Propylene polymerization production process fuzzy optimal prediction system and method
A technology for forecasting systems and production processes, applied in manufacturing computing systems, biological neural network models, forecasting, etc., can solve problems such as low measurement accuracy, fuzzy optimal forecasting systems, and susceptibility to human factors
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Embodiment 1
[0049] 1. Reference figure 1 , figure 2 and image 3 , a fuzzy optimal forecast system for propylene polymerization production process, including propylene polymerization production process 1, on-site intelligent instrument 2 for measuring easily measurable variables, control station 3 for measuring operating variables, DCS database for storing data 4, fuzzy Optimum forecasting system 5 and melt index forecast value display instrument 6, the on-site intelligent instrument 2 and the control station 3 are connected to the propylene polymerization production process 1, the on-site intelligent instrument 2 and the control station 3 are connected to the DCS database 4, and the The DCS database 4 is connected to the input end of the fuzzy optimal forecast system 5, and the output end of the fuzzy optimal forecast system 5 is connected to the melt index forecast value display instrument 6, and it is characterized in that: the fuzzy optimal forecast system includes:
[0050] (1) Th...
Embodiment 2
[0089] 1. Reference figure 1 , figure 2 and image 3 A method for fuzzy optimal forecasting of a propylene polymerization production process comprises the following steps:
[0090] (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;
[0091] (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;
[0092] (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 by C=MU,...
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