Electric Energy Prediction And Design Of Software System For Hot Rolled Strip Production Line

With the development of the world economy,the problems of resources and environment have become more and more prominent.Energy saving,emission reduction and sustainable development have become a common understanding of various industries and economic activities.Iron and steel enterprise is a key area of energy consumption,accounts for 14%of the total industrial energy.Power consumption accounts for about 11%of the industrial power consumption.High energy consumption not only causes high cost,but also wastes resources and pollutes environment.Therefore,it is of great significance to study the modeling and prediction of energy consumption on the production line,and to apply the method to the software system of the enterprise.In this thesis,the process of hot rolling strip production line is understood in detail,there are many equipment on the production line.The main researched objects of the prediction model are the rough rolling mill and the finishing mill with the most energy consuming and the most complex process.The mechanism of energy consumption is related to the pressing regulation of the products.The rolling factors including the total rolling pass,the rolling speed,the rolling time,the rolling temperature,the rolling force,the rolling moment and so on were analyzed,get the mechanism model.According to the model,we know the working mechanisms of roughing mill and finishing mill are different,but when the slab size and product size are determined,the rolling way is determined.They can be predicted by the uniform model.Energy consumption mechanism model are the same,the input includes slab thickness,slab length,slab width,product thickness,and steel type and the output includes the consumption of the product.Through mechanism analysis,it can be concluded that the energy consumption of the rough rolling mill and the finishing mill is nonlinear.To predict more accurately,this paper uses the nonlinear regression,support vector machine and improved genetic algorithm optimization support vector machine are used to analyze and compare with each other.Finally,it is proved that the improved genetic algorithm optimization support vector machine(IGA-SVR)model has the best prediction effect.At the same time,the simulation results are verified by changing the coefficient of friction coefficient,rolling force arm coefficient,temperature and other factors in the rolling process to meet the actual production process of the hot rolled strip production line.In addition,a software system based on energy consumption prediction model is established.This paper mainly introduces the development platform and process of the system,and then explains the overall architecture of the system,the function module and the design of the database involved in the process of the system development.Finally,the interface of the energy management system is designed.It can realize the energy data statistical analysis,energy consumption prediction and analysis and so on,and provide a platform for the digital management of energy in the enterprise.