Publication detail
Markov Decision Process to Optimise Long-term Asset Maintenance and Technologies Investment in Chemical Industry
Chin, H.H. Wang, B. Varbanov, P.S. Klemeš, J.J.
English title
Markov Decision Process to Optimise Long-term Asset Maintenance and Technologies Investment in Chemical Industry
Type
book chapter
Language
en
Original abstract
The decisions on synthesising a process network are often to optimise the payback periods based on investment cost. In addition to the core investment and the cost of used resources, the long-term reliable operation of the process is also crucial. Given available states and technologies of the assets, this study aims to identify the long-term optimal asset planning policy. Markov Decision Process (MDP) is a promising tool in identifying the optimal policy under different states of the assets or equipment. The failure probability of the unit is modelled with the ‘bathtub’ model and each of the condition states are incorporated in the MDP. The decisions to implement the redundant units in the process with variety of technologies are allowed. This paper applied the MDP into an equivalent Mixed Integer Non-linear Programming (MINLP) to solve for the optimal long-term assets decision and the maintenance policy. The applicability of the method is tested on a real case study from Sinopec Petrochemical Plant. The capital and expected operational cost that accounts for equipment maintenance for an infinite time horizon are determined.
English abstract
The decisions on synthesising a process network are often to optimise the payback periods based on investment cost. In addition to the core investment and the cost of used resources, the long-term reliable operation of the process is also crucial. Given available states and technologies of the assets, this study aims to identify the long-term optimal asset planning policy. Markov Decision Process (MDP) is a promising tool in identifying the optimal policy under different states of the assets or equipment. The failure probability of the unit is modelled with the ‘bathtub’ model and each of the condition states are incorporated in the MDP. The decisions to implement the redundant units in the process with variety of technologies are allowed. This paper applied the MDP into an equivalent Mixed Integer Non-linear Programming (MINLP) to solve for the optimal long-term assets decision and the maintenance policy. The applicability of the method is tested on a real case study from Sinopec Petrochemical Plant. The capital and expected operational cost that accounts for equipment maintenance for an infinite time horizon are determined.
Keywords in English
Markov Decision Process;Asset Optimisation;Reliability;Maintenance Planning;Mixed Integer Non-linear Programming (MINLP)
Released
25.06.2021
Publisher
Elsevier Ltd.
ISBN
9780323885065
ISSN
1570-7946
Book
31st European Symposium on Computer Aided Process Engineering
Number
50
Pages from–to
1853–1858
Pages count
6
BIBTEX
@inbook{BUT172208,
author="Hon Huin {Chin} and Bohong {Wang} and Petar Sabev {Varbanov} and Jiří {Klemeš},
title="Markov Decision Process to Optimise Long-term Asset Maintenance and Technologies Investment in Chemical Industry",
booktitle="31st European Symposium on Computer Aided Process Engineering",
year="2021",
number="50",
month="June",
pages="1853--1858",
publisher="Elsevier Ltd.",
isbn="9780323885065",
issn="1570-7946"
}