Operation Optimization And System Development Of Continuous Annealing Production Process In Cold Rolling Plant

With the process of science and technology,the competition in the market of iron and steel enterprises is increasingly intensified,and at the same time the requirements of energy saving for iron and steel enterprises is also increasingly strengthened in China.So it has become a more urgent need for iron and steel enterprises to further improve their operation optimization in production processes.Operations optimization is located between the production scheduling layer and the process control layer in the automation system of iron and steel enterprises.It can determine a set of optimal setting values for control variables through optimization algorithms and then these setting values are sent the control layer to act as the reference control output.The operation optimization can help to achieve stable operation of the production process,improve the efficiency of the equipment and the quality of the product,and reduce energy consumption and production costs.Since operation optimization can directly determine the product quality and energy consumption,it has become one of the hottest research areas in process control and has a significant importance for iron and steel enterprises.This dissertation takes the continuous annealing process of cold rolling plant in a major iron and steel enterprise as the background,and investigates the multi-objective operation optimization and system development of this production line so as to improve strip steel quality,reduce energy consumption and increase production outputs.The detailed research components are as follows:(1)The continuous annealing process is analyzed based on mechanism and data-driven views,and the main factors that have significant effect on strip quality are extracted.Since the strip quality control,the energy consumption and the product output are interacting and conflicting with each other,a multi-objective operation optimization model is established so as to minimize the deviation of strip hardness,energy consumption of heating furnace,and deviation of output temperature of strips,and maximize the total product output.(2)For the multi-objective optimization problem,a self-adaptive multi-objective genetic algorithm is developed.In the proposed algorithm,a self-adaptive selection strategy for multiple crossover operators is adopted to improve the search robustness and diversity.In addition,the concepts of personal best and global best solutions in particle swarm optimization are incorporated to update solution so as to improve the search convergence speed.Computational results on benchmark multi-objective problems illustrate the efficiency of the proposed algorithm.(3)For the established operation optimization model of continuous annealing,the above self-adaptive multi-objective genetic algorithm is used to solve it.The computational results on practical instances also illustrate the algorithm’s efficiency.(4)Based on the established mathematical model and algorithm,the operation optimization software for continuous annealing production process is developed from the demands of practical production.