| Biology technology gives scope to more and more function in chemical industry, medicine hygienism, agriculture, forestry, husbandry, fishery industry, foods light industry, energy and environment as one of the critical technology in the economy development in 21 century. Biology project puts forward higher and higher demand to automatic control technology as the biotechnology boosts sharply, the scale of bioengineering enlarges. The process of biological fermentation depends on multiple environmental factors featured as dynamic, nonlinear, multivariable and strong coupling. What's more, biochemical reaction mechanism in biological fermentation is so complicated that the key biological parameters which directly reflect on the characters in biological fermentation can't be measured real-time on line. This issue has become the bottleneck for restricting optimal control in biological fermentation, which seriously influences the automatic production in the process of biological fermentation. The important research in the field of biological project has focused on the advanced measurement and control technology in the process of biological fermentation process. The research is so significant to raise the automatic level for the equipment, to promote innovation for biological engineering technology, to enhance production efficiency and economic effectiveness and cultivate new variety in biology.The dissertation was supported by the National High Technology Research and Development Program ("863 Program"), one project of which is the Research for the Soft Sensor Method and Optimal Control based on FNN Inverse in the Process of Biological Fermentation under Grant 2007 AA04Z179. It is also supported by Specialized Research Fund for the Doctoral Program of Higher Education of China, one project of which is the Research for the Soft Sensor Method and Optimal Control based on FNN Inverse in the Process of Biological Fermentation under Grant 20070299010. The dissertation focuses on building the CPSO- LSSVM soft sensor model which is vital to key biological parameter in cell concentration, substrate concentration, production concentrate in the process of biological fermentation based on the method of data driven. It also puts forward the closed loop control method based on generalized predictive control (GPC) and particle swarm optimization (PSO) rolling optimization in the process of biological fermentation. The dissertation also develops the engineering soft functioning in key biological parameter and optimal control on line based on NET technology platform and C# language. It also demonstrates amino acids and typical strain lysine, conducts the relevant experimental research. The main research is as the following:1. The data-driven softsensor model in bio-chemical fermentation process has been put forward as key biological parameters checked straight and online are not available. The CPSO-LSSVM soft sensor model, including key parameters in typical bio-chemical reaction like cell, substrate, and product concentration, is established based on the combination of approximate ability of nonlinear function of LS-SVM. randomness and ergodicity of chaos motion and overall optimization of tPSO. and owns a series of advantages such as high precision of modeling, capacity of generalization and high speed of convergence with contrast to regular soft-sense measurement like LS-SVM, BP nerve net, and so on.2. Ways on how to set precise models have been offered as there are many variables in the bio-chemical fermentation and it is difficult to obtain nonlinear and mechanism model. Based on data in reaction and RBF as core function, LS-SVM nonlinear control model is set in typical fermentation process, as well as the piecewise linear precision model, with the adoption of sampling point and linearization, which possesses some advantages like simple structure and high accuracy.3. On the basis of the generalized predictive control theory, how to control the continuous feeding in the process and how to optimize the control online has been raised, as well as design methods on predictive control based on PSO optimization. The real-time optimal control and precision gets improved effectively for some though problems like optimum unavailable and complicated calculation get resolved as there are deficiencies in the traditional predictive control design based on Diophantine formula.4. Based on NET platform and C language, strategies of core calculation on CPSO-LSSVM soft-sensor and generalized predictive control have been explored. as well as the intelligent and modularized sense-and-control software system, stable and reliable, which can measure physical parameters like temperature, pressure and dissolved oxygen and sense bio-chemical parameters like cell, substrate, and product concentration, and can optimize feeding control with variables.
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