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Application of K- NN regression for predicting coal mill related variables

机译:K-NN回归在预测煤磨相关变量​​的应用

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Modern coal fired power plants are required to handle a variety of coal types and accommodate large load changes. Coal mills grind the coal to required fineness and primary air dries and supplies the pulverized fuel to the burners. The dynamic response of coal mills is poor due to simple controls and various faults occurring inside the milling system. In this paper, an approach for time series prediction of n- step ahead values of important variables associated with the milling system is provided. A simple, data driven, non parametric technique i.e. k-NN regression is used for the prediction. The prediction of mill variables is helpful for improving controls and optimizing the mill operation. The proposed approach is applied for 5 minute ahead prediction and validated using the actual data obtained from a coal fired power plant in Gujarat, India.
机译:现代燃煤发电厂需要处理各种煤炭类型并适应大负载变化。煤磨机将煤研磨到所需的细度和初级空气干燥,并将粉煤泵供应到燃烧器上。由于铣削系统内部发生简单的控制和各种故障,煤磨机的动态响应差。在本文中,提供了一种与铣削系统相关联的重要变量的N-Step前面值的时间序列预测方法。简单,数据驱动,非参数技术,即K-NN回归用于预测。研磨机变量的预测有助于改善控制和优化研磨操作。拟议的方法适用于前方预测5分钟,并使用从印度古吉拉特邦的煤发电厂获得的实际数据进行验证。

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