Research On Swarm Energy Conservation Particle Swarm Optimization Algorithm And Its Application In Fermentation Process Control

Fermentation is the basis of bioengineering as well as modern biology technology and bioengineering industrialization. In the fermentation engineering, many studies have focused on the selection and transformation of the bacteria in order to improve the level and productivity of fermentation. Modern biotechnology has made significant progress in genetic engineering and metabolic engineering fields. Strains can be high yield by induced mutation, gene recombination, and cultured. But, getting the best products by optimizing the model and control of fermentation process is still one of the main problems existing in the fermentation engineering. Therefore, control optimization of microbial fermentation process studies received increasing attention. Particle swarm optimization method is simple, easy to implement, and suitable for complex optimization problems. Therefore, using Particle Swarm Optimization algorithm for optimization of fermentation process model has become an effective way to improve the fermentation process optimal control level. In the optimization of fermentation process control, it is needed to achieve multi-objective control tasks (such as the highest yield, the shortest, the minimum) to improve production efficiency and economic efficiency. In fermentation area, traditional methods of deal with multi-objective problems (such as weighted combining of objectives and transforming objectives into constraints) are difficult to implement and easy to lose the optimal solution of non-convex objective function, resulting in the decision-making mistakes. Multi-objective evolutionary algorithm based on PSO is very suitable for solving the complex multi-objective optimal control problem of the fermentation process, because it is a global optimization algorithm, and can handle almost all types of objective function and constraints.Based on the analysis of existing research on Particle Swarm Optimization algorithm and the research on the phenomenon of easy to premature convergence and sink into local optimum, which usually occurs in the optimization process of particle swarm optimization algorithm, a Swarm Energy Conservation Particle Swarm Optimization (SEC-PSO) is proposed. SEC-PSO, which is designed with the concept of energy conservation, can solve the problem of premature convergence frequently appeared in standard PSO algorithm by partitioning its population into several sub-swarms adaptively according to the energy of the swarm. The simulation results of typical optimization problems show that the algorithm has better global search capability and faster convergence.We studied the convergence and distribution of the optimization process of multi-objective evolutionary algorithms, and proposed a Swarm Energy Conservation Multi-objective Particle Swarm Optimization (SEC-MOPSO) algrothm. In this algrothm, a Swarm Energy Conservation mechanism is used. The mechanism is combined with non-dominated sorting method, adaptive grid mechanism, and elitist strategy to improve the search capabilities of particles and avoid falling into the second-best non-dominated front. We structured a co-evolution algorithm based on complementary strengths and weaknesses among populations. In this algorithm, evolution rules of sub-population are Non-dominated sorting Genetic algorithm (NSGA) and SEC-MOPSO respectively. The simulation results show that the proposed algorithms have better distribution and convergence performence of solution than classic multi-obective evolutionary algorithm.In the research of fermentation process optimal control method, a run-to-run optimization exploits the repetitive nature of fed-batch processes in order to deal with the optimal problems of fed-batch fermentation process with inaccurate process model and unsteady process state. The kinetic model parameters, which are used in the operation condition optimization of the next run, is adjusted by calculating time-series data got from real fed-batch process in the run-to-run optimization. The simulation is taken based on industrial yeast fermentation process simulation model. The results show that this strategy can adjust its kinetic model dynamically and overcome the instability of fed-batch process effectively.SEC-PSO proposed in this paper has a strong global search capability and fast convergence. Pareto optimal solutions of SEC-MOPSO and CEMO have good convergence and distribution. Run-to-run strategy with SEC-PSO and CEMO provides an effective method to control optimization of fed-batch fermentation process.