化工学报 ›› 2018, Vol. 69 ›› Issue (6): 2594-2602.DOI: 10.11949/j.issn.0438-1157.20171496

• 过程系统工程 • 上一篇    下一篇

基于动态代谢通量分析的发酵过程多目标优化

高岩, 赵忠盖, 刘飞   

  1. 江南大学轻工过程先进控制教育部重点实验室, 江苏 无锡 214122
  • 收稿日期:2017-11-09 修回日期:2018-01-17 出版日期:2018-06-05 发布日期:2018-06-05
  • 通讯作者: 刘飞
  • 基金资助:

    国家自然科学基金项目(61573169);流程工业综合自动化国家重点实验室开放课题(PAL-N201502)。

DMFA-based multi-objective optimization for fermentation processes

GAO Yan, ZHAO Zhonggai, LIU Fei   

  1. Key Laboratory of Advanced Control for Light Industry Processes, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu, China
  • Received:2017-11-09 Revised:2018-01-17 Online:2018-06-05 Published:2018-06-05
  • Supported by:

    supported by the National Natural Science Foundation of China (61573169) and State Key Laboratory of Synthetical Automation for Process Industries (PAL-N201502).

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

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

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