Propylene polymerization production process optimal soft survey instrument and method based on chaos optimization
An optimal soft-sensor, propylene polymerization technology, applied in chaos models, instruments, electrical program control, etc., can solve the problems of influence, low measurement accuracy, and vulnerability to human factors.
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Embodiment 1
[0092] refer to figure 1 , figure 2 and image 3 , an optimal soft measuring instrument for propylene polymerization production process based on Chaos chaos optimization, including propylene polymerization production process 1, on-site intelligent instrument for measuring easy-to-measure variables 2, control station for measuring operating variables 3, storing data The DCS database 4 and the melt index soft measurement value display instrument 6, the on-site intelligent instrument 2, the control station 3 are connected to the propylene polymerization production process 1, the on-site intelligent instrument 2, the control station 3 are connected to the DCS database 4, and the The soft sensor instrument also includes an optimal soft sensor model 5 based on Chaos chaos optimization, the DCS database 4 is connected to the input end of the optimal soft sensor model 5 based on Chaos chaos optimization, and the Chaos chaos optimization based The output end of the optimal soft sens...
Embodiment 2
[0185] refer to figure 1 , figure 2 and image 3 , a kind of optimal soft sensor method of propylene polymerization production process based on Chaos chaos optimization, described soft sensor method mainly comprises the following steps:
[0186] 1), for the propylene polymerization production process object, according to the process analysis and operation analysis, select the operational variables and easily measurable variables as the input of the model, and the operational variables and easily measurable variables are obtained from the DCS database;
[0187] 2) Preprocess the sample data, center the input variables, that is, subtract the average value of the variables; then pre-whiten the input variables, that is, decorrelate the variables, and apply a linear transformation to the input variables;
[0188] 3) Perform independent component analysis on the preprocessed data, including:
[0189] (3.1) Select a random initial weight B;
[0190] (3.2) Iteratively update B, B...
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