Generation-consumption Prediction Method Of Cove Oven Gas In Steel Industry And Its Application

Cove oven gas (COG) as a class of byproduct gas is a significant part of the secondary fuel in steel industry. Precisely forecasting the holder level and generation-consumption demand of the COG system can provide the scheduling workers with the scientific and reasonable guidance for balancing the whole gas system, reducing the waste of gas diffusion and decreasing the energy consumption of the steel enterprise.Based on an analysis of generation mechanism and the consumption characteristics of COG system, the main system units (COG users) that affect the COG gas holder level are determined. The generation and consumption of the COG are considered as a class of prediction problem based on the time series. The prediction of gas holder level is considered as a regression problem. A generation and consumption model and a gas holder level model are established based on the least square support vector machine (LSSVM) for COG system in this paper. An on-line learning algorithm and the circular Bayesian optimization are modeled in order to accelerate the model establishment and enhance the prediction precision. The practical production data from Shanghai BaoSteel is used to verify the proposed model and algorithm. The results demonstrate that the prediction precision can be greatly guaranteed under the circumstance of small training sample and high random noises. In addition, the comparison to some other prediction model also shows that the presented approach accommodates to the on-line prediction process of COG system in steel industry.Combined with the requirement of the Energy Center in Baosteel, the generation-consumption prediction and scheduling system for COG system is developed on the basis of the proposed generation-consumption model and holder level prediction model. At present, this system has been run for test in the Energy Center of Baosteel and provides with an effective guidance for the energy scheduling workers.