Research On Estimation Of Energy Consumption Of Process And Production Line In Iron And Steel Industry

(1)With the increasing tension of energy,it is necessary to reinforce energy management for industrial enterprises.As the great consumer of energy,iron and steel enterprises should take measures to improve energy utilization rate.Therefore improving energy utility is of great importance in energy management of iron and steel enterprises.Energy estimation plays an important role in realizing energy management,and consequently this thesis is focused on the energy estimation of iron and steel enterprises.Firstly,energy data is processed by statistical learning method so as to get the unit energy medium consumption of process and production line.Secondly,the Kalman filtering algorithm is adopted to further filter the unit energy data so as to achieve more precise results.The main contents are as follows:Taking the energy system of a Chinese iron and steel enterprise as the background,the problem of energy medium estimation for iron and steel enterprise is derived and energy consumption estimation is performed for the process and the production line,respectivley.For the process or production line without clear classification of products,e.g.,coking process,statistical method is used for energy estimation based on total yield and total energy of the process or production line.The unit energy consumption of process and production line can be obtained through estimating different types of energy consumption data.Based on the practical production data,the obtained prediction error illustrates the validity of the proposed modeling method.(2)Because of the high temperature environment and equipment inaccuracy,there are gross errors in some of the energy data.In order to eliminate the impact of abnormal data to energy medium estimation,K-means algorithm is applied to the energy data so as to delete the abnormal ones.Before processing the data,normalization is implemented to eliminating the influence of difference in data dimension.Then the number of categories of K-means algorithm,the similarity measure and dimension of sample data are determined.Based on the obtained unit energy consumption value,the effectiveness of the proposed algorithm is demonstrated by the comparison of total energy consumption with and without abnormal data.(3)The processed energy data by K-means is used to further improve the accuracy of unit energy consumption through the discrete Kalman filtering algorithm.In order to improve the performance of discrete Kalman filter,the selection of parameters of Kalman filter is studied.The effects of different observation noise and process noise are studied with respect to convergence speed and the estimation variance of the algorithm.With correct parameters,the discrete Kalman filtering algorithm is then used to estimate unit energy consumption.Finally,the validity of the algorithm is proved by the comparison of total energy consumption before and after filtering.(4)To support the application of energy medium estimation in iron and steel enterprises,the system of energy medium estimation is developed in this thesis.The system realizes the energy medium estimation,energy data management and energy consumption benchmark study in the view of the process,production line and products,respectively.This system can provide effective support for energy management for iron and steel enterprises.