Systematic Analysis And Application Research Of Energy Consumption In Cement Production

My country’s "dual carbon" goals and tasks are arduous,and enterprises are under great pressure to save energy.The Monitoring Analysis of cement energy consumption Production Corporate is conducive to discovering the production links of cement production enterprises to reduce energy consumption,reducing enterprise energy consumption,reducing enterprise costs on the one hand,and alleviating the current shortage of electricity and energy in my country on the other hand.The influencing factors of energy consumption in the cement production process are complex and have strong time-varying,time-delay,nonlinear and other characteristics.To this end,this paper studies the construction of a cement production energy consumption prediction model based on the deep learning method through the energy consumption data of cement production enterprises,and designs an energy consumption analysis system for cement production enterprises to achieve effective supervision of cement production energy consumption.In this paper,a cement production energy consumption prediction model based on the sliding window deep belief network of the improved particle swarm optimization algorithm is constructed.First,the improved PSO particle swarm optimization algorithm is used to optimize the network model structure,and the sliding window technology is used to eliminate the time-varying delay of the cement production energy consumption data,and then use the unsupervised layer-by-layer greedy algorithm to train the network structure forward,and then use the Adam algorithm to perform reverse fine-tuning of the DBM network weights to further improve the structural parameters of the network model to improve the prediction accuracy of cement production energy consumption,and finally use the comparison The method is verified by the data of Nanyang Wolong Zhonglian Cement Enterprise Database,and finally the IPSO-SW-DBN cement production prediction model is verified,which has high accuracy.In this paper,a cement production energy consumption prediction model IPSO-SW-DBN is constructed.The network model structure is first optimized using the improved PSO particle swarm algorithm,and the sliding window technology is used to eliminate the time-varying delay of the cement production energy consumption data.,and then use the unsupervised layer-by-layer greedy algorithm to train the network structure forward,and use the Adam algorithm to perform reverse fine-tuning of the weights of the DBM network,optimize the structural parameters of the network model to improve the prediction accuracy of energy consumption in cement production,and finally use the comparison method.,and verified with the data of Nanyang Wolong Zhonglian Cement Enterprise Database.The experimental results show that the IPSO-SW-DBN cement production prediction model has high accuracy.This paper further designs the overall framework of the energy consumption analysis system for cement enterprises based on the specific business needs of enterprises for energy consumption management.Common modules such as query are designed and implemented.