DMFA-based multi-objective optimization for fermentation processes

doi:10.11949/j.issn.0438-1157.20171496

Abstract

Abstract:

A multi-objective micro-scale optimization strategy for fermentation processes was proposed to achieve optimal operation on the basis of a dynamic metabolic flux analysis (DMFA) model. According to different dynamic characteristics, the strategy divided a fermentation process into two stages of cell growth and product synthesis,in which objective functions and constraints were designed from micro metabolic flux and pathways. Multi-objective particle swarm optimization (MOPSO) was employed as key algorithm to find operation trajectory. The strategy was applied to simulation of penicillin fermentation for optimizing acceleration rate of feed stock and pH trajectory. The results showed that terminal product concentration was increased by 3.26% and total feed amount was decreased by 0.6 L, indicating effectiveness of the strategy.

Key words: dynamic metabolic flux analysis, metabolism, optimization, multi-objective particle swarm optimization, fermentation

摘要：

通过动态代谢通量分析方法建立发酵过程模型，提出了一种基于微观代谢信息的发酵过程多目标优化策略，该策略基于所建微观模型，根据动态特性将发酵过程分为菌体生长和产物合成两个阶段，进行特征分析并从微观通量层面分别设计优化目标与约束条件，采用多目标粒子群算法求得最优解。该方法用于青霉素发酵过程底物流加速率和pH的操作轨迹优化，仿真实验结果表明，采用基于微观通量的多目标优化策略能够提高产物终端浓度，表明优化策略的有效性。

关键词: 动态代谢通量分析, 代谢, 优化, 多目标粒子群算法, 发酵

CLC Number:

TQ920

GAO Yan, ZHAO Zhonggai, LIU Fei. DMFA-based multi-objective optimization for fermentation processes[J]. CIESC Journal, 2018, 69(6): 2594-2602.

高岩, 赵忠盖, 刘飞. 基于动态代谢通量分析的发酵过程多目标优化[J]. 化工学报, 2018, 69(6): 2594-2602.

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