Research On Key Technologies Of Energy Consumption Monitoring And Prewarning System For Cement Enterprises

Energy management system,as an important component of the cement enterprise information system,is used for the energy data collection,storage,statistics and analysis in order to realize the scientific management of energy data,and plays an important role in device management,energy consumption calculation,energy planning,fault alarm,energy prediction.As a subsystem of energy management system,the energy monitoring and warning system of cement enterprises plays a significant role in promoting the unified scheduling of energy in the cement industry,optimizing the energy management process,improving labor productivity and reducing the risk of enterprise operation.Combined with the actual situation of a cement plant,this topic is based on the data collection platform,combining relevant theoretical knowledge and data to carry out empirical analysis under the background of the energy management system,and studies the key technologies of the energy monitoring and prewarning system of cement enterprises.First of all,this project has studied the method of the energy data acquisition based on serial communication and OPC technology,and analyzed the factors affecting the success rate of energy data acquisition.The function module mainly includes the user custom process energy consumption formula calculation module,the serial port self-starting module,the fault identification module and the module of running time statistics for key equipments.The key component that affects the success rate of the energy data acquisition are mainly to realize the coordination between the host,Ethernet,network management center,network control system and terminal equipment.To this end,a method of data caching in local database is proposed and designed to achieve continue transferring from breakpoint for data.This part of research is not only the basis of energy consumption monitoring,but also the key to its function.Secondly,this paper studies and preliminarily establishes prewarning index system for the energy consumption,and puts forward the method for determining the warning interval and establishing the prewarning model.According to the characteristics of energy consumption data,this paper proposes the method of K-means clustering analysis to identify prewarning interval and support vector machine for prewarning modeling.The least squares support vector machine regression method is used to model for the single variable previous warning model,and the PSO-SVM method for the multivariable prewarning model,and the data for the coal consumption data for the 1# clinker line and the power consumption data for the raw material preparation are verified.Finally,the goal of real-time monitoring,statistical analysis and prewarning of energy consumption data is achieved through a software platform.