Multi-stage fusion modeling for penicillin fermentation process based on EM algorithm

doi:10.3969/j.issn.0438-1157.2014.12.039

CIESC Journal ›› 2014, Vol. 65 ›› Issue (12): 4935-4941.DOI: 10.3969/j.issn.0438-1157.2014.12.039

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Multi-stage fusion modeling for penicillin fermentation process based on EM algorithm

XIONG Weili^1,2, YAO Le², XU Baoguo^1,2

1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, Jiangsu, China;
2. School of Internet of Things Engineering, Institute of Automation, Jiangnan University, Wuxi 214122, Jiangsu, China

Received:2014-09-19 Revised:2014-09-23 Online:2014-12-05 Published:2014-12-05
Supported by:
supported by the National Natural Science Foundation of China (21206053, 21276111) and by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

基于EM算法的青霉素发酵过程多阶段融合建模

熊伟丽^1,2, 姚乐², 徐保国^1,2

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;
2. 江南大学物联网工程学院自动化研究所, 江苏无锡 214122

通讯作者: 熊伟丽
基金资助:
国家自然科学基金项目(21206053,21276111);江苏高校优势学科建设工程资助项目(PAPD).

Abstract

Abstract: Penicillin fermentation process has the feature of the distinct phases, which can be seen from some key operating variables. In this paper, the key process variable, namely, the cold water flow rate was taken as the scheduling variable, which was then classified by the fuzzy C-means clustering algorithm. The cluster centers were considered as the main operating points of the penicillin fermentation process. Local models were constructed around each operating point based on EM algorithm. Thereafter, sub-models were combined togethter according to the posterior distribution of the scheduling variable. The feasiblity and effectiveness of the proposed method was illustrated through the Pensim simulation platform.

Key words: EM algorithm, fuzzy C-means clustering algorithm, penicillin fermentation process, fusion modeling

摘要： 青霉素发酵过程具有明显的阶段特征,该特征从一些关键操作变量信息中能够得到反映.本文从反应过程的多个操作变量中,选取关键过程变量——冷水流加速率作为调度变量,并采用模糊C均值聚类算法对其进行分类,各聚类中心作为青霉素发酵过程的主要工况点;基于EM算法围绕不同工况点建立局部子模型,最后根据采样数据阶段特征的后验分布将各子模型融合.基于此方法采用Pensim仿真平台数据,能够辨识数据的阶段特征,并建立青霉素发酵过程的融合模型.仿真结果表明该模型具有较高的拟合精度,能对该发酵过程的主导变量进行比较精确的预测.

关键词: EM算法, 模糊C均值聚类算法, 青霉素发酵过程, 融合建模

CLC Number:

TP183

XIONG Weili, YAO Le, XU Baoguo. Multi-stage fusion modeling for penicillin fermentation process based on EM algorithm[J]. CIESC Journal, 2014, 65(12): 4935-4941.

熊伟丽, 姚乐, 徐保国. 基于EM算法的青霉素发酵过程多阶段融合建模[J]. 化工学报, 2014, 65(12): 4935-4941.

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Multi-stage fusion modeling for penicillin fermentation process based on EM algorithm

基于EM算法的青霉素发酵过程多阶段融合建模

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