Energy Consumption Analysis And Evaluation And Prediction Of Iron And Steel Enterprises

The steel industry is one of the most important basic industries sector and the basis of industry. Iron and steel enterprises consume a lot of energy, high-yield steel products consume large amounts of energy. However, due to various reasons, the energy consumption per ton steel in China is much higher than the world advanced level, and it is10%higher than the foreign advanced level in key enterprise in China. Energy saving and consumption reducing in steel industry is urgent to solve and it is related to the development of the country and the society.This subject is for the purpose of energy saving in iron and steel enterprise, analysis and evaluation of energy consumption in iron and steel enterprise, find out the key energy-saving, predict the iron and steel enterprises’energy consumption, dig energy-saving latent capacity. It is high consistent with the provisions of the national industrial energy efficiency. The contents of the research in the process industry has a strong versatility and promotion, and it is important to the economic and society.In order to reduce the energy consumption in iron and steel enterprises, this paper studies the energy consumption in iron and steel enterprise, the specific work is as follows:On the basis of the material flow model of the iron and steel enterprises, it started with the concept of "reference material flow", analyzed the flow of Fe-containing material in the steel production process which may occur in the actual production, and analyzed the change of energy consumption per ton of steel in these cases. The analysis results show that in the steel production process, any Fe-containing materials which join into the production process from outside are conducive to energy conservation, and the more posterior processes, the energy saving is more notable; any Fe-containing materials which as output of a process or inptut of other upstream process, will increase the process energy consumption, thereby increasing the energy consumption per ton steel, and the more posterior processes, the energy saving is more notable; any Fe-containing materials recycling using in internal processes, will make the process energy consumption increase.In order to have a comprehensive evaluation of the energy consumption of iron and steel enterprises, it selected energy consumption per ton steel as the evaluation index, built a hierarchy of gray groups analytic hierarchy process, used gray group analytic hierarchy process to evaluat the impact of the11kinds of substances on the evaluation index. This guide enterprises to establish scientific energy saving measures.When predicted iron and steel enterprises of energy consumption, selected the GA-BP neural network as a predictive model, chose the substances which affect the energy consumption per ton steel as the inputs of the neural network. In order to simplify prediction model, it used the principal component analysis to reduce the input’s dimension. Beside, used GA to optimize the initial weights and thresholds of BP neural network, overcame some shortcomings of the BP neural network itself, so improved the prediction accuracy.