Method for predicting ammonia process flue gas desulphurization efficiency based on multiple parameters

A technology for desulfurization efficiency and prediction methods, applied in separation methods, chemical instruments and methods, biological neural network models, etc., can solve problems such as only reflection, measurement deviation, and no precedent

Inactive Publication Date: 2012-09-26
NORTHEAST DIANLI UNIVERSITY
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Problems solved by technology

This method is easily affected by the complex situation of the flue outlet, resulting in measurement deviation or can only reflect the local sulfur cont

Method used

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  • Method for predicting ammonia process flue gas desulphurization efficiency based on multiple parameters
  • Method for predicting ammonia process flue gas desulphurization efficiency based on multiple parameters
  • Method for predicting ammonia process flue gas desulphurization efficiency based on multiple parameters

Examples

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Embodiment 1

[0186] Example 1: Using the flue gas volume, circulation pump flow rate, concentration pump flow rate, ammonia concentration, absorption liquid concentration, liquid-gas ratio, inlet flue gas temperature, ammonia consumption, and spray slurry of an ammonia-based flue gas desulfurization system in a domestic power plant The above four models were trained with 1500 sets of operating parameters such as density, pH value of spray tower slurry, and pH value of pre-scrubber slurry, and then tested with 100 sets of operating parameters. The results are shown in Table 1 (partial test samples).

[0187] Table 1

[0188]

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Abstract

The invention relates to a method for predicting ammonia process flue gas desulphurization efficiency based on multiple parameters. The method is characterized by comprising the following steps of: selecting four different artificial intelligent computation models and taking parameters acquired in an ammonia process desulphurization system operational process such as multiple groups of flue gas amounts, flow of a circulating pump, the flow of a concentration pump, the ammonia concentration, the concentration of absorption liquid, the liquid-gas ratio, the inlet flue gas temperature, the ammonia consumption, the density of spraying slurry, the pH value of slurry of a spraying tower and the pH value of the slurry of a pre-washing tower as input variables of the four models; respectively training each model, and establishing a non-linear function relationship between four desulphurization parameters and the desulphurization efficiency; then respectively transmitting parameters monitored in real time into the trained artificial intelligent model, and predicting the desulphurization efficiency; and taking the average value of two predicted values in the middle as a final predicted value... The method disclosed by the invention can be used for better predicting the ammonia process desulphurization efficiency and has the characteristics of higher stability and stronger prediction capability compared with single model prediction.

Description

technical field [0001] The invention relates to the field of predicting the efficiency of ammonia-based flue gas desulfurization, in particular to a multi-parameter-based method for predicting the efficiency of ammonia-based flue gas desulfurization. Background technique [0002] There is no good monitoring method for the operating efficiency of the ammonia-based flue gas desulfurization device, and the traditional method of directly measuring the sulfur concentration in the flue gas with flue gas analyzers and other measuring instruments is still the main method. This method is easily affected by the complex situation of the flue outlet, which leads to measurement deviation or can only reflect the local sulfur content of the flue. [0003] At present, there is no precedent in China for the simple and accurate detection method of ammonia desulfurization efficiency. Contents of the invention [0004] The purpose of the present invention is to make up for the defects and te...

Claims

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Application Information

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IPC IPC(8): G06N3/02B01D53/78B01D53/48
Inventor 洪文鹏陈重张毅张玲张智达关越波
Owner NORTHEAST DIANLI UNIVERSITY
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