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Download fileAn intelligent real-time cyber-physical toolset for energy and process prediction and optimisation in the future industrial Internet of Things
journal contribution
posted on 2017-10-26, 08:40 authored by Sarogini PeaseSarogini Pease, Russell Trueman, Callum Davies, Jude Grosberg, Kai Hin Yau, Navjot Kaur, Paul ConwayPaul Conway, Andrew WestAndrew WestEnergy 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.
History
School
- Mechanical, Electrical and Manufacturing Engineering
Published in
Future Generation Computer SystemsVolume
79Issue
Part 3Pages
815 - 829Citation
PEASE, S.G. ...et al., 2018. 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, 79(3), pp. 815-829.Publisher
© ElsevierVersion
- AM (Accepted Manuscript)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Acceptance date
2017-09-10Publication date
2017-10-03Notes
This paper was accepted for publication in the journal Future Generation Computer Systems and the definitive published version is available at https://doi.org/10.1016/j.future.2017.09.026ISSN
0167-739XPublisher version
Language
- en