Optimization of Preventive Maintenance Scheduling in Processing Plants
18th European Symposium on Computer Aided Process Engineering – ESCAPE 18 Bertrand Braunschweig and Xavier Joulia (Editors) © 2008 Elsevier B.V./Ltd. All rights reserved.
Optimization of Preventive Maintenance Scheduling in Processing Plants
DuyQuang Nguyen and Miguel Bagajewicz
The University of Oklahoma, R. T-335 SEC, 100 E. Boyd, Norman, OK 73019, USA
Abstract
A new methodology designed to optimize both the planning of preventive maintenance and the amount of resources needed to perform maintenance in a process plant is presented. The methodology is based on the use of a Montecarlo simulation to evaluate the expected cost of maintenance as well as the expected economic loss, an economical indicator for maintenance performance. The Montecarlo simulation describes different failure modes of equipment and uses the prioritization of maintenance supplied, the availability of labour and spare parts. A Genetic algorithm is used for optimisation. The well-known Tennessee Eastman Plant problem is used to illustrate the results. Keywords: Preventive maintenance, Maintenance optimization, Montecarlo simulation
1. Introduction
Maintenance can be defined as all actions appropriate for retaining an item/part/equipment in, or restoring it to a given condition (Dhillon, 2002). More specifically, maintenance is used to repair broken equipments, preserve equipment conditions and prevent their failure, which ultimately reduces production loss and downtime as well as the environmental and the associated safety hazards. It is estimated that a typical refinery experiences about 10 days downtime per year due to equipment failures, with an estimated economic lost of $20,000-$30,000 per hour (Tan and Kramer, 1997). In the age of high competition and stringent environmental and safety regulations, the perception for maintenance has been shifted from a “necessary evil” to an effective tool to increase profit, from a supporting part to an integrated part of the production
Optimization of Preventive Maintenance Scheduling in Processing Plants
DuyQuang Nguyen and Miguel Bagajewicz
The University of Oklahoma, R. T-335 SEC, 100 E. Boyd, Norman, OK 73019, USA
Abstract
A new methodology designed to optimize both the planning of preventive maintenance and the amount of resources needed to perform maintenance in a process plant is presented. The methodology is based on the use of a Montecarlo simulation to evaluate the expected cost of maintenance as well as the expected economic loss, an economical indicator for maintenance performance. The Montecarlo simulation describes different failure modes of equipment and uses the prioritization of maintenance supplied, the availability of labour and spare parts. A Genetic algorithm is used for optimisation. The well-known Tennessee Eastman Plant problem is used to illustrate the results. Keywords: Preventive maintenance, Maintenance optimization, Montecarlo simulation
1. Introduction
Maintenance can be defined as all actions appropriate for retaining an item/part/equipment in, or restoring it to a given condition (Dhillon, 2002). More specifically, maintenance is used to repair broken equipments, preserve equipment conditions and prevent their failure, which ultimately reduces production loss and downtime as well as the environmental and the associated safety hazards. It is estimated that a typical refinery experiences about 10 days downtime per year due to equipment failures, with an estimated economic lost of $20,000-$30,000 per hour (Tan and Kramer, 1997). In the age of high competition and stringent environmental and safety regulations, the perception for maintenance has been shifted from a “necessary evil” to an effective tool to increase profit, from a supporting part to an integrated part of the production
References: Bloch H.P and Geitner F.K. (2006). Maximizing Machinery Uptime, Elsevier, MA, USA. Center for Chemical Process Safety, AIChE (1989). Guidelines for Process Equipment Reliability Data with Data Tables, ISBN 0816904227 Dhillon B.S. 2002. Engineering Maintenance, CRC Press, Boca Raton, USA. Nguyen D.Q, C. Brammer and M. Bagajewicz. (2008). A New Tool for the Evaluation of the Scheduling of Preventive Maintenance for Chemical Process Plants. Industrial and Engineering Chemistry Research, To appear. Ricker N.L and Lee J.H. (1995). Nonlinear Modeling and State Estimation for the Tennessee Eastman Challenge Process. Comput. Chem. Eng., 19(9), 983-1005. Shum, Y.S and Gong, D.C. (2006). The Application of Genetic Algorithm in the Development of Preventive Maintenance Analytic Model. The International Journal of Advanced Manufacturing Technology. Vol. 32, pp.169-183. Saranga. H. (2004). Opportunistic Maintenance Using Genetic Algorithms. Journal of Quality in Maintenance Engineering, 10(1), pp. 66-74. Tan J.S. and Kramer M.A. (1997). A General Framework For Preventive Maintenance Optimization In Chemical Process Operations. Computers and Chemical Engineering, 21(12), pp. 1451-1469. Tischuk, John L. (2002). The Application of Risk Based Approaches to Inspection Planning. Tischuk Enterprises (UK). Wang H. and Pham H. (2006). Reliability and Optimal Maintenance, Springer Series in Reliability Engineering, Springer-Verlag, London. Wang H. (2002). A survey of maintenance policies of deteriorating systems. European Journal of Operational Research, 139(3), pp. 469-489.