Sliding window time difference-Gaussian process regression modeling method based on local time lag reconstruction

A technology of Gaussian process regression and modeling method, which is applied in the field of complex industrial process modeling and soft sensing, and can solve problems such as time lag of leading variable and time difference of sliding window

Active Publication Date: 2016-11-23
JIANGNAN UNIV
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Problems solved by technology

[0009] In order to solve the above-mentioned technical problems, the object of the present invention is to provide a TDGPR model that does not need to wait for the arrival of new input data, but only needs to use the corresponding time-delay reconstruction samples as model input, so as to obtain the real-time predicted value of the leading variable in advance , to a certain extent solve the problem of leading variables with time lag Sliding window time difference-Gaussian process regression modeling method based on local time-delay reconstruction

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  • Sliding window time difference-Gaussian process regression modeling method based on local time lag reconstruction
  • Sliding window time difference-Gaussian process regression modeling method based on local time lag reconstruction
  • Sliding window time difference-Gaussian process regression modeling method based on local time lag reconstruction

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[0042] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0043] combine figure 1 Shown modeling flow schematic diagram and 2 actual industrial process cases, the present invention is described in further detail:

[0044] Industrial Case 1: Sulfur Recovery Unit Process

[0045] The sulfur recovery unit (Sulfur recovery unit, SRU) is an important part of the refinery treatment system, responsible for the treatment of sulfur-containing gases (such as H 2 S and SO 2 ) in order to avoid causing great harm to the environment, a brief schematic diagram of the specific reaction process flow is as follows figure 2 As shown, the process has 5 auxiliary variables and 2 leading variables. In the source of process data, 7 variables can ...

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Abstract

The invention relates to a sliding window time difference-Gaussian process regression modeling method based on local time lag reconstruction. The method is suitable for chemical processes with time lag, nonlinear and time-variant characteristics. By means of the method, the latest process time-variant dynamic state can be tracked step by step through a sliding window strategy; meanwhile, parameters are extracted from process periodical time lag characteristics through a fuzzy curve analysis method in a sliding window and used for time lag reconstruction of local model training samples and testing samples; the variable drift characteristics on the locally-reconstructed sliding window are depicted through a time difference-Gaussian process regression (TDGPR) model. An effective real-time prediction and control technical support means is provided for the industrial process, product quality can be easily improved, production cost is controlled, and potential safety hazards are avoided.

Description

technical field [0001] The invention relates to a soft sensor modeling method of a sliding window time-difference Gaussian process regression (LTR-MWTDGPR) based on local time-delay reconstruction, and belongs to the field of complex industrial process modeling and soft sensor. Background technique [0002] With the continuous strengthening of modern industrial processes for product quality control and optimization requirements, the online measurement technology of process variables also puts forward higher requirements accordingly. In many practical applications, quality-related variables (such as gas flow concentration, the content of certain chemical components in products, etc.) are often difficult to measure online, but they need to be obtained in a timely manner. In this context, soft-sensing technology emerged as the times require. It infers the real-time value of difficult-to-measure leading variables by constructing a mathematical model of auxiliary variable sets an...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06Q10/04
CPCG06Q10/04G06F30/367
Inventor 熊伟丽李妍君刘登峰张丽萍徐保国
Owner JIANGNAN UNIV
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