基于最小二乘支持向量机的焦炉煤气柜位预测模型及应用

COG holder level prediction model based on least square support vector machine and its application

针对焦炉煤气柜位难以机理预测问题,通过分析煤气的产消及柜位变化特点,建立了基于最小二乘支持向量机的柜位预测模型.构造梯度网格搜索算法优选模型参数和大样本筛选方法选取训练样本,从而提高了预测精度.上海宝钢实际煤气数据的仿真结果表明,所建模型参数选取耗时少,预测效果良好,可为煤气的平衡调度提供科学指导.

Aiming at the prediction for coke oven gas holder level in steel enterprises, which is very difficult to be modeled using the mechanism modeling, a gasholder level prediction model combined with the analysis of the gas productionconsumption and level variation is established based on the least square support vector machine. A gradient grid search algorithm for selecting the model＇s parameters and an effective big samples selection approach to build the training samples are proposed to improve the prediction accuracy. The simulation results using the practical gas data in Shanghai Baosteel show that, the proposed method has shorter parameter optimization time and better performance, and can provide scientific guidance for the gas balance scheduling process.