A prediction method for coal-fired unit denitration control system inlet nitrogen oxide

A technology of nitrogen oxides and control systems, applied in prediction, neural learning methods, biological neural network models, etc., can solve problems such as inability to adapt, long online training and learning time, achieve good results, save calculation time, and calculate short time effect

Inactive Publication Date: 2017-12-12
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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This invention greatly reduces the workload of staff and reduces maintenance costs, but this techn...

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  • A prediction method for coal-fired unit denitration control system inlet nitrogen oxide
  • A prediction method for coal-fired unit denitration control system inlet nitrogen oxide
  • A prediction method for coal-fired unit denitration control system inlet nitrogen oxide

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[0060]The prediction method of the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0061] Such as figure 1 As shown, the forecasting method shown includes the following steps:

[0062] Step 1: collect the concentration value of nitrogen oxides at the inlet through the field instrument, and save it;

[0063] Step 2: Preprocessing the data collected in Step 1;

[0064] Step 3: Carry out online sequential extreme learning machine learning through the preprocessed data in step 2;

[0065] Step 4: Send the new collected value of nitrogen oxide concentration to the input end of the prediction model established in Step 3 to obtain the output weight at the next moment;

[0066] Step 5: Use the output weight obtained in step 4 as the input of the single hidden layer feedforward neural network regression model of the online sequential extreme learning machine to obtain the next predicted value;

[0067] Step...

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Abstract

The invention relates to a prediction method for coal-fired unit denitration control system inlet nitrogen oxide. The method comprises the steps of collecting the concentration of inlet nitrogen oxide; pre-processing the data; performing on-line sequential extreme learning machine learning; sending a new nitrogen oxide concentration collection value into an input end of a predication model built in the third step and calculating an output weight of the next moment; using the obtained output weight as the input of a single-implicit strata feedforward neural network regression model of the on-line sequential extreme learning machine to obtain the next prediction value; returning the next prediction value obtained in the fifth step to the fourth step. The prediction method employs the online extreme learning machine, so that the calculation speed is high, only output weight needs to be updated and calculation time is greatly saved; a recursion formula is added on the basis of an off-line extreme learning machine and new output weights are obtained according to new data, so that the online learning capability is implemented, the calculation time is short and the prediction precision and the generalization ability are better than those of a neural network.

Description

technical field [0001] The invention belongs to the field of model prediction, and in particular relates to a method for predicting inlet nitrogen oxides of a denitrification control system of a coal-fired unit. Background technique [0002] With the rapid development of China's power industry, air and acid rain pollution has become increasingly serious. Especially in recent years, most cities in North China have experienced smog, which has caused great troubles to people's lives. According to the "Emission Standards of Air Pollutants for Thermal Power Plants" promulgated and implemented by China, the maximum allowable limit for nitrogen oxide emissions from thermal power boilers is 100mg / Nm3. It can be seen that efficiently solving the emission of nitrogen oxides is already a very important issue. [0003] With the improvement of national environmental protection indicators, the emission requirements of nitrogen oxides and sulfur oxides in thermal power plants are also stri...

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Application Information

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IPC IPC(8): G06Q10/04G06N3/08
CPCG06N3/08G06Q10/04
Inventor 张少康金秀章张琨丁续达
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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