Multi-output Gaussian Process Regression Model with Combined Kernel Function for Polyester Esterification Processes

• Published in: 东华大学学报（英文版） Vol. 40; no. 1; pp. 27 - 33
• Main Authors: WANG Hengqian, GENG Junxian, CHEN Lei
• Format: Journal Article
• Language: English
• Published: College of Information Science and Technology,Donghua University,Shanghai 201620,China 2023; Engineering Research Center of Digitized Textile & Apparel Technology,Ministry of Education,Donghua University,Shanghai 201620,China
• Subjects: multi-output Gaussian process regression; seasonal and trend decomposition using loess(STL); combined kernel function; polyester esterification process
• DOI: 10.19884/j.1672-5220.202111009
• ISSN: 1672-5220

TQ342%TP181; In polyester fiber industrial processes,the prediction of key performance indicators is vital for product quality.The esterification process is an indispensable step in the polyester polymerization process.It has the characteristics of strong coupling,nonlinearity and complex mechanism.To solve these problems,we put forward a multi-output Gaussian process regression(MGPR)model based on the combined kernel function for the polyester esterification process.Since the seasonal and trend decomposition using loess(STL)can extract the periodic and trend characteristics of time series,a combined kernel function based on the STL and the kernel function analysis is constructed for the MGPR.The effectiveness of the proposed model is verified by the actual polyester esterification process data collected from fiber production.