Optimization Control Of Fermentation To Fill Material Rate Based On Improved PSO

Fermentation engineering is the foundation of modern biochemical engineering and biological technology and industrialization. How to improve the level of protein fermentation and productivity is the hot topic of protein fermentation industry research. Protein fermentation plays an important role as fermentation product. It is because of by the using of protein fermentation, it can realize a lot of popular biological products that has complex structure and not easy synthesized. With the development of modern biological technology, the exploration and optimization of protein fermentation mainly concentrated in two aspects. On one side many scholars concentrate their research work on the selection and the transformation of bacteria. They used induce bacteria variation, gene recombination and bacteria training to improve the characteristics of strains; on the other side they improve the output of the products of the fermentation through the optimization of the parameters in the protein fermentation process and control protein fermentation process without changing the bacteria.In recent years, with the development of group swarm intelligence algorithm, it has been the hot topic in the optimization control field. As a group intelligent algorithm, particle swarm optimization algorithm, which is simple, easy to be realized, optimization ability strong and suitable for complicated solution of the optimization problems provides an effective way for the fermentation optimization. But because the protein fermentation is the complex intermittent biological mechanism reaction process, it involves many parameters and the reaction conditions are complicated. So applying in the protein fermentation process, particle swarm optimization algorithm is worst at the convergence, easy to fall into local optimum and slow at finding the optimum. It makes the improvement of the traditional particle swarm algorithm become inevitable. In this paper, in the foundation of the traditional particle swarm algorithm, we adopt different improvement plan according to the above problems in PSO-S, and at last we put forward a new chaos type quantum particle group of step-by-step updating algorithm (WQFS-PSO), which can improve multi-disadvantage of the particle swarm optimization. And by using the standard test function to test the improved particle swarm algorithm, we can get that the new algorithm is better in precision and the speed of optimization than the traditional algorithm. At last, we imply both the traditional and the new algorithm in the process of HPV protein fermentation and the interleukin fermentation process. Through the comparison, we can test out the proposed WQFS-PSO algorithm has a good effect on the fermentation process of the two kinds protein. The fermentation results show that the WQFS-PSO method dose a better job in solving the bad convergence, easy to fall into local optimum problems, and it can be applied to the optimization process of protein fermentation technology, and it can be used in the exploration and optimization of protein fermentation technology.