Optimal soft sensor system and method for intelligent weighted propylene polymerization production process
An optimal soft-sensor, propylene polymerization technology, applied in electrical program control, biological neural network model, comprehensive factory control, etc., can solve problems such as low measurement accuracy and easy to be affected by human factors
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Embodiment 1
[0082] 1. Reference figure 1 , figure 2 and image 3 , an intelligent weighted propylene polymerization production process optimal soft measurement system, including propylene polymerization production process 1, on-site intelligent instrument 2 for measuring easily measurable variables, control station 3 for measuring operating variables, and DCS database for storing data 4 , intelligent weighted optimal soft measurement system 5 and melt index soft measurement value display instrument 6, the on-site intelligent instrument 2, the control station 3 are connected with the propylene polymerization production process 1, the on-site intelligent instrument 2, the control station 3 and the DCS database 4 connected, the DCS database 4 is connected to the input end of the intelligent weighted optimal soft sensor system 5, the output end of the intelligent weighted optimal soft sensor system 5 is connected to the melting index soft sensor value display instrument 6, and the intellige...
Embodiment 2
[0155] 1. Reference figure 1 , figure 2 and image 3 , an intelligent weighted propylene polymerization production process optimal soft-sensing method includes the following steps:
[0156] (1) For the propylene polymerization production process object, according to the process analysis and operation analysis, the operational variables and easily measurable variables are selected as the input of the model, and the operational variables and easily measurable variables are obtained from the DCS database;
[0157] (2) Preprocess the sample data, center the input variables, that is, subtract the average value of the variables; and then perform normalization processing, that is, divide by the change interval of the variable value;
[0158] (3) The PCA principal component analysis module is used to pre-whiten the input variables and de-correlate the variables. It is realized by applying a linear transformation to the input variables, that is, the principal components are obtained...
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