High sulfur natural gas purifying process modeling and optimizing method based on extreme learning machine
An extreme learning machine and purification process technology, applied in biological neural network models, special data processing applications, instruments, etc., can solve difficult target problems, solve pros and cons, inconsistencies and other problems, achieve fast learning speed and improve model accuracy , the effect of good generalization performance
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[0050] see image 3 , a method for modeling and optimizing the purification process of high-sulfur natural gas based on extreme learning machine, the method is carried out as follows:
[0051] Step 1: Determine the input variables of the high-sulfur natural gas purification and desulfurization process model: select m process operation parameters that can be effectively controlled during the production process of the high-sulfur natural gas purification and desulfurization process as model input variables, where m=10, input The variables are: x 1 Indicates the inlet flow rate of the desulfurization absorption tower amine liquid, x 2 Indicates the inlet flow rate of tail gas absorption tower amine liquid, x 3 Indicates the raw material gas processing capacity, x 4 Indicates the circulating volume of semi-rich amine solution, x 5 Indicates the inlet temperature of the primary absorption tower amine liquid, x 6 Indicates the inlet temperature of the secondary absorption tower...
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