Debutanizer soft measurement modeling method based on ALIESN online learning algorithm

Abstract

The invention discloses a debutanizer soft measurement modeling method based on an ALIESN online learning algorithm. According to the method, key variables are normalized to obtain auxiliary variablesand dominant variables, and then dynamic modeling is carried out, so that the auxiliary variables and the dominant variables only having a static characteristic time sequence are converted into ALIESN input variables having dynamic characteristics; carrying out offline training learning on the first n data of the ALIESN input variable by adopting an ALIESN ridge regression offline learning algorithm to obtain a neural network offline training output weight; and finally, taking the upper numerical value as an initial weight of online learning, and starting online output weight training from the (n + 1) th data of the ALIESN input variable to obtain a final network output weight and a predicted output variable. The method is simple and rapid in learning algorithm, low in cost, few in training data, high in dynamic nonlinear system approximation capability, high in network output weight speed, capable of improving the prediction precision of the butane content and suitable for the requirement for real-time learning in the fine chemical engineering process and the like.

Description

technical field [0001] The invention relates to a soft sensor modeling method for a debutanizer tower based on an augmented leakage integral echo state network (ALIESN) fast online learning algorithm, belonging to the field of soft sensor, specifically a debutanizer based on an ALIESN online learning algorithm Soft-sensing modeling method for alkane towers. Background technique [0002] In the chemical process, real-time online monitoring of important variables plays an extremely important role in product quality control. However, these variables are often in the environment of high temperature, strong nonlinearity, strong radiation, and rapid changes in the chemical process, and cannot be directly, timely, and accurately measured and collected. Therefore, the application of soft sensor technology was born. [0003] In the past ten years, soft sensor technology has developed rapidly, especially the soft sensor modeling method based on neural network, which has been applied ...

AI Technical Summary

Problems solved by technology

However, in the existing chemical process based on neural network soft sensor modeling methods, there are generally complex algorithms, large data sets are required for learning and training, the sampling time of input and output variables is long, the learning time is long, and the learning cost is high. Problems such as fast and real-time attainment of high learning accuracy have caused the monitoring of important variables to fail to meet the relevant accuracy requirements of fine chemicals

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