Robust integrated production-maintenance scheduling for an evaporation network

•Coordination of production and maintenance improves resource efficiency.•Long-term fouling effects are included to achieve optimal operation.•A two-stage stochastic approach is used to tackle uncertainty.•Surrogate plant models allow the optimization is solved in real time.•A further analysis is pr...

Full description

Saved in:
Bibliographic Details
Published in:Computers & chemical engineering Vol. 110; pp. 140 - 151
Main Authors: Palacín, C.G., Pitarch, J.L., Jasch, C., Méndez, C.A., de Prada, C.
Format: Journal Article
Language:English
Published: Elsevier Ltd 02-02-2018
Subjects:
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Coordination of production and maintenance improves resource efficiency.•Long-term fouling effects are included to achieve optimal operation.•A two-stage stochastic approach is used to tackle uncertainty.•Surrogate plant models allow the optimization is solved in real time.•A further analysis is provided through offline multi-objective optimization. This work aims to reduce the global resource consumption in an industrial evaporation network by better tasks management and coordination. The network works in continuous, processing some products in several evaporation plants, so optimal load allocation and product-plant assignment problems appear. The plants have different features (capacity, equipment, etc.) and their performance is affected by fouling inside the heat exchangers and external factors. Hereby, the optimizer has to decide when maintenance operations have to be triggered. Therefore, a mixed production/maintenance scheduling problem arises. The plant behavior is approximated by surrogate linear models obtained experimentally, allowing thus the use of mixed-integer linear optimization routines to obtain solutions in acceptable time. Furthermore, uncertainty in the weather forecast and in the production plan is also considered via a two-stage stochastic programming approach. Finally, a trade-off analysis between other objectives of interest is given to support the decision maker.
ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2017.12.005