期刊
APPLIED MATHEMATICAL MODELLING
卷 108, 期 -, 页码 205-219出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.apm.2022.03.026
关键词
Batch culture; Environmental disturbance; Biological robustness; Multi-objective optimization; Fuzzy approximation
资金
- National Natural Science Foundation of China [11771008]
- Natural Science Foundation of Shandong Province, China [ZR2015FM014]
- Postdoctoral Science Foundation of China [2015M572061]
- Postdoctoral Science Foundation of Qingdao, China
This paper investigates the mathematical modeling and parameter identification of glycerol bioconversion to 1,3-Propanediol in batch fermentation. A stochastic differential system with disturbance is proposed to capture the genetic stochasticity and environmental disturbances in the biochemical system. A multi-objective parameter identification model is formulated to ensure model refinement and biological robustness. The optimization problems are solved using linear matrix inequality techniques. Numerical results demonstrate the effectiveness of the proposed stochastic dynamical system and parameter identification method.
This paper considers the mathematical modeling and parameter identification of glycerol bioconversion to 1,3-Propanediol in the batch fermentation. Taking into account the characteristic of inherently genetic stochasticity and environmental disturbances in this biochemical system, a stochastic differential system with disturbance is proposed to describe this microbial fermentation process. To guarantee the model refinement and the biological robustness simultaneously, a multi-objective parameter identification model is proposed. To obtain the optimal parameters, the multi-objective optimization problem is converted to a sequence of single-objective optimization problems. Then, the problems are further approximated to a sequence of the optimization problems with linear matrix inequality constraints under the help of the T-S fuzzy approximation. Finally, the optimization problems were solved by using the linear matrix inequality optimization techniques. Numerical results show that the proposed stochastic dynamical system is fit to describe this batch process and our proposed method of parameter identification is also feasible.(c) 2022 Elsevier Inc. All rights reserved.
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