Research On Improvement Of Quantum Evolutionary Algorithm And Its Application In Rolling Schedule Optimization

Cold-rolled sheets played an important role in the national economy. Nowadays,Chinese steel production is very huge, but the level of production is lower, especially thecore of cold rolled steel strip production technology almost completely in the hands offoreign companies. Hence, modern cold rolling production line with independentintellectual property rights is built, and the cold-rolled sheets of high of high quality areproduced so as to enhance the competitiveness in the world market, which is a commontarget of domestic researchers. Rolling schedule calculation is one of the core technologiesof tandem cold rolling process, and a reasonable rolling schedule is an effective way ofimproving the quality of the cold-rolled sheets. However, in this respect, there exist greatgaps between Chinese enterprises and advanced international levels; in order to catch upwith the advanced technology in the world, the Chinese reseachers need to make moreintensive study. In this study, relying on engineering practice of process optimizationcomputer system of five stand tandem cold rolling mills in a cold rolled-sheets plant, theoptimization study and practice of the rolling schedule based on quantum evolutionaryalgorithm (QEA) and support vector machine (SVM) is applied to process optimizationcomputer system.The mathematical models of rolling process are foundation of rolling scheduleoptimization. Aiming at the different technology objectives, different objective functionsand constraints are chosen. The objective functions are optimized by the improvedquantum evolutionary algorithm. Firstly, the lookup table of the quantum rotation gate isanalyzed, which is the most important influence factor of performance of the quantumevolutionary algorithm. The performance is improved by modifying the lookup table.Then, based on the minimize energy consumption objective function, the rolling scheduleis optimized by the improved quantum evolutionary algorithm. The total power reducedmore than3%. This method has already stable operation for two years in a plant, a largenumber of energy is saved and a lot of economic benefits are created.In the traditional quantum evolutionary algorithm, the rotation angle of quantum rotation gate is updated by the lookup table, but the lookup table needs to be designedaccording to the specific questions, therefore its commonality is poor. For overcoming thisshortage, the particle swarm optimization (PSO) and differential evolutionary algorithm(DEA) are introduced to update the rotation angle of the quantum rotation gate by themean of heuristic search in the angle space, and the hybrid quantum evolutionaryalgorithm (HQEA) is constructed. The test results of standard functions show that thehybrid quantum evolutionary algorithm can strengthen the global convergenceperformance of the quantum evolutionary algorithm and improve its commonality. TheHQEA is applied to optimize the rolling schedule and the rolling force and power arebalanced. Its practical value In the engineering application is proved.In the process of schedule optimization, the common method of dealing with multipleobjectives optimization is that a weight is given to every objective, and then they areaggregated into one single objective function. For eliminating the human impact of theweight assignment, it is researched that rolling schedule is optimized with multi-objectiveevolutionary algorithm (MOEA). To improve the execution efficiency of multi-objectiveevolutionary algorithm, the quantum computation and chaos are introduced to the MOEA,and quantum chaotic multi-objective evolutionary algorithm (QCMOEA) is proposed.Some standard functions testing show that the efficiency of QCMOEA is30%higher thanNSGA-II. Finally, the QCMOEA is applied to schedule optimization of tandem coldrolling mill, and the reasonable schedules are obtained. There is a theoretical foundationfor MOEA applying to schedule optimization, which is future direction.At present, hundreds of high accuracy sensors are installed on the modern rolling mill.Therefore, a mass data about equipment status and rolling process are stored in thedatabase of the process optimization computer system. Application of data mining in therolling schedule optimization is discussed and practiced, and support vector machine waschosen as data mining tool to predict the rolling force. Firstly, the mass data in thedatabase is preprocessed and a sample set for rolling force prediction based on supportvector machine is built. Secondly, the support vector machine is trained by the samplesand predicted the rolling force deviations between setting value and measured value.Finally, the result is applied to adjust the setting value of the rolling force, and the accuracy of the rolling force prediction is improved to less than5%. It is an effectivemethod for schedule optimization.The development of process optimization computer system of tandem cold rollingmill in a sheet plant is practiced based on the WinCC configuration tool and MicrosoftSQL Server2005database tool. The WinCC and programming languages such as ANSI-Cand VBScript are used for building human machine interface (HMI) monitoring system,accomplishing rolling schedule calculation and optimization, realizing the function offiling and query rolling process data and equipment status data, and improving the millmanagement function.