AOD Refining Alloy Parameter Prediction And Optimization Of The End Point

Energy consumption forecast and optimization of enterprise energy planning is to develop an important part. Endpoint through the prediction and optimization system can grasp the trend of energy consumption, control of energy storage capacity and reduce energy waste, reduce production costs and improve quality of the environment, for improving the metallurgical enterprise products market competitiveness, economic efficiency and information management the level of great importance. High energy consumption of metallurgical enterprises is a long-term problems troubled enterprise.Metallurgical Enterprise energy consumption system is a complex system, which has a strong non-linear. At present, both the mechanism of analytical model, or statistical model, the model of the process of modeling assumptions by many constraints, these models to a large extent only the actual energy consumption pattern in a similar simulation, it is difficult to deal with Metallurgical Enterprise energy consumption system, complex nonlinear relationship. In this paper, the current energy consumption in large metallurgical enterprises, energy, information transmission slow to deal with long-term intelligence and information systems and low level of status, access to domestic and foreign literature in a wide range of information and on-site research, based on production needs and economic and practical point of view, the artificial neural networks combined with wavelet analysis to China Steel Group Co., Ltd., Jilin Ferroalloy Factory Five EAF refining low carbon ferrochrome as the research object, attempt to create a prediction and optimization of energy consumption model.Based on the ferroalloy production processes are fully investigated and research, in the synthesis model of energy consumption at home and abroad on the basis of the artificial neural network and wavelet analysis method to combine the establishment of energy forecasting model. Five plants for refining low carbon ferrochrome energy system in the relationship between elements of the complex nature of the use of wavelet neural network has a close and strong convergence speed, can effectively avoid the local minimum, etc., based on wavelet neural network algorithm energy forecasting model in order to collect energy consumption data based on wavelet neural network forecasting model of energy consumption for training and testing, the use of MATLAB simulation software. The results showed that the stability of wavelet neural network better, more robust and efficient energy consumption increased forecast accuracy. After the energy consumption in the wavelet neural network forecasting model based on genetic algorithm based on micro-carbon ferrochromium refining energy system optimization model. Known the amount of chromium ore, lime addition, silicon-chromium alloy, such as adding the volume of conditions, the use of the optimization model, available as a unit power consumption to optimize the objectives of the water content of chromium ore, molten slag basicity period, intermittent time optimization of production parameters, providing energy optimization strategy. Finally, investigate Jilin Ferroalloy Co, Ltd. energy information management system. Analysis and research the Jilin Ferroalloy Co, Ltd. energy information management system architecture from the monitoring network of the energy information and the energy information management software program.