诺西肽发酵过程生化参数软测量方法的研究
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摘要
发酵是现代流程工业中常见的一种生产方式,被广泛应用于医药、食品和化工等领域。在发酵过程中,重要生化参数(如菌体浓度、基质浓度、产物浓度等)的实时获取,对于过程的优化与控制具有十分重要的意义。然而,目前还没有实用的在线传感器能直接检测这些参数,工业生产中大都采用实验室取样分析的方法来获得。离线分析具有较大的时间滞后,给上述重要生化参数的实时监测以及该过程的直接质量控制带来了很大的困难。因此,在现有技术条件下,如何实现难于在线测量的重要生化参数的在线检测,是目前发酵生产中亟待解决的问题。
软测量是一种利用在线可测变量对难测变量进行在线估计的技术,为解决上述问题提供了一条有效途径。本文以诺西肽发酵过程为研究背景,结合诺西肽发酵过程的实际生产操作机理,对诺西肽发酵过程难于在线测量的关键生化参数的软测量建模方法及相关问题做了较为深入的研究与探讨。
本文在深入分析诺西肽发酵过程生产操作机理的基础上,重点研究了诺西肽发酵过程的多阶段特性,提出了两种不同的基于多阶段特性的诺西肽发酵过程生化参数软测量建模方法,同时也对软测量技术的相关问题做了较为深入的研究与探讨：
(1)对面向诺西肽发酵过程的RBF网络软测量建模方法,做了深入的分析与研究,并对相关的几个关键问题做了改进。针对辅助变量的选择问题,提出了基于隐函数存在定理的辅助变量选择方法；针对异常数据的检测问题,提出了基于k-NN的异常数据检测方法；针对发酵过程中系统的“状态轨迹”与发酵初始条件密切相关的实际特点,提出了基于加权RBF神经网络的软测量辨识建模方法。
(2)面向可获得充足诺西肽发酵过程输入输出数据的情况,提出了基于分阶段黑箱模型的诺西肽发酵过程生化参数软测量方法。针对诺西肽发酵过程所具有的多阶段特性,提出了基于FCM和神经网络的阶段划分方法,并基于加权RBF神经网络建模方法构建了分阶段的生化参数黑箱软测量模型。
(3)面向无法获得充足诺西肽发酵过程输入输出数据的情况,以在一定假设条件下简化得到的诺西肽发酵过程机理模型为基础,提出了基于分阶段串联混合模型的生化参数软测量方法。同时,提出了改进的模型训练算法,并利用加权RBF神经网络建模方法,实现了机理模型中未知参数的预估。
(4)以原有的诺西肽发酵过程计算机监控系统为基础,给出了诺西肽发酵过程关键生化参数软测量系统的实施方案,阐述了各相关功能模块的地位和作用。同时,分析和讨论了软测量模型校正方法,并将本文提出的软测量方法,应用于诺西肽分批发
酵过程放罐时机的识别。
上述方法的提出为实现诺西肽发酵过程关键生化参数的实时监测提供了有效的途径,同时也为实现直接质量控制奠定了坚实的基础。针对无法获得充足建模数据的情况,采用机理模型与数据相结合的串联混合模型软测量方法,利用少量的建模数据辨识机理模型中的未知参数,进而实现关键参数的实时预估；当可以获得充足的过程输入输出数据时,为了避免简化的机理模型不能完全描述过程特性的缺陷,利用神经网络可以以任意精度逼近非线性函数的特性,采用基于黑箱模型的软测量方法实现关键参数的实时预估。上述方法在诺西肽发酵过程中所取得的成功应用,验证了提出方法的有效性。这些方法可以推广到其它生物发酵过程或具有类似特性的复杂工业生产过程。
Fermentation is a common mode of production in modern process industry, which has been widely used in the production of medicine, food, chemical products, and so on. During fermentation processes, the real-time access of some important biochemical parameters such as biomass and substrate concentration is of great significance for process optimization and control. However, until now there are no practical on-line sensors that can be used to measure them directly. In the industrial production, biochemical parameters are mostly measured through sampling analysis. Off-line analysis has large time-delay, which brings many difficulties for the real-time monitoring of important biochemical parameters and the direct quality control of this process. Therefore, for fermentation processes, one of the key problems to be solved is how to fulfill the on-line measurements of important biochemical parameters under existing technical conditions.
Soft-sensing is a technique that estimates the hard-to-measure variables using easily available process variables, which provides an effective way for solving the above problems. Taking Nosiheptide fermentation process as research background, in combination with the mechanism of Nosiheptide fermentation process, deep research and discussion on the soft-sensing modeling methods and related problems are performed for the key biochemical parameters that are hard-to-measure on-line in Nosiheptide fermentation process.
Based on deep analysis of the mechanism of Nosiheptide fermentation process, multistage characteristic of Nosiheptide fermentation process is mainly studied, and two biochemical parameter soft-sensing modeling methods based on multistage characteristic are proposed in this dissertation. Meanwhile, thorough research and discussion on problems related to soft-sensing technique are performed.
(1) Discussion and research on RBF neural network soft-sensing modeling method for Nosiheptide fermentation process are performed, and improvements for several related key problems are provided. Aiming at the problem of secondary variable selection, a secondary variable selection method based on the implicit function existence theorem is proposed; Aiming at the problem of outlier identification, a k-NN algorithm based outlier identification method is proposed; By taking into account the fact that the state trajectories of fermentation processes are closely dependent on fermentation initial conditions, a weighted RBF neural network based soft-sensing identification modeling method is presented.
(2) In the case that sufficient input and output data of Nosiheptide fermentation process can be obtained, a phase-identifying-oriented black-box model based biochemical parameter soft-sensing method is proposed for Nosiheptide fermentation process. For multistage characteristic of Nosiheptide fermentation process, a phase identification method based on FCM algorithm and neural network technique is presented. And based on the weighted RBF neural network soft-sensing modeling method, a phase-identifying-oriented black-box soft-sensing model for biochemical parameters is constructed.
(3) In the case that no sufficient input and output data of Nosiheptide fermentation process can be obtained, a phase-identifying-oriented series hybrid model based biochemical parameter soft-sensing method is presented on the basis of simplified Nosiheptide fermentation process mechanism model obtained under some assumed conditions. Meanwhile the improved training algorithm for series hybrid model is presented, and the prediction of unknown parameters is realized by using the weighted RBF neural network modeling method.
(4) Based on the existing computer monitoring and control system for Nosiheptide fermentation process, the implementation scheme of biochemical parameter soft-sensing system is presented for Nosiheptide fermentation process, and the role of some related function modules is also expounded. Meanwhile, the soft-sensing model updating method is analyzed and discussed. Finally, the soft-sensing methods proposed in this dissertation are applied to identify the draw-off timing of Nosiheptide fermentation process.
The above mentioned methods provide an effective way for the implementation of the real-time monitoring of important biochemical parameters, and also lay a solid foundation for the implementation of direct quality. In the case that no sufficient modeling data can be obtained, by using series hybrid model based soft-sensing method which combines mechanism model and data, the unknown parameters of mechanism model are identified with little modeling data, and then the real-time prediction of key parameters is realized. In case that sufficient process input and output data can be obtained, to avoid the defect that the simplified mechanism model couldn't completely describe process characteristics, taking advantage of the characteristic of neural network that it can approximate nonlinear functions with arbitrary precision, the real-time estimation of the key parameters is realized using the black-box model based soft-sensing method. The above methods have been successfully used in Nosiheptide fermentation process, which validates their effectiveness. The methods can be also generalized to other fermentation processes or other similar complex industrial processes.
软测量是一种利用在线可测变量对难测变量进行在线估计的技术,为解决上述问题提供了一条有效途径。本文以诺西肽发酵过程为研究背景,结合诺西肽发酵过程的实际生产操作机理,对诺西肽发酵过程难于在线测量的关键生化参数的软测量建模方法及相关问题做了较为深入的研究与探讨。
本文在深入分析诺西肽发酵过程生产操作机理的基础上,重点研究了诺西肽发酵过程的多阶段特性,提出了两种不同的基于多阶段特性的诺西肽发酵过程生化参数软测量建模方法,同时也对软测量技术的相关问题做了较为深入的研究与探讨：
(1)对面向诺西肽发酵过程的RBF网络软测量建模方法,做了深入的分析与研究,并对相关的几个关键问题做了改进。针对辅助变量的选择问题,提出了基于隐函数存在定理的辅助变量选择方法；针对异常数据的检测问题,提出了基于k-NN的异常数据检测方法；针对发酵过程中系统的“状态轨迹”与发酵初始条件密切相关的实际特点,提出了基于加权RBF神经网络的软测量辨识建模方法。
(2)面向可获得充足诺西肽发酵过程输入输出数据的情况,提出了基于分阶段黑箱模型的诺西肽发酵过程生化参数软测量方法。针对诺西肽发酵过程所具有的多阶段特性,提出了基于FCM和神经网络的阶段划分方法,并基于加权RBF神经网络建模方法构建了分阶段的生化参数黑箱软测量模型。
(3)面向无法获得充足诺西肽发酵过程输入输出数据的情况,以在一定假设条件下简化得到的诺西肽发酵过程机理模型为基础,提出了基于分阶段串联混合模型的生化参数软测量方法。同时,提出了改进的模型训练算法,并利用加权RBF神经网络建模方法,实现了机理模型中未知参数的预估。
(4)以原有的诺西肽发酵过程计算机监控系统为基础,给出了诺西肽发酵过程关键生化参数软测量系统的实施方案,阐述了各相关功能模块的地位和作用。同时,分析和讨论了软测量模型校正方法,并将本文提出的软测量方法,应用于诺西肽分批发
酵过程放罐时机的识别。
上述方法的提出为实现诺西肽发酵过程关键生化参数的实时监测提供了有效的途径,同时也为实现直接质量控制奠定了坚实的基础。针对无法获得充足建模数据的情况,采用机理模型与数据相结合的串联混合模型软测量方法,利用少量的建模数据辨识机理模型中的未知参数,进而实现关键参数的实时预估；当可以获得充足的过程输入输出数据时,为了避免简化的机理模型不能完全描述过程特性的缺陷,利用神经网络可以以任意精度逼近非线性函数的特性,采用基于黑箱模型的软测量方法实现关键参数的实时预估。上述方法在诺西肽发酵过程中所取得的成功应用,验证了提出方法的有效性。这些方法可以推广到其它生物发酵过程或具有类似特性的复杂工业生产过程。
Fermentation is a common mode of production in modern process industry, which has been widely used in the production of medicine, food, chemical products, and so on. During fermentation processes, the real-time access of some important biochemical parameters such as biomass and substrate concentration is of great significance for process optimization and control. However, until now there are no practical on-line sensors that can be used to measure them directly. In the industrial production, biochemical parameters are mostly measured through sampling analysis. Off-line analysis has large time-delay, which brings many difficulties for the real-time monitoring of important biochemical parameters and the direct quality control of this process. Therefore, for fermentation processes, one of the key problems to be solved is how to fulfill the on-line measurements of important biochemical parameters under existing technical conditions.
Soft-sensing is a technique that estimates the hard-to-measure variables using easily available process variables, which provides an effective way for solving the above problems. Taking Nosiheptide fermentation process as research background, in combination with the mechanism of Nosiheptide fermentation process, deep research and discussion on the soft-sensing modeling methods and related problems are performed for the key biochemical parameters that are hard-to-measure on-line in Nosiheptide fermentation process.
Based on deep analysis of the mechanism of Nosiheptide fermentation process, multistage characteristic of Nosiheptide fermentation process is mainly studied, and two biochemical parameter soft-sensing modeling methods based on multistage characteristic are proposed in this dissertation. Meanwhile, thorough research and discussion on problems related to soft-sensing technique are performed.
(1) Discussion and research on RBF neural network soft-sensing modeling method for Nosiheptide fermentation process are performed, and improvements for several related key problems are provided. Aiming at the problem of secondary variable selection, a secondary variable selection method based on the implicit function existence theorem is proposed; Aiming at the problem of outlier identification, a k-NN algorithm based outlier identification method is proposed; By taking into account the fact that the state trajectories of fermentation processes are closely dependent on fermentation initial conditions, a weighted RBF neural network based soft-sensing identification modeling method is presented.
(2) In the case that sufficient input and output data of Nosiheptide fermentation process can be obtained, a phase-identifying-oriented black-box model based biochemical parameter soft-sensing method is proposed for Nosiheptide fermentation process. For multistage characteristic of Nosiheptide fermentation process, a phase identification method based on FCM algorithm and neural network technique is presented. And based on the weighted RBF neural network soft-sensing modeling method, a phase-identifying-oriented black-box soft-sensing model for biochemical parameters is constructed.
(3) In the case that no sufficient input and output data of Nosiheptide fermentation process can be obtained, a phase-identifying-oriented series hybrid model based biochemical parameter soft-sensing method is presented on the basis of simplified Nosiheptide fermentation process mechanism model obtained under some assumed conditions. Meanwhile the improved training algorithm for series hybrid model is presented, and the prediction of unknown parameters is realized by using the weighted RBF neural network modeling method.
(4) Based on the existing computer monitoring and control system for Nosiheptide fermentation process, the implementation scheme of biochemical parameter soft-sensing system is presented for Nosiheptide fermentation process, and the role of some related function modules is also expounded. Meanwhile, the soft-sensing model updating method is analyzed and discussed. Finally, the soft-sensing methods proposed in this dissertation are applied to identify the draw-off timing of Nosiheptide fermentation process.
The above mentioned methods provide an effective way for the implementation of the real-time monitoring of important biochemical parameters, and also lay a solid foundation for the implementation of direct quality. In the case that no sufficient modeling data can be obtained, by using series hybrid model based soft-sensing method which combines mechanism model and data, the unknown parameters of mechanism model are identified with little modeling data, and then the real-time prediction of key parameters is realized. In case that sufficient process input and output data can be obtained, to avoid the defect that the simplified mechanism model couldn't completely describe process characteristics, taking advantage of the characteristic of neural network that it can approximate nonlinear functions with arbitrary precision, the real-time estimation of the key parameters is realized using the black-box model based soft-sensing method. The above methods have been successfully used in Nosiheptide fermentation process, which validates their effectiveness. The methods can be also generalized to other fermentation processes or other similar complex industrial processes.
引文
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