bp particle swarm optimized industrial melt index soft sensor instrument and method
A BP particle swarm and melt index technology, applied in the field of soft measuring instruments, can solve the problems of difficult parameter selection, low measurement accuracy, and low noise sensitivity
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Embodiment 1
[0088] refer to figure 1 , figure 2 , an industrial melt index soft measuring instrument optimized by BP particle swarm, 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 DCS database for storing data 4 And the melt index soft measurement value display instrument 6, the field intelligent instrument 2, the control station 3 are connected with the propylene polymerization production process 1, the field intelligent instrument 2, the control station 3 are connected with the DCS database 4, and the soft measurement instrument is also Including the soft sensor model 5 optimized by the particle swarm optimization BP neural network fuzzy equation, the DCS database 4 is connected to the input end of the industrial melting index soft sensor model 5 optimized by the BP particle swarm optimization, and the industrial melting index soft sensor model 5 ...
Embodiment 2
[0169] refer to figure 1 , figure 2 , a soft-sensing method for propylene polymerization production process based on particle swarm optimization algorithm to optimize BP neural network fuzzy equation model, the specific implementation steps of the soft-sensing method are as follows:
[0170] 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;
[0171] 2) Preprocess the model training samples input from the DCS database, and centralize the training samples, that is, subtract the average value of the samples, and then standardize them so that the mean value is 0 and the variance is 1. This processing is accomplished using the following algorithmic procedure:
[0172] 2.1) Calculate the mean: TX ...
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