Optimal Fuzzy Network Pesticide Waste Liquid Incinerator Furnace Temperature Optimization System and Method
A fuzzy network, incinerator technology, applied in combustion methods, incinerators, general control systems, etc., can solve the problems of difficult to control furnace temperature, too low or too high furnace temperature, etc.
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Embodiment 1
[0088] refer to figure 1 , figure 2 , the optimal fuzzy network pesticide waste liquid incinerator furnace temperature optimization system, including the field intelligent instrument 2 connected with the incinerator object 1, the DCS system and the host computer 6, the DCS system includes a data interface 3, a control station 4 And database 5, described field smart instrument 2 is connected with data interface 3, and described data interface is connected with control station 4, database 5 and host computer 6, and described host computer 6 comprises:
[0089] The standardization processing module 7 is used to preprocess the model training samples input from the DCS database, centralize the training samples, that is, subtract the average value of the samples, and then standardize it:
[0090] Calculate the mean: TX ‾ = 1 N Σ i = ...
Embodiment 2
[0128] refer to figure 1 , figure 2 , the method for optimizing the furnace temperature of the pesticide waste liquid incinerator with the optimal fuzzy network, the specific implementation steps of the method are as follows:
[0129] 1) Determine the key variables used, collect the data of the variables from the DCS database when the production is normal as the input matrix of the training sample TX, and collect the corresponding furnace temperature to optimize the operating variable data as the output matrix Y;
[0130] 2) Preprocess the model training samples input from the DCS database, and centralize the training samples, that is, subtract the average value of the samples, and then standardize them so that the mean value is 0 and the variance is 1. This processing is accomplished using the following algorithmic procedure:
[0131] 2.1) Calculate the mean: TX ‾ = 1 N Σ ...
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