Research On The Dynamic Modeling And Optimized Controlling Of Biochemical Process

The rapid development of biotechnology has highlighted biochemical industry in the national economy. However, its level of automation is still lower than other industrial processes because of the complex biochemical reaction mechanism, and the features of non-linearity, time variability, model uncertainty as well as the lack of reliable sensors to be used in on-line inspection of process variables. For these reasons, the studies of suitable intelligent modeling, optimization control strategy and the application to biological and chemical process have become a new trend in biochemical engineering, which may be of great profundity promoting the development of chemical and biological technology and reducing the consumption of raw materials and energy.The funding program for this paper (863 program focusing on the development of high-tech sub-topic) " The Applied Engineering Research of Modern, Intensive Fermentation Process Control Based on Metabolic Engineering and Intelligent Works " is intended to solve the urgent problems for biochemical industry, which includes: 1) the study of the state prediction and presumption, soft sensor, the universal law of external regulation of metabolic flow and key technologies of the fermentation process based on the metabolic model; 2) the study of application research of online controlling technology of fermentation process based on the metabolic network model; 3) the study of controlling for fermentation process based on the intelligent model; 4) the study of critical technologies for data mining, multivariable analysis and online fault diagnosis in the process of fermentation based on intelligent model to construct an on-line fault diagnosis or early warning systems;5) the study of methods to construct demonstration devices and systems (reporting, real-time curve, databases, etc.) for the process controlling. Of the 5 aspects of the study, my research focuses on 1), 3) and 5), of which the biochemical processes involved is mainly about glutamic acid fed-batch fermentation process. The main done work goes as the follows:A soft model of glutamic acid fermentation process is constructed by using the standard support vector regression (SVR) on the basis of common biochemical models and analyzing current domestic and international researches of biochemical process modeling and optimization controlling. The experiments verify that this model can effectively measure 3 state variables, namely, glutamic acid concentration, biomass concentration and residual sugar concentration, which can not be done on-line. However, although the forecast model has a certain degree of accuracy, but somen problems are still existed, such as the model can not be training online, and the training is more time-consuming with the increasing sampling number.To solve the problem of the soft-sensor model using the SVR, another soft modeling based on least square support vector regression(LSSVR) algorithm for glutamic acid fermentation process is constructed. The experiments indicate that the model despite shortened training time the prediction accuracy declines, the problem that the model can not be training online still remain.For the problems with the soft-sensor modeling using LSSVR, an improved multi-input and output online LSSVR (MIMO-OLSSVR) with a forgetting factor is designed and applied. To improve the automation degree of the algorithm, an immune genetic algorithm (IGA) is adopted to achieve automatic selection of MIMO-OLSSVR parameters. Using the MIMO-OLSSVR model to estimate glutamic acid fermentation process, the experiments show that the model can simultaneously predict the three state variables and improve the accuracy while reducing time consumption by training online. However, this modeling is a black box in nature and does not reveal the operational characteristics of the actual biochemical process.To further explore the dynamics of biochemical process, the theory of hybrid systems is adopted for glutamic acid fed-batch fermentation process, and a hybrid kinetics system model is accordingly constructed. The research has proved the existence of systematic solution, uniqueness and the continuous solution dependence on the initial values and parameters in the space of continuous segmentation. Taking the highest production as the optimization target, and searching for the suitable feeding operation variables by utilizing the chaos and simplex method, the experiments prove that the model can achieve the purpose, which may provide a new theoretical for the research of biochemical process control.This study proposes a dynamic multi-model fusion modeling method for glutamic acid fermentation process by using online data and offline data on the basis of various models to solve problems from weakness of the mathematical model or the soft-sensing model, which may often fail in actual biological changes with the time advance. For the multi-objective optimization as the program requires, an improved multi-objective quantum delta-potential-well-based particle swarm optimization (MQDPSO) strategy is presented on the basis of standard particle swarm optimization algorithm and is tested by the Deb test functions. Then combined with the techniques of multi-model fusion modeling, dynamic optimizing for glutamic acid fed-batch fermentation process is achieved and the feasibility of the optimization method is proved.The improved modeling techniques and the control strategies integrated into the distributed control system can achieve the purposes of real-time status of forecasting, monitoring and modularizing modeling and optimizing the glutamic acid fed-batch fermentation process, which can bring much convenience to the application in real control systems.