Research On Practical Swarm Optimization And Its Application In Control Of Fermentation Process

The microbial fermentation engineering is foundation of bioengineering and modern biotechnology technology. With the increasing level of the fermentation industry and the high denmand for optimization in modern production, the control in fermentation has become a universal concern. Given high non-linearity, variability and complexity of the microbial fermentation process, the classic optimal methods and control technology cannot obtain good results in fermentation processes. Because of swarm intelligence algorithm is robust and can process multiple problems in parallel with searching by groups, swarm intelligence algorithm for the microbial fermentation process provides a new method, which is simple and effective for complex optimization problems and can improve the effectivity of fermentation. Therefore, it is of academic significance and practical value to research on swarm intelligence algorithm and its application in optimization.Based on the analysis of the researching swarm intelligence algorithm and its application, an improved algorithm W-PSO, which takes the worst position of the particle swarm optimization into consideration, is proposed. Based on the former algorithm, an improved PSO algorithm-swarm energy constant particle swarm optimization (SEC-PSO) in which the population is partitioned into several sub-swarms adaptively according to the energy of the swarm is proposed. The methods of parameters selection and adjustment and guiding formulas are given by building micro-dynamic model of paticles. Finally the improved algorithms are applied in control of fermentation process including controller parameters optimization, system modeling and process optimization and control. The experimental analysis can show that the application is feasible and effective.The experimental results show that comparing with the standard PSO, the improved algorithms can be applied in complex optimization with better convergence speed. By tuning parameters of feedback controller, parameter estimation and dynamic optimization of fed batch in fermentation process, the number of iterations is reduced and searching capability and control efficiency increase significantly.