, adequate mathematical models are required. So, the authors aim to present a high-quality model of the corn steep liquor process for the sequential production of H 2 and CH 4 . This paper proposes a technique for identifying the best mathematical model of the process using the metaheuristics crow search algorithm (CSA). The CSA was applied for the first time to mathematical modelling of the considered two-stage anaerobic digestion process, using real experimental data. Based on the analysis of the numerical data from the model parameter identification procedures, the influence of the main CSA parameters—the flight length, fl , and the awareness probability, AP —was investigated. Applying classical statistical tests and an innovative approach, InterCriteria Analysis, recommendations about the optimal CSA parameter tuning were proposed. The best CSA algorithm performance was achieved for the AP = 0.05, fl = 3.0, followed by AP = 0.10, fl = 2.5, and AP = 0.15, fl = 3.0. The optimal tuning of the CSA parameters resulted in a 29% improvement in solution accuracy. As a result, a mathematical model of the considered two-stage anaerobic digestion process with a high degree of accuracy was developed.

Suggested Citation

  • Olympia Roeva & Gergana Roeva & Elena Chorukova, 2024. "Crow Search Algorithm for Modelling an Anaerobic Digestion Process: Algorithm Parameter Influence," Mathematics, MDPI, vol. 12(15), pages 1-20, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:15:p:2317-:d:1441964
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    References listed on IDEAS

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    1. Nebojsa Bacanin & Catalin Stoean & Miodrag Zivkovic & Miomir Rakic & Roma Strulak-Wójcikiewicz & Ruxandra Stoean, 2023. "On the Benefits of Using Metaheuristics in the Hyperparameter Tuning of Deep Learning Models for Energy Load Forecasting," Energies, MDPI, vol. 16(3), pages 1-21, February.
    2. Tong, Charles, 2006. "Refinement strategies for stratified sampling methods," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1257-1265.
    3. Qian Cheng & Huajuan Huang & Minbo Chen, 2021. "A Novel Crow Search Algorithm Based on Improved Flower Pollination," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-26, October.
    4. Olympia Roeva & Dafina Zoteva & Gergana Roeva & Velislava Lyubenova, 2023. "An Efficient Hybrid of an Ant Lion Optimizer and Genetic Algorithm for a Model Parameter Identification Problem," Mathematics, MDPI, vol. 11(6), pages 1-22, March.
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