We consider a batch process characterized by multiple, correlated process and product variables. The focus is on the identification of optimal process settings in the start-up period. Mathematical programming and multivariate statistical modelling are combined to solve the adjustment problem for the batch start-up. Partial least squares (PLS), a multivariate statistical approach, is used to model the relationship between process and product variables under good operating conditions. The optimal adjustment is identified by solving a mixed-integer quadratic program (MIQP) such that the recommended process settings are consistent with the PLS model. The start-up adjustment algorithm is operator-assisted; i.e. it uses input from the operator to compensate for unavailable but important process and product information. The operator's input is modelled as a constraint of the MIQP, and good production practices are also taken into account. The proposed adjustment algorithm is applied to the start-up period of a filament extrusion batch process and retrospective data indicate the effectiveness of the algorithm.
Manufacturing start-up problem solved by mixed-integer quadratic programming and multivariate statistical modelling
Reprints and Corporate Permissions
Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?
To request a reprint or corporate permissions for this article, please click on the relevant link below:
Academic Permissions
Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?
Obtain permissions instantly via Rightslink by clicking on the button below:
If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.
Related Research Data
Related research
People also read lists articles that other readers of this article have read.
Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.
Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.