Control method of penicillin production process based on cooperative training local weighted partial least squares (LWPLS)

Abstract

The invention discloses a control method of a penicillin production process of cooperative training and Local Weighted Partial Least Squares (LWPLS), and the control method is used for soft measurement modeling under the condition that the quantity modeling data is relatively small and realizing prediction of product information of a penicillin production process. According to the control method, an effective linear prediction model is established by using a cooperative training-based local weighted partial least squares learning method, the problem of low model precision under the condition that the quantity of sampling data of the penicillin production process is too small is overcome, and the predication accuracy and the performance of the model established directing at the process are improved, thereby enabling the penicillin production process to be more reliable, and enabling the product quality to be more stable.

Description

technical field [0001] The invention belongs to the field of penicillin production process prediction and control, and in particular relates to a soft sensor modeling method based on a small number of samples and using a collaborative training algorithm and a local weighted partial least squares algorithm. Background technique [0002] In the production process of penicillin, the detection and control of penicillin product concentration is of vital significance. Due to the cost of detection equipment, the difficulty of component detection, time lag and other factors, soft sensing methods are often used in the production process of penicillin to predict the concentration information of penicillin. In the industrial process, the important variables like the penicillin concentration are called the leading variables, and some other easily measurable variables are called the auxiliary variables. Soft measurement refers to the technical method of using auxiliary variables to pred...

AI Technical Summary

Problems solved by technology

However, when the number of training samples is small, the traditional multivariate statistical methods often fail to achieve effective prediction accuracy; in addition, the traditional multivariate statistical learning methods are often used Data that includes auxiliary variables and corresponding leading variable information, that is, what we call labeled data, and data that does not have corresponding leading variables that only contain auxiliary variable information, that is, what we call unlabeled data, is often ignored

Models built using existing labeled data are often not accurate enough, and unlabeled data with certain useful information is wasted. This shortcoming of traditional methods greatly limits their predictive effect

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