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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/27124

Title: An intelligent real-time cyber-physical toolset for energy and process prediction and optimisation in the future industrial internet of things
Authors: Pease, Sarogini G.
Trueman, Russell
Davies, Callum
Grosberg, Jude
Yau, Kai Hin
Kaur, Navjot
Conway, Paul P.
West, Andrew A.
Keywords: Wireless networks
Real-time systems
Energy efficiency
Energy management
Process planning
Issue Date: 2017
Publisher: © Elsevier
Citation: PEASE, S.G. ...et al., 2017. An intelligent real-time cyber-physical toolset for energy and process prediction and optimisation in the future industrial internet of things. Future Generation Computer Systems, In Press.
Abstract: Energy waste significantly contributes to increased costs in the automotive manufacturing industry, which is subject to energy usage restrictions and taxation from national and international policy makers and restrictions and charges from national energy providers. For example, the UK Climate Change Levy, charged to businesses at 0.554p/kWh equates to 7.28% of a manufacturing business’s energy bill based on an average total usage rate of 7.61p/kWh. Internet of Things (IoT) energy monitoring systems are being developed, however, there has been limited consideration of services for efficient energy-use and minimisation of production costs in industry. This paper presents the design, development and validation of a novel, adaptive Cyber-Physical Toolset to optimise cumulative plant energy consumption through characterisation and prediction of the active and reactive power of three-phase industrial machine processes. Extensive validation has been conducted in automotive manufacture production lines with industrial three-phase Hurco VM1 computer numerical control (CNC) machines.
Description: This paper is in closed access until 3rd October 2018.
Version: Accepted for publication
DOI: 10.1016/j.future.2017.09.026
URI: https://dspace.lboro.ac.uk/2134/27124
Publisher Link: https://doi.org/10.1016/j.future.2017.09.026
ISSN: 0167-739X
Appears in Collections:Closed Access (Mechanical, Electrical and Manufacturing Engineering)

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