Propylene polymerization production process online forecasting system and method based on group intelligent optimization
A technology for forecasting systems and production processes, applied in electrical program control, biological neural network models, comprehensive factory control, etc., can solve problems such as low measurement accuracy and easy to be affected by human factors, so as to simplify input variables and improve the model performance, efficiency-enhancing effects
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Embodiment 1
[0048] 1. refer to figure 1 , figure 2 and image 3 , an optimal online forecasting system for propylene polymerization production process based on crowd intelligence optimization, including propylene polymerization production process 1, on-site intelligent instrument for measuring easily measurable variables 2, control station for measuring operating variables 3, storing data The DCS database 4, the optimal online forecast system 5 based on crowd intelligence optimization and the melt index forecast value display instrument 6, the on-site intelligent instrument 2, the control station 3 are connected with the propylene polymerization production process 1, and the on-site intelligent instrument 2 , the control station 3 is connected with the DCS database 4, and the DCS database 4 of the crowd intelligence is connected with the input end of the optimal online forecasting system 5 based on the crowd intelligence optimization, and the optimal online forecast system 5 based on t...
Embodiment 2
[0087] 1. refer to figure 1 , figure 2 and image 3 , a method for optimal forecasting of propylene polymerization production process based on crowd intelligence optimization includes the following steps:
[0088] (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;
[0089] (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;
[0090] (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 principa...
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