Support vector regression modeling in recursive just-in-time learning framework for adaptive soft sensing of naphtha boiling point in crude distillation unit  

作　　者：Venkata Vijayan S Hare Krishna Mohanta Ajaya Kumar Pani 

机构地区：[1]Department of Chemical Engineering,Birla Institute of Technology and Science,Pilani,Rajasthan,333031,India

出　　处：《Petroleum Science》2021年第4期1230-1239,共10页石油科学（英文版）

摘　　要：Prediction of primary quality variables in real time with adaptation capability for varying process conditions is a critical task in process industries.This article focuses on the development of non-linear adaptive soft sensors for prediction of naphtha initial boiling point(IBP)and end boiling point(EBP)in crude distillation unit.In this work,adaptive inferential sensors with linear and non-linear local models are reported based on recursive just in time learning(JITL)approach.The different types of local models designed are locally weighted regression(LWR),multiple linear regression(MLR),partial least squares regression(PLS)and support vector regression(SVR).In addition to model development,the effect of relevant dataset size on model prediction accuracy and model computation time is also investigated.Results show that the JITL model based on support vector regression with iterative single data algorithm optimization(ISDA)local model(JITL-SVR:ISDA)yielded best prediction accuracy in reasonable computation time.

关 键 词：Adaptive soft sensor Just in time learning Regression Support vector regression Naphtha boiling point 

分 类 号：TE626.9[石油与天然气工程—油气加工工程]