Input data management for energy related discrete event simulation modelling. (10th January 2017)

• Record Type: Journal Article
• Authors: Kouki, Meriem; Cardin, Olivier; Castagna, Pierre; Cornardeau, Celine
• Is Part Of: Journal of cleaner production. Volume 141(2017)
• Page Start: 194
• Page End: 207
• Publication Date: 2017-01-10
• Subjects: Discrete event simulation -- Energy consumption modelling -- Input data management -- Manufacturing system; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2016.09.061
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Any manufacturing activity creates product and energy flows whose management provides an overview of how a manufacturing system interacts with its environment. Recently, simultaneous management of these flows using discrete event simulation is increasingly being discussed in literature. However, the reliability of such projects basically depends on the method used to model energy consumption and quality of input data. Mindful of these challenges, this work reviews some existing energy related discrete-event simulation projects and discuss the issue of energy related input data management. Against this background, it proposes an operation-based concept to model the energy consumption of a manufacturing system and put an emphasis on how the load profiles could be acquired, processed and introduced into the simulation model, depending on its variabilities and the complexity of the associated energy-using equipment. The main proposal of this article is to introduce the possibility to model complex energy consumption behaviours of various pieces of equipment using stochastic distributions. The case of an injection moulding system is presented to validate the proposed method and to evaluate its accuracy. A comparison with actual measurements of energy consumption shows a deviation of less than 3% of the discrete-event simulation model. Highlights: An operation-based approach to model energy consumption is proposed. The work mainly focuses on energy input data management. Specify how load profile should be modelled, how measured data should be processed. A load profile is modelled by energy blocks with deterministic or stochastic values. The deviation between the real and the simulated energy consumption is less than 3%.