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Heat-engine plant grinding machine load prediction method based on periodic rolling optimization

A rolling optimization and load forecasting technology, applied in forecasting, instrumentation, data processing applications, etc., can solve problems such as difficulty in predicting mill load in thermal power plants and reducing model forecasting time.

Inactive Publication Date: 2015-12-16
XI AN JIAOTONG UNIV
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Problems solved by technology

[0005] The object of the present invention is to provide a load detection method of a mill in a thermal power plant to solve the problem that the load of a mill in a thermal power plant is difficult to predict. Support vector machine and other technical means realize the comprehensive utilization of each parameter information, significantly improve the model building time in the process of model building, reduce model complexity, reduce model prediction time, and finally be able to predict the mill load of thermal power plants online in real time

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  • Heat-engine plant grinding machine load prediction method based on periodic rolling optimization
  • Heat-engine plant grinding machine load prediction method based on periodic rolling optimization
  • Heat-engine plant grinding machine load prediction method based on periodic rolling optimization

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[0040] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0041] The block diagram of the thermal power plant mill load prediction method based on periodic rolling optimization of the present invention is as follows figure 1 As shown, the flow chart is as figure 2 shown. Taking a mill in a thermal power plant as an example, a specific application of the present invention is given. The mill model is DTM350 / 700, and the speed is 17.57r / min. DCS is used to realize the collection of mill input signals, the execution of algorithms, and other various operation monitoring functions. The system structure is as follows: image 3 shown. In the real-time control layer, it is composed of Siemens S7-400 and S7-200 series PLCs and their extended input and output modules, making full use of the advantages of S7-200 series PLCs such as low cost, easy expansion, and simple programming to real...

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Abstract

The invention discloses a heat-engine plant grinding machine load prediction method based on periodic rolling optimization and aims to solve the problem that the heat-engine plant grinding machine load is hard to detect. The invention adopts multiple correlated parameters about grinding machine noise, vibration and the like to perform periodic rolling optimization firstly, and then establishes a simplified least square support vector machine model based on periodic characteristics for predicting the grinding machine load. The method utilizes periodic characteristics of correlated parameters, is advantaged by low complexity, high prediction precision and goodness in online application and the like, and provides reliable guarantee for safe and economic operation of the heat-engine plant pulverizing system.

Description

technical field [0001] The present invention relates to a load forecasting method for a mill in a thermal power plant, in particular to a load forecasting method for a mill in a thermal power plant based on periodic rolling optimization. By means of technical means such as support vector machines, it is possible to predict the load of mills in thermal power plants online in real time. Background technique [0002] The pulverizing system is one of the main auxiliary systems of the thermal power plant, and the mill is the key equipment of the pulverizing system. Whether the mill can run normally and whether it is running in the best working condition is directly related to the working efficiency of the pulverizing system. Effective prediction of load changes in the mill for optimal control can greatly reduce power consumption and steel consumption, increase mill output, reduce noise, reduce dust pollution, and improve operating efficiency. Therefore, how to accurately predic...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
Inventor 司刚全郭璋曹晖贾立新张彦斌
Owner XI AN JIAOTONG UNIV
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