Heating Grate Production Optimization And Operation Energy Saving

The iron and steel industry is a high-energy-consuming industry in my country,and heating furnaces,as an important energy-consuming equipment among them,are the focus of current research.Based on the current severe energy situation in my country,this article mainly starts from the two perspectives of planned production of heating furnaces and improved on-site operation,and realizes intelligent production scheduling and energy efficiency intelligent diagnosis of the rolling system.The research content is as follows:First,based on the optimization idea,under the premise of ensuring that the order can be completed before the delivery date,consider the change in the width direction of the rolled billet,and set the maximum jump coefficient of the slab target tapping temperature to indicate the degree of change of the slab tapping temperature.The layout of the rolling plan determines the rolling sequence of the slab;in terms of heating grate production,this article takes the lowest total energy consumption of the heating furnace group as the target value,and takes the relationship between the heating load of the heating furnace and the output,the rolling mill and the heating furnace As constraints,the heating grate production model was established,and the Monte Carlo method was used to solve the mathematical model,and the optimal solution for the production of the grate group was obtained.The results showed that the results obtained by the production scheduling model were better than those obtained by the on-site scheduling model.The energy consumption of the production plan is lower by 2.7%;Secondly,based on the analysis of the fuel consumption mechanism of the heating furnace,the characteristic parameters that affect the energy consumption of the heating furnace are screened.Using the XGBoost algorithm in machine learning,the selected factors are used as the characteristic parameters,and the heating furnace energy consumption is used as the target parameter to establish the energy consumption.Forecast model.Call the established predictive model to analyze the main factors affecting energy consumption.Output the feature importance of each feature parameter in the prediction model,and sort it,select some of the influencing factors,and carry out regular discussions.The results show that when the furnace time increases from 190 min to 245 min,the average furnace time increases by 5 min,the unit consumption increases by 0.27 kgce/t;when the slab discharge temperature increases from 1220 ?to 1244 ?,the average discharge temperature increases by 12 ?,the unit consumption increases by 1.02 kgce/t;the average air-fuel ratio increases from 2.8 to 3.8,The average air-fuel ratio increased by 0.1,and the unit consumption increased by 0.56kgce/t;Then carried out the energy consumption study of the heating furnace,condensed the existing production indicators on site,and established 7 indicator models to guide on-site production.On the basis of the energy efficiency intelligent diagnosis model,the energy consumption intelligent diagnosis model is perfected by deeply mining the fuel consumption formula of the heating furnace and introducing the independent influence factor of pressure.Among them,the normalization of the changes in the heat items caused by the changes of 5 factors can quickly obtain the contribution of each influencing factor,and help the site quickly find the main reason for the high or low energy consumption of the heating furnace;Finally,based on the research content of Chapters 2 to 4,the database is reconstructed,and the database data is called through Java to communicate with the compiled interface,and an online system is established to realize the intelligent production and energy efficiency intelligent diagnosis of the rolling system.