基于机理模型的2-KGA混菌发酵过程关键状态变量预报

doi:10.3969/j.issn.0438-1157.2013.08.030

化工学报 ›› 2013, Vol. 64 ›› Issue (8): 2913-2917.DOI: 10.3969/j.issn.0438-1157.2013.08.030

基于机理模型的2-KGA混菌发酵过程关键状态变量预报

丰强强¹, 潘玉霖¹, 成宝琨¹, 孙君伟², 袁景淇¹

1. 上海交通大学自动化系, 系统控制与信息处理教育部重点实验室, 上海 200240;
2. 河北维尔康制药有限公司, 河北石家庄 050031

收稿日期:2012-12-09 修回日期:2013-01-30 出版日期:2013-08-05 发布日期:2013-08-05
通讯作者: 袁景淇
作者简介:丰强强(1987- ),男,硕士研究生。
基金资助:
博士学科点专项科研基金项目(20110073110018);国家自然科学基金项目(60974068)。

Key variables prediction based on mechanism models of 2-KGA mixed culture fermentation

FENG Qiangqiang¹, PAN Yulin¹, CHENG Baokun¹, SUN Junwei², YUAN Jingqi¹

1. Key Laboratory of System Control and Information Processing, Ministry of Education, Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China;
2. Hebei Welcome Pharmaceutical Co., Ltd., Shijiazhuang 050031, Hebei, China

Received:2012-12-09 Revised:2013-01-30 Online:2013-08-05 Published:2013-08-05
Supported by:
supported by the Research Fund for the Doctoral Program of Higher Education (20110073110018)and the National Natural Science Foundation of China (60974068).

摘要/Abstract

摘要： 维生素C生产的前体2-KGA是巨大芽孢杆菌和普通生酮古龙酸菌混合发酵的产物。利用先前建立的2-KGA混菌发酵动力学模型对背景厂80个批次的实测数据进行分析,结果表明该模型能够很好地符合工业生产的实际情况。在模型参数灵敏度分析的基础上固定了部分模型参数,并选取具有代表性的3个罐批(劣等、普通、优势),利用移动数据窗口技术和滚动参数辨识方法成功地进行了2-KGA浓度和底物浓度的超前4 h和8 h拟在线预报,预报误差均在5%以内。同时还比较了固定长度时间窗口和变长度时间窗口的预报结果,并根据现场实际数据特点分析了二者的优劣。

关键词: 2-KGA, 动力学模型, 移动数据窗口, 滚动参数辨识, 产物浓度预报

Abstract: 2-Keto-L-gulonic acid (2-KGA),the precursor for vitamin C synthesis,is produced by the mixed culture of Ketogulonicigenium vulgare and Bacillus megaterium.In this paper,the previously established kinetic model for 2-KGA mixed culture was firstly tested with the data of 80 industrial batches.Based on sensitivity analysis,it was found that some insensitive parameters might be assigned fixed values to minimize computing time.Then,the model was used to predict the most important state variables,i.e.,substrate and product concentrations.Moving data window technique and rolling parameter identification approach were used in the prediction process.4 h and 8 h ahead prediction errors for 2-KGA concentration were less than 5%.

Key words: 2-keto-L-gulonic acid, dynamic model, moving data window, rolling parameter identification, prediction of product concentration

中图分类号:

TP273

丰强强, 潘玉霖, 成宝琨, 孙君伟, 袁景淇. 基于机理模型的2-KGA混菌发酵过程关键状态变量预报[J]. 化工学报, 2013, 64(8): 2913-2917.

FENG Qiangqiang, PAN Yulin, CHENG Baokun, SUN Junwei, YUAN Jingqi. Key variables prediction based on mechanism models of 2-KGA mixed culture fermentation[J]. CIESC Journal, 2013, 64(8): 2913-2917.

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基于机理模型的2-KGA混菌发酵过程关键状态变量预报

Key variables prediction based on mechanism models of 2-KGA mixed culture fermentation

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