Large chemical process distributed modeling method based on CCA-PLS

Abstract

The invention discloses a large chemical process distributed modeling method based on CCA-PLS. All process variables are collected in the large-scale chemical process, output variables are determined according to a process quality index, feature components of the process variables are extracted by the adoption of a canonical correlation analysis method, the largest association coefficients between each output variable and all input variables and corresponding main axis vectors are calculated according to the feature components, independent input variables and interaction input variables of each subsystem are selected according to the absolute values of components of the main axis vectors, and accordingly a large system decomposition is achieved. Modeling is carried out on the subsystems by means of a PLS algorithm after subsystem division, components which enable the covariance between the input variables and the output variables to be the largest are extracted, and a subsystem model is obtained through a regression modeling technology. The large chemical process distributed modeling method has the advantages that only input and output data in the process are used, input variable screening of the subsystems is carried out by means of the canonical correlation analysis principle, the model dimension number is lowered, the model structure is simplified, subsystem modeling is carried out through the PLS modeling algorithm, difficulties in calculation caused by a large number of collinear variables in actual application are eliminated, and modeling precision is high.

Description

technical field [0001] The invention belongs to the technical field of chemical process modeling and relates to a large chemical process distributed modeling technology based on canonical correlation analysis (CCA) and partial least squares (PLS). Background technique [0002] With the advancement of technology and people's pursuit of economic interests, the scale of chemical production process is getting larger and more complex. A process is usually composed of many subsystems interacting through material, energy and information flow. There are complex correlations between subsystems, and there are variability and uncertainty in the external environment and internal disturbances. The traditional centralized predictive control (Centralized MPC) has encountered difficulties in object modeling and optimization calculations [1], and has been unable to meet the Industrial production development needs. The above-mentioned new characteristics of chemical process, coupled with the...

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Problems solved by technology

[0013] The purpose of the present invention is to provide a data-driven distributed modeling algorithm for large chemical process, which does not require knowledge of process mechanism, only based on production data analysis for subsystem division and dimensionality reduction, and further establishes a subsystem model to solve the current problem of large chemical process The problem that the complexity and accuracy of the model structure cannot be taken into account provides a model basis for distributed predictive control and a simple and feasible method for modeling the actual large-scale chemical process

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