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Incorporating setting information for maintenance‐free quality modeling of batch processes
Author(s) -
Ge Zhiqiang,
Song Zhihuan,
Gao Furong
Publication year - 2013
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.13864
Subject(s) - process (computing) , quality (philosophy) , computer science , variable (mathematics) , process modeling , batch processing , regression analysis , industrial engineering , production (economics) , process engineering , work in process , data mining , engineering , machine learning , operations management , mathematics , mathematical analysis , philosophy , epistemology , programming language , operating system , economics , macroeconomics
Typically, the operation condition of the batch process is changed frequently, following different recipes or manufacturing various production grades. For quality prediction purpose, the prediction model should also be updated or rebuilt, which leads to a significant model maintenance effort, especially for those processes which have various phases. To reduce such effort, a maintenance‐free method is proposed in this article, which incorporates the setting information of the batch process for modeling. The whole process variations are separated into two parts: setting information related and other quality related variations. By constructing a relationship between setting variables and other process variables, the data variations explained by the setting information can be efficiently removed. Then, a robust regression model connecting process variables to the quality variable is developed in different phases of the batch process. The feasibility and effectiveness of the proposed method is evaluated through an industrial injection molding process. © 2012 American Institute of Chemical Engineers AIChE J, 59: 772–779, 2013