Multi-objective optimal control of Docosahexaenoic Acid (DHA) production in fed-batch fermentation by Schizochytrium sp. (October 2022)

• Record Type: Journal Article
• Authors: Rohman, F.S.; Roslan, M.F.; Muhammad, D.; Shoparwe, N.F.; Hamid, A.A.
• Is Part Of: Biocatalysis and agricultural biotechnology. Number 45(2022)
• Publication Date: 2022-10
• Subjects: Docosahexaenoic acid -- Bioreactor -- Fermentation -- Optimal control -- Hybrid strategy -- Control vector parameterization -- Multi-objective optimi -- z -- ation; Agricultural biotechnology -- Periodicals; Enzymes -- Biotechnology -- Periodicals; 660.6
• DOI: 10.1016/j.bcab.2022.102490
• Languages: English
• ISSNs: 1878-8181

Abstract

Abstract: Docosahexaenoic Acid (DHA) production from fermentation process in a fed-batch reactor is suited solved using optimal control to obtain optimal temperature and feed flowrate trajectories. This fermentation process, which involves numerous conflicting objectives, necessitates the solution of a multi-objective optimal control (MOOC). MOOC results, which include a variety of ideal solutions, are configured as Pareto Front (PF). The ε-constraint with hybrid strategy (HS), and elitist non-dominated sorting genetic algorithm (NSGA-II) have been implemented to tackle conflict bi-objectives: minimisation final time and maximizing DHA production. By computing performance measurements such as space (SP), hypervolume (HV), and pure diversity (PD), these MOOC techniques were compared to the characteristic of the Pareto solution. Due to the most precise, diverse, and desirable spread points along the PF, the ε-constraint technique is the most effective. Each Pareto solution point comprises a unique combination of optimal feed flowrate trajectories, resulting in a unique amount of final time and DHA concentration. These solutions provide a variety of options for evaluating trade-offs and establishing the best operating strategy. Highlights: The production of DHA with Schizochytrium sp. was carried out in fed-batch fermentation. The feeding strategy was optimized numerically. Pareto Front was generated by genetic algorithm and ε-constraint with hybrid strategy. The control discretization approach was used for optimal control. The shortest time and maximum DHA can be obtained.