Intelligent Optimization Of Load Distribution For Hot Rolled Strip

It is necessary to formulate suitable rolling procedures in the hot strip rolling process.The core issue of rolling regulations for finishing mills is load distribution.The essence of the load distribution problem is to reasonably distribute the total reduction from the entrance to the exit of the finishing mills to each stand to meet the target requirements.The quality of load distribution plan has a significant impact on product quality,equipment loss and production efficiency.The traditional load distribution schemes are usually formulated by the empirical method,which is feasible but not optimal.As the market continues to improve product quality requirements,traditional load distribution methods are increasingly unable to meet the production needs of contemporary enterprises.The exploration of new methods is becoming more and more important.With the rapid development of computer industry,the advantages of intelligent technology are more and more obvious.Using intelligent methods to deal with the problem of load distribution can well meet the production requirements and formulate more reasonable rolling regulations.In this paper,the research progress in the field of strip load distribution and intelligent algorithm at home and abroad is introduced in detail.The mathematical models in the finishing process of hot continuous rolling are analyzed,and the relationship between them is clarified.The objective function model of load balance,shape optimization and rolling power minimization is established.In view of the problem of load distribution in the finishing mills of hot continuous rolling,the advantages and disadvantages of various intelligent optimization methods are comprehensively considered.Particle swarm optimization(PSO)is selected to study load distribution.The main research contents are as follows.The inertia weight of particle swarm optimization has a significant impact on the convergence performance of the algorithm.Firstly,the performance of particle swarm optimization algorithms that weight has been improved,which are linear decreasing weight.adaptive weight and random weight,is compared and studied by benchmark function.Among them,the inertia weight of the random weight particle swarm optimization algorithm is a random number that obeys a certain distribution.It can balance the global and local convergence performance of the algorithm and can be applied to load distribution instances.However,in the benchmark function test results,the random weight PSO algorithm only shows better results in some functions.Its overall optimization effect is not very ideal.In order to further improve the performance of particle swarm optimization,constriction factor is used to replace inertia weight in particle velocity updating formula.The inertia weight only limits the previous speed of particles,while the constriction factor can limit the overall speed of particles,so that the local convergence performance of the algorithm is significantly improved.The consequences of function test and example calculation results of load distribution for constriction factor particle swarm optimization(CFPSO)indicate that the optimization accuracy of the algorithm is greatly improved,and the convergence speed is also good.Finally,hybrid constriction factor particle swarm optimization(HCFPSO)is proposed drawing on the experience of the hybrid method in genetic algorithm.The hybrid strategy can combine the speed and position information of two parent particles to produce more excellent offspring particles.The diversity of particles is improved,which avoids the algorithm can't jump out of the local extremum and improve the optimization ability of the algorithm.The consequences of benchmark function test indicate that the improved hybrid particle swarm optimization algorithm has good performance in convergence accuracy and convergence speed.The calculation results of load distribution examples show that the optimized load distribution scheme can obtain a very stable relative convexity of the 4th to 7th stands.The shape of the strip is improved effectively.The result of total rolling power is obviously lower than that of other methods.The improved hybrid particle swarm optimization algorithm can best meet the needs of the target and has better practical application value.According to the actual demand of hot rolled strip production,this paper establishes a suitable mathematical model of load distribution.The performance of the particle swarm optimization algorithm was gradually optimized.Including weight improvement,constriction factor and improved hybrid particle swarm optimization algorithm.Finally,the effectiveness of these improved algorithms is verified by the high-dimensional multi-extreme benchmark function test and the load distribution calculation.