Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm

Zain, Mohamad Zihin bin Mohd, Kanesan, Jeevan, Kendall, G. and Chuah, Joon Huang (2017) Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm. Expert Systems with Applications, 91 . pp. 286-297. ISSN 0957-4174

Full text not available from this repository.


Fed-batch fermentation has gained attention in recent years due to its beneficial impact in the economy and productivity of bioprocesses. However, the complexity of these processes requires an expert system that involves swarm intelligence-based metaheuristics such as Artificial Algae Algorithm (AAA), Artificial Bee Colony (ABC), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Differential Evolution (DE) for simulation and optimization of the feeding trajectories. DE traditionally performs better than other evolutionary algorithms and swarm intelligence techniques in optimization of fed-batch fermentation. In this work, an improved version of DE namely Backtracking Search Algorithm (BSA) has edged DE and other recent metaheuristics to emerge as superior optimization method. This is shown by the results obtained by comparing the performance of BSA, DE, CMAES, AAA and ABC in solving six fed batch fermentation case studies. BSA gave the best overall performance by showing improved solutions and more robust convergence in comparison with various metaheuristics used in this work. Also, there is a gap in the study of fed-batch application of wastewater and sewage sludge treatment. Thus, the fed batch fermentation problems in winery wastewater treatment and biogas generation from sewage sludge are investigated and reformulated for optimization.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/878878
Keywords: Fed-batch fermentation; Backtracking Search Algorithm; Evolutionary algorithms; Wastewater treatment; Feeding trajectory optimization; Sewage sludge
Schools/Departments: University of Nottingham, Malaysia > Faculty of Science and Engineering — Science > School of Computer Science
University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1016/j.eswa.2017.07.034
Depositing User: Kendall, Graham
Date Deposited: 05 Feb 2018 10:49
Last Modified: 04 May 2020 19:02
URI: https://eprints.nottingham.ac.uk/id/eprint/49536

Actions (Archive Staff Only)

Edit View Edit View