| 沈邦玉,刘重阳,叶剑雄,冯恩民.微生物连续发酵系统参数辨识与优化算法[J].,2012,(1):150-156 |
| 微生物连续发酵系统参数辨识与优化算法 |
| Identification of parameters and optimization algorithm in system of microbial continuous culture |
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| DOI：10.7511/dllgxb201201026 |
| 中文关键词: 数学模型 参数辨识 PSO算法 连续发酵 |
| 英文关键词: mathematical model parameter identification PSO algorithm continuous cultures\@ |
| 基金项目:“八六三”国家高技术研究发展计划资助项目(2007AA02Z208);“九七三”国家重点基础研究发展计划资助项目(2007CB714304);国家自然科学基金资助项目(10471014,10871033);数学天元基金资助项目(111260777). |
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| 中文摘要: |
| 考虑3-HPA对细胞生长的抑制作用和底物与产物的跨膜运输方式,建立了能更好描述微生物连续发酵过程的新的数学模型,以计算值与实验稳态数据之间的平均相对误差为优化目标,以多个动力系统为状态约束,建立了参数辨识模型,证明了该辨识模型的参数可辨识性,并构造了改进的粒子群(PSO)算法求解该辨识模型．数值结果表明该新的动力学模型能更好地描述实际微生物连续发酵过程. |
| 英文摘要: |
| Considering the inhibition of 3-HPA to the growth of cells and the mode of transmemberane transport between substrate and product, a novelty mathematical model is established to describe the microbial continuous cultures better. Taking the mean minimal error between calculated values and the experimental data of steady state as the performance index, a parameter identification model involving multiple dynamic systems is presented. The identifiability of the model is also proved. An improved particle swarm optimization (PSO) algorithm is constructed to solve the parameter identification model. Numerical results show that the established model can describe the microbial continuous cultures process better. |
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