Cement Burning Electricity Based On Energy-saving Control Platform Of Cement Furnace And Kiln The Optimization Method Of Consumption And Its Parameters

Cement manufacturing industry is one of the industry for energy dependence is very high,in the total cost of the whole production,energy consumption accounts for more than half of them,although the cement enterprises in recent years has made great efforts to save energy,but tons of cement comprehensive power consumption level of cement production with advanced world cement enterprise power consumption level still has certain gap.In the face of the pressure of enterprise development,cement enterprises will pay more and more attention to energy saving and consumption reduction.In the process of cement production,cement calcination system is the most energyconsuming part.There are many physical and chemical reactions in almost every link and equipment of cement calcination system,which leads to a large number of data involved in cement production.In addition,most of the cement data in the production process are nonlinear and strongly coupled,so it is difficult for traditional methods to comprehensively and quickly analyze the relationship between these data and power consumption.Therefore,it is impossible to effectively,accurately and comprehensively predict the power consumption of cement calcination.Aiming at the optimization of the power consumption of cement calcination and the prediction of the optimal key parameters in the calcination process,a model of power consumption optimization and key parameters prediction based on improved BP neural network was proposed.The specific research work is as follows:(1)To introduce production technology of cement calcination system and its process mechanism,and analyze the power consumption in the process of calcining cement distribution and key factors that affect the power consumption,through specific variables,the analysis of the influence of preliminary selection for power consumption for subsequent selection,for cement calcination system power consumption optimization and optimal key parameter prediction model lays the foundation.(2)In view of the nonlinear and coupling of the data of cement,established the cement calcining power consumption prediction model based on BP neural network,and based on the BP neural network,some defects of BP neural network has made some improvements,respectively by genetic algorithm and particle swarm algorithm of BP neural network weights and thresholds are optimized,and contrast optimization results,Finally,the improved BP neural network based on genetic algorithm was selected to establish the prediction model of cement calcination power consumption,which realized the more accurate prediction of power consumption.(3)Based on the established power consumption prediction model of cement calcination,the Mean Impact Value(MIV)method was used to calculate the average Impact Value of each key parameter,which was taken as the sensitivity Value of the parameter,and its Value represented the influence degree on power consumption.The key parameters were screened according to the sensitivity Value.Eliminate some parameters that have little influence on power consumption,reduce dimension of model input data,and select and optimize the model.(4)Finally,the genetic algorithm takes the power consumption as the fitness value to search for the optimal individual of the key parameters,which can be used as the specific parameter value of the power consumption optimization structure to provide reference for the operators.