| 基于阶段辨识的诺西肽发酵过程菌体浓度软测量 |
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| 引用本文： | 杨强大,王福利,常玉清.基于阶段辨识的诺西肽发酵过程菌体浓度软测量[J].控制理论与应用,2009,26(9):1026-1030. |
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| 作者姓名： | 杨强大 王福利 常玉清 |
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| 作者单位： | 杨强大(东北大学,材料与冶金学院,辽宁,沈阳,110004);王福利,常玉清(东北大学,信息科学与工程学院,辽宁,沈阳,110004;东北大学,流程工业综合自动化教育部重点实验室,辽宁,沈阳,110004) |
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| 基金项目： | 国家自然科学基金资助项，国家973计划子课题资助项目 |
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| 摘 要： | 由于发酵过程中系统非线性特性与发酵阶段密切相关的实际特点,针对诺西肽发酵过程菌体浓度的估计问题,提出了一种基于阶段辨识的软测量方法.首先以分阶段的诺西肽发酵过程非结构模型为基础.根据隐函数存在定理进行辅助变量的合理选择;然后利用经数学推导得到的指示变量"伪比生长率"完成发酵阶段的在线辨识,并采用神经网络构建出对应于各阶段的局部软测量模型.实际应用结果表明,所提方法有效、预估精度较高.
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| 关 键 词： | 发酵 软测量 辅助变量选择 阶段辨识 神经网络 |
| 收稿时间： | 2007/9/26 0:00:00 |
| 修稿时间： | 2009/4/27 0:00:00 |
Phase-identifying-oriented soft sensor for biomass in Nosiheptide fermentation process |
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| YANG Qiang-d,WANG Fu-li and CHANG Yu-qing.Phase-identifying-oriented soft sensor for biomass in Nosiheptide fermentation process[J].Control Theory & Applications,2009,26(9):1026-1030. |
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| Authors: | YANG Qiang-d WANG Fu-li and CHANG Yu-qing |
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| Affiliation: | School of Materials and Metallurgy, Northeastern University, Shenyang Liaoning 110004, China;School of Information Science and Engineering, Northeastern University, Shenyang Liaoning 110004, China; Key Laboratory of Integrated Automation of Process Industry, Northeastern University, Ministry of Education, Shenyang Liaoning 110004, China;School of Information Science and Engineering, Northeastern University, Shenyang Liaoning 110004, China; Key Laboratory of Integrated Automation of Process Industry, Northeastern University, Ministry of Education, Shenyang Liaoning 110004, China |
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| Abstract: | Fermentation processes have different nonlinear characteristics in different fermentation phases. A Phaseidentifying-oriented soft sensor approach is proposed for estimating the biomass in Nosiheptide fermentation process. Based on the segmented unstructured model of Nosiheptide fermentation process, the secondary variables are selected according to the implicit function existence theorem. The on-line identification of fermentation phases is accomplished by using an indicator variable which is gained by mathematical inference, and for each phase, a local soft sensor model is developed. The practical application results show the effectiveness and validity of the presented approach. |
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| Keywords: | fermentation soft sensor selection of secondary variables phase identification neural networks |
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