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Modelling method of data‐driven model combined with a priori knowledge and its application in average particle size estimation of composite colloidal sols
Author(s) -
Zhou Yang,
Li Shaojun
Publication year - 2021
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.23856
Subject(s) - a priori and a posteriori , composite number , particle size , colloidal particle , estimation , particle (ecology) , materials science , computer science , colloid , biological system , composite material , engineering , chemical engineering , geology , systems engineering , philosophy , oceanography , epistemology , biology
A universally applicable hybrid modelling method is proposed for nonlinear industrial processes that combine the a priori process knowledge with a data‐driven model. This method constructs a unified framework for the modelling process by integrating a data‐driven modelling technique, sampling detection technique, constraint optimization problem, and an evolutionary algorithm. In the modelling process, a swarm intelligence algorithm is used to optimize the model parameters under the circumstances of satisfying the constraints of a priori knowledge. By adding the constraints of process a priori knowledge, more information can be obtained about the actual process and the over‐fitting problem can be avoided to some extent, especially when modelling a system with a small quantity of samples. In order to show the effectiveness of the method proposed in this paper, two general data‐driven models, the polynomial regression model and radial basis function network model, are used as case studies. Moreover, a function simulation experiment is designed to test effectiveness, and applied to estimate average particle size of ZrO2‐TiO2 composite colloidal sols.