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Abstract In iron and steel enterprises， the volume of byproduct gas system fluctuates frequently， the imbalance phenomenon is serious and the byproduct gas balance between supply and demand has enormous influence on the enterprise’s production cost and energy consumption. There are various processes and equipment relating to variety of energy medium. Combined the property with support vector machine classification， the HP filter， Elman neural network and least squares support vector machine were applied to establish the SVC-HP-ENN-LSSVM forecasting model， and the optimization operation was made according to the characteristics of the energy-using equipment， energy utilization and the predicted results. The application of the model showed that the predicted average relative error values of byproduct gas were under the 4% which can meet the requirement of industrial production. The forecast results of optimization scheduling solved the imbalance of gas system， and when it was applied to the steel business typical working， about 10% of main process energy consumes was saved. Assuming there are 330 days operation in a year，the self-provided power plant can produce more than 104 148 t steam which can save 9 998 208 kg standard coal.
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Received: 23 December 2015
Published: 26 July 2016
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2016, Vol. 51 
