Pesticide production waste liquid incinerator temperature optimization system and method of support vector machine
A technology of support vector machines and incinerators, applied in the direction of combustion methods, incinerators, general control systems, etc., can solve problems such as too low or too high furnace temperature, difficult to control furnace temperature, etc.
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Embodiment 1
[0070] Reference figure 1 , figure 2 , Support vector machine pesticide production waste liquid incinerator temperature optimization system, including field intelligent instrument 2, DCS system and host computer connected to incinerator object 1. The DCS system includes data interface 3 and control station 4 With the database 5, the field intelligent instrument 2 is connected with the data interface 3, and the data interface is connected with the control station 4, the database 5 and the upper computer 6, and the upper computer 6 includes:
[0071] The standardization processing module 7 is used to preprocess the model training samples input from the DCS database, and center the training samples, that is, subtract the average value of the samples, and then standardize them:
[0072] Calculate the mean: TX ‾ = 1 N X i = 1 N T X i - - - ( 1 )
[0073] Calculate the variance: σ x 2 = 1 N - 1 X i = ...
Embodiment 2
[0101] Reference figure 1 , figure 2 , Support vector machine-based method for optimizing the furnace temperature of pesticide production waste liquid incinerator. The specific implementation steps of the method are as follows:
[0102] 1) Determine the key variables used, collect the data of the variables mentioned during normal production from the DCS database as the input matrix of the training sample TX, and collect the corresponding furnace temperature and the operating variable data for optimizing the furnace temperature as the output matrix O;
[0103] 2) The model training samples input from the DCS database are preprocessed, and the training samples are centered, that is, the average value of the samples is subtracted, and then standardized, so that the average value is 0 and the variance is 1. The processing is completed by the following formula process:
[0104] 2.1) Calculate the mean: TX ‾ = 1 N X i = 1 N T X i - - - ( 1...
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