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A new method of chemical process quality prediction

A chemical process and quality prediction technology, applied in the field of automation, can solve problems such as inability to deal with nonlinear processes and high modeling complexity, and achieve the effects of improving interpretation ability, strengthening prediction performance, and reducing complexity

Inactive Publication Date: 2019-01-18
HANGZHOU DIANZI UNIV
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  • Claims
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AI Technical Summary

Problems solved by technology

[0003] Aiming at the problems that the traditional partial least squares algorithm cannot handle the nonlinear process and the modeling complexity is relatively high, the present invention proposes a new type of chemical process quality prediction method

Method used

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  • A new method of chemical process quality prediction
  • A new method of chemical process quality prediction
  • A new method of chemical process quality prediction

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Embodiment Construction

[0053] The present invention will be further described below in conjunction with embodiment.

[0054] Take the Tennessee-Eastman process as an example:

[0055] The Tennessee-Eastman process consists of five parts: reactor, condenser, separator, compressor and stripper, and contains 12 operating variables and 41 measured variables. There are 21 failure variables.

[0056] Step 1. Collect sensor data in the chemical process, process it and establish a new process prediction model.

[0057] The specific steps are:

[0058] 1.1 The data in the chemical process is collected for offline modeling. The data is divided into two categories, process data X and quality data Y. There are a total of N samples.

[0059] X=[x 1 ,x 2 ,...x m ],x 1 ,x 2 …x m ∈R N×1

[0060] Y=[y 1 ,y 2 ,...y p ],y 1 ,y 2 ...y p ∈R N×1

[0061] where x 1 ,x 2 ,...x m etc. respectively represent the reactant concentration, pressure, temperature, valve opening and other variables in the Tenn...

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Abstract

The invention discloses a novel chemical process quality prediction method, comprising the following steps: step 1, collecting sensor data in the chemical process, processing and establishing a novelprocess prediction model; Step 2: Collecting the newly obtained data during the chemical process operation, and using the prediction model obtained in step 1 to carry out on-line prediction. First, the orthogonal signal correction algorithm is used to deal with the historical data collected from the process, and then the prediction model is established by using the processed data. In order to improve the ability of the model to deal with nonlinear data, support vector machine is introduced into the prediction model. This method improves the disadvantage of the traditional prediction model thatthe modeling process is complex and can not deal with the nonlinear data, and improves the ability of the model to track the real data.

Description

technical field [0001] The invention belongs to the technical field of automation and relates to a novel chemical process quality prediction method. Background technique [0002] As the modern chemical process continues to develop towards large-scale, integrated, and complex directions, more and more sensors are used in the chemical process, and more process information can be collected. Even so, in the chemical process control system, there are still some important quality variables that cannot be measured in real time. Failure to know the values ​​of these quality variables can seriously affect the quality of the product and the safety of production. A major goal of chemical process data analysis is to build a regression model based on experimental data or historical data, and then use the built model to predict product quality. The high-latitude nature of process data makes it difficult to accurately measure product quality, which leads to the development of multivariat...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/04
CPCG06Q10/04G06Q10/06395G06Q50/04Y02P90/30
Inventor 张日东李翔
Owner HANGZHOU DIANZI UNIV
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