Based On Emd-bp Neural Network Prediction Model Of Energy Management System Design And Implementation

The steel industry is the pillar industry of national economy which accounts for 15% of the nation's total industrial energy consumption. Thus it has been a typical industry for energy conservation and pollution reduction. So far, the energy management of steel enterprises has been confronting with lots of problems, such as large amounts and low concentration of measurement points; backward level in automation and informatization etc. For a long time, statistics are collected by field operator manually which cause ultimate wasting of energy due to its few information, slow data transferring and long processing circle. Therefore it is worth of research that how to establish the energy management system to improve enterprises'efficiency of energy management and to allocate energy reasonably so as to realize the overall energy conservation and pollution reduction.In this paper, blast furnace gas in the steel industry is a main object to study. Gas has been the most important secondary energy sources during the production process of steel plants. But it is often the case that plants have to take emission disposals as their first choice when gas is poorly dispatched and no balancing adjustment has been done. This has caused severe waste therefore it is of crucial significance in terms of energy conservation and pollution reduction for steel enterprises to predict scientifically the volumes of occurrence and usage of blast furnace gas and to stipulate reasonable plan of it.This paper attempts to analyze pipeline system of blast furnace gas as well as occurrence and consumption status of each user and put forward one energy management design based on EMD-BP ANN predicting model. Compared with the traditional BP ANN model, the EMD-BP ANN model may have faster prediction speed and higher accuracy. Therefore, the author designs and implements an energy management system based on EMD-BP ANN predicting model through real effect analyze in this paper. At last, the author could safely conclude that the system will keep better balance of the gas system, meanwhile reducing gas-emission rate and realizing a better effect for energy conservation.