A multi-model dynamic soft sensor modeling method

Abstract

A multi-model dynamic soft measuring modeling method comprises the steps of establishing multiple sub models by utilizing a self-adaptive fuzzy core clustering method and a least square support vector machine; then taking a probability distribution function constructed by a proof synthesis rule as a weight factor to perform fusing on sub model output to obtain the output of multiple models; finally performing dynamic estimation on predicted errors of the multiple models by combining an autoregression moving average model.

Description

technical field [0001] The invention relates to a soft-sensing method for esterification rate in the polyester industrial production process, in particular to a multi-model dynamic soft-sensing method based on evidence theory synthesis rules and an autoregressive sliding average model. Background technique [0002] figure 1 Shown is the basic process of esterification reaction, esterification reaction as a key link in the entire polyester production process, plays a decisive role in stabilizing polyester production. The key quality index of the outlet of the first esterification tank in the reaction device - the esterification rate directly affects the progress of subsequent reactions and the crystallization performance of polyester products, so the entire production process is often controlled by controlling the esterification rate. However, different polycondensation processes have different requirements on the esterification rate, so operating conditions such as reaction...

AI Technical Summary

Problems solved by technology

However, in recent decades, with the advancement of science and technology, modern industrial production has higher and higher requirements for the production process, the amount of data has increased sharply, the data types have become more and more complex, and the working conditions are complex and changeable. On the other hand, industrial processes are generally dynamic, and static soft-sensing methods usually cannot reflect the dynamic information and global characteristics of industrial processes, resulting in poor adaptability of the model and cannot be used for a long time

Therefore, the simple and conventional soft-sensing methods in the past can no longer meet the needs of modern production processes, and are prone to problems such as poor matching of process characteristics, low prediction accuracy, and poor adaptability.

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