Optimal of metering pump feeding system based on genetic algorithm

 

 

DING Yanfeng, YU Shiming

 

(College of Information and Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

 

 

Abstract: Aiming at the problem that the multipump synergy work of metering pump feeding system, an optimized scheduling with elitist selection and individual migration of multiobjective genetic algorithm was put forward. The work state and flow of each pump were scheduled reasonably to make the whole system operating optimal. The squared minimum of difference between the actual and the targeted flow was regarded as the first target,and the minimum total system power consumption was regarded as the second in operation. The mathematical model of feeding system optimization was established. Firstly,the optimized scheduling algorithms was written using Matlab platform to analyze the effectiveness and feasibility of the algorithm.The Matlab simulation results indicate that the genetic algorithm after 15~20 generations iteration,you can target the flow of 800L/H reasonable flow optimization allocation, but also effectively reduce the total power consumption of the system to meet the needs of the experimental design.Finally, combined with real system,the algorithm was implemented in Java language and embedded into the real system.The operation of real system indicate that the optimal scheduling algorithm implementation of high efficiency,optimize the effect is obvious.

 

Key words: metering pump feeding system; optimal scheduling; multiobjective; genetic algorithm