Melt index online detection method based on subspace independent component regression model

An independent component and melt index technology, applied in electrical digital data processing, special data processing applications, instruments, etc., can solve the problems of difficult to achieve satisfactory results, difficult to capture all-round information, etc., to improve the accuracy of soft sensing, enhance robustness awesome effect

Inactive Publication Date: 2013-11-13
ZHEJIANG UNIV
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Problems solved by technology

However, due to the complexity of the polypropylene production process, it is often difficult for a single soft-sensing ...

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  • Melt index online detection method based on subspace independent component regression model
  • Melt index online detection method based on subspace independent component regression model
  • Melt index online detection method based on subspace independent component regression model

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Embodiment Construction

[0016] The invention aims at the problem of predicting the melt index of the polypropylene production process, and establishes a local subspace independent component regression analysis and an integrated model through key variables that are easy to measure in the process, and is used for online soft measurement of the melt index of the process.

[0017] The main steps of the technical solution adopted in the present invention are respectively as follows:

[0018] Step 1: Under each operating condition, collect the data of key variables in the polypropylene production process through the distributed control system and real-time database system: X={x i ∈ R m} i＝1,2,…,n . Among them, n is the number of samples, and m is the number of key variables. These data are stored in the historical database respectively, and some data are selected as samples for modeling.

[0019] Step 2: Obtain the melt index value corresponding to the sample used for modeling in the historical databas...

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Abstract

The invention discloses a melt index online detection method based on a subspace independent component regression model. The melt index online detection comprises the following steps of firstly selecting key variables influencing the variation of melt indexes as input variables, and utilizing values of the melt indexes obtained through laboratory analysis as output variables; by utilizing a subspace decomposition algorithm, based on variable contribution degree indexes, dividing modeling dataset into a plurality of subspaces, and respectively establishing an independent component regression analysis model for each subspace dataset; and integrating and synthesizing information of different sub models to realize online soft measurement of the melt indexes in a polypropylene production process. Compared with the traditional independent component regression analysis method, the melt index online detection method based on the subspace independent component regression model, which is disclosed by the invention, can improve the soft measurement estimation precision of the melt indexes in the polypropylene production process, and also improve the robustness of a soft measurement model.

Description

technical field [0001] The invention belongs to the field of soft sensor modeling and application in the chemical production process, and in particular relates to a soft sensor modeling and online detection method for polypropylene melt index based on a subspace independent component regression model. Background technique [0002] As an important material, polypropylene is widely used in many industries. In this production process, a very important indicator is the melt index. In the actual process, the measurement of this indicator is extremely difficult, and the current common method is to obtain it through offline measurement in the laboratory. Compared with the online real-time measurement method, the offline measurement of the melt index often takes 1-2 hours, which is very unfavorable for the closed-loop quality control of the polypropylene process. In order to improve the automation degree and product quality of polypropylene production process, online measurement of...

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Application Information

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IPC IPC(8): G06F19/00
Inventor 葛志强宋执环
Owner ZHEJIANG UNIV
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