The Research Of Electric Furnace Production Process Energy Consumption Monitoring And Intelligent Analysis Methods Of Power Consumption

Ferroalloy enterprise is a typical high energy consumption enterprises, electric furnace is energy-consuming subject. Its energy consumption to determine the cost of the product, the study of power consumption in favor of lower costs, thereby enhancing market competitiveness. Ferroalloy enterprises to saving energy, face two problems:first, how a more accurate measurement methods, economic and rational deployment of energy; second, how to establish a scientific and rational energy consumption analysis methods. This article is focusing on these two issues, the furnace energy consumption monitoring and electricity consumption analysis, identified the four factors of the EAF energy consumption the greatest impact, summed up its own rules and build mathematical models and analysis, enterprise energy decision-making, to achieve energy efficiency to provide scientific and theoretical basis.This paper first, the process of low-carbon ferromanganese smelting process in the eight branch of Sinosteel Jilin Ferroalloy Co., Ltd., The DCS system of the production process, the whole company from the production site connected to the metrological administrative department of industrial Ethernet communications technology into a coherent whole,and build energy management information platform based on B/S structure of the production process. The system platform based on JAVA programming language, JSP+Ajaxanywhere, Struts framework design, Tomcat5.0application server and Microsoft SQL Server2005database using Eclipse+Myeclipse, development tools, for achieve the accurate collection of eight branch801#,802#and803#three EAF energy consumption data, reliable transmission and scientific statistics, to ensure a seamless connection of the internal network, information sharing, a substantial increase in the production management levellaid a solid foundation, for in-depth energy analysis.On the basis of the use of GA-BP algorithm of the furnace energy consumption modeling analysis and the main factors Pareto structure sequence. First, for the electric furnace, the energy consumption analysis model, and model training. Were used and compared several different intelligent algorithm, first, the standard BP algorithm; Second, the improved BP algorithm, improving methods is the adaptive learning rate method, the additional momentum method, the LM algorithm; third, genetic algorithm optimization to improveBP neural network algorithm, referred to as the GA-BP algorithm, that is, both the advantages of complementary, comprehensive optimization capability of genetic algorithm and BP neural network algorithm to learn the characteristics of training ability combined with genetic algorithm to optimize the initial weights of BP network, LM algorithm as the next step training of the GA optimized BP network, thus avoiding the standard BPalgorithm convergence for a long time, vulnerable to the shortcomings of the local minima.Then, maked use of Matlab simulation software for training, simulation model, validation in the training rate, accuracy and generalization capability, the GA-BP algorithm is the most appropriate algorithm, using the GA-BP algorithm.Final, Global memory optimization and forecasting capabilities, combined with actual production data of the eight branch of Sinosteel Jilin Ferroalloy Co., Ltd., the use of smart algorithm to analyze the power consumption of ferroalloy production will depend on what factors change, and knownfactors influence the extent of the mathematical description, a mathematical analysis of the power consumption factor, according to the importance of Pareto structure sequence provides an important guarantee for the science of energy management for the enterprise.