Propylene polymerization production process optimal prediction system based on multimode crowd-sourcing and method
A technology for forecasting systems and production processes, applied in manufacturing computing systems, forecasting, biological neural network models, etc., can solve problems such as being easily affected by human factors and low measurement accuracy
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Embodiment 1
[0066] 1. refer to figure 1 , figure 2 and image 3 , an optimal forecasting system for propylene polymerization production process based on multi-mode group intelligence, including propylene polymerization production process 1, on-site intelligent instrument 2 for measuring easy-to-measure variables, control station 3 for measuring operating variables, and data storage DCS database 4, optimal forecast system 5 based on multi-mode group intelligence and 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, the on-site intelligent instrument 2, the control station The station 3 is connected with the DCS database 4, the DCS database 4 is connected with the input end of the optimal forecasting system 5 based on multi-mode group intelligence, and the output end of the optimal forecast system 5 based on the multi-mode group intelligence is connected with th...
Embodiment 2
[0123] 1. refer to figure 1 , figure 2 and image 3 , an optimal forecasting method for propylene polymerization production process based on multi-modal crowd intelligence includes the following steps:
[0124] (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;
[0125] (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;
[0126] (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 c...
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