Clean Coal Technology

2020, v.26;No.129(05) 103-110

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Energy consumption modeling of cement production based on integrated neural network

HUANG Kun;YANG Wen;DING Xiaohua;

Abstract：

In order to improve the accuracy of energy consumption modeling and prediction in the cement production process,an integrated energy consumption prediction model for cement production based on neural network and Markov correction was proposed in this paper. In the data preprocessing stage,in order to reduce the scale of processing data,the average influence value method was used to reduce the data dimension,and the sensitive variables were filtered,six of the 12 variables that have a greater impact on the energy consumption output were selected to construct a 6-input single-output neural network,which made the energy consumption modeling stage selection. The structure of the neural network model is simpler,and could effectively reduce the time required to train the neural network. In the energy consumption modeling stage,in order to establish a better performance energy consumption model,the integrated learning idea was adopted on the basis of the neural network as the energy consumption modeling meta-learner,and several meta-learners were combined into a stronger performance. The average of the predicted output values of multiple neural networks was used as the prediction result of the integrated model. The dependent variables of the cement firing system and the corresponding furnace energy consumption value were used as experimental data for model training,verification and prediction. The experimental results show that the determination coefficient of the prediction results of the integrated model is improved by 0.019 compared with a single neural network. The mean value of the relative residual size between the predicted value and the true value is also reduced by 0.027 compared with the single neural network. The performance of the model is improved. In the energy consumption prediction stage,in order to further improve the prediction accuracy of the model,the Markov residual correction method is introduced,that is,the current predicted value is corrected based on the residual of the historical predicted energy consumption value and the actual energy consumption value to improve the prediction of the integrated energy consumption model accuracy. The experimental results show that the relative residual error of the predicted value corrected by Markov's correction method is reduced from-0.6% to-0.25%. The energy consumption predicted value is closer to the actual value,the prediction accuracy is significantly improved,and the law of electric energy consumption change and dependent variable of cement furnace firing system can be better excavated. The energy consumption is accurately predicted,which provides a more accurate reference basis for energy consumption supervision in the cement production process. Based on the description of the three stages of cement production energy consumption modeling,a cement production integrated energy consumption prediction model based on neural network and Markov correction is proposed in this paper,which has better prediction effects and higher prediction accuracy on cement production energy consumption.

Key Words： neural network;energy consumption modeling;cement production;integrated algorithm;Markov correction

Abstract:

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Foundation: 国家重点研发计划资助项目(2016YFB0601501)

Authors: HUANG Kun;YANG Wen;DING Xiaohua;

DOI: 10.13226/j.issn.1006-6772.IF20080609

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