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中文摘要：
针对钢铁企业煤气系统中转炉煤气柜柜位难以建立模型进行预测的问题,结合概率神经网络、HP（Hodric-Prescott）滤波、Elman神经网络各自的性质建立了PNN-HP（2）-ENN模型,用于对转炉煤气柜柜位进行分类预测.将模型应用在企业实际数据中,实验结果表明,所建模型分类准确、耗时少、预测效果良好.与其它常用模型相比,此模型适合转炉煤气柜柜位的预测,能够为副产煤气的合理调度提供操作依据.
英文摘要：
Prediction of Lindz-Donawitz gas holder level in iron and steel enterprises is difficult to model.A LindzDonawitz gas holder level prediction PNN-HP（ 2）-ENN model is proposed,which combines the properties of probabilistic neural network,HP（ Hodric-Prescott） filter,and Elman neural network.The simulation uses data from an iron and steel enterprise,and the results show that the predictive effect of the function is excellent,with high classification accuracy and less time used.Results further show that it is more suitable for blast furnace gas output prediction than other methods,and can provide some operating proposals for the reasonable scheduling of byproduct gas.
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