Intermittent industrial process online quality prediction method based on JY-MKPLS

Abstract

The invention discloses an intermittent industrial process online quality prediction method based on JY-MKPLS. According to the method, two-dimensional input matrixes X and X and two-dimensionaloutput matrixes Y and Y are obtained through three-dimensional input data of a process a and a process b; standardization processing is performed; the matrixes are projected from a low-dimensional original space to a high-dimensional characteristic space F, and kernel matrixes K and K are calculated in the high-dimensional characteristic space; the kernel matrixes K and K are standardized; the JY-MKPLS algorithm is run; load matrixes of K<ai> and K<bi> are calculated; the steps are repeated till A principal elements are extracted; a score matrix T and a load matrix P of an input data matrix K and a score matrix U and a load matrix Q of an output data matrix Y are calculated; batch process quality prediction is performed; latest output data y<new> is obtained on line, anda prediction error beta<n> of a current batch is calculated; a model prediction error is checked; newly generated a-process data is used for replacing the model prediction error; and model updating is performed. Through the method, a new process prediction model with high precision can be quickly established, modeling efficiency and prediction precision can be improved, and the operation cost ofan enterprise is effectively controlled.

Description

technical field [0001] The invention belongs to the field of quality prediction of industrial production batch process, in particular to an online quality prediction method of batch industrial process based on JY-MKPLS. Background technique [0002] The batch process is generally used to produce small batches, multiple types, and high value-added products to meet the current market demand for products with many varieties and high specifications, so it plays a very important role in modern industrial production. In an industrial process, product quality has always been the focus of people's attention, because product quality not only affects the economic benefits of an enterprise, but also affects its reputation. However, in the actual batch industrial process production, the product quality can be obtained through offline measurement only after a batch of production is completed, and the sampling period of the offline measurement method is generally long, that is, the lag ti...

AI Technical Summary

Problems solved by technology

However, if the batch process is a new process that has not been put into use or is in use soon, and its process data is scarce, according to the conventional data-based modeling method, only a small amount of new process data can not guarantee the prediction accuracy. production experiments need to be designed to obtain enough new process data

However, this will not only increase the input cost and bring a long lag time, but also the modeling efficiency is low, which makes the quality prediction lose its meaning, which is not conducive to the real-time adjustment of the production strategy and the expansion of the production scale, and seriously slows down the optimization of production operation. speed

In addition, the actual industrial production process is often nonlinear, and due to the existence of disturbance and other factors, the data distribution will appear uneven and uneven.

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