Energy Efficiency Analysis And Energy Consumption Prediction Of Sintering Process

With the green transformation and quality development of iron and steel industry,new requirements are put forward for the intelligent level of production process.Sintering process is one of the iron and steel production processes,and its energy consumption accounts for about 23%of the total energy consumption.It is necessary to analyze and predict the utilization and consumption of energy in sintering process.In the study,select the production parameters of sintering process in iron and steel enterprise.Through the establishment of sintering exergy balance model,find the main influencing factors of internal and external exergy loss,and obtain the main process parameters affecting energy consumption.The results show that the sintering process efficiency is 40.23%,the internal loss rate is 55.10%,and the external loss rate is 4.67%,the energy utilization efficiency is low.Solid fuels account for 83.37%of the total input.Solid fuels include coke powder and pulverized coal,the results show that the thickness of the material layer,the temperature of the mixture,the moisture of the mixture and the ratio of quicklime are the main factors affecting the solid fuel consumption.Select neural network as the prediction method,based on grey correlation degree method,nine factors are established as the input of the prediction model.The BP neural network prediction model is used to train and test the network through the field data,the average absolute error of the prediction model for sintering energy consumption is 0.68kgce·t^-1,the maximum error of BP neural network is 2.69 kgce·t^-1;genetic algorithm is used to optimize the BP neural network(GA-BP)prediction model,the results show that the average absolute error of the optimized energy consumption is 0.49 kgce·t^-1,and the overall prediction error is smaller.The results show that GA BP model has high prediction accuracy.The method can provide technical support for energy consumption analysis and automatic adjustment of sintering process.Figure 26;Table 9;Reference 66...