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An intelligent real-time cyber-physical toolset for energy and process prediction and optimisation in the future industrial internet of things

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journal contribution
posted on 26.10.2017 by Sarogini Pease, Russell Trueman, Callum Davies, Jude Grosberg, Kai Hin Yau, Navjot Kaur, Paul Conway, Andrew West
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.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Future Generation Computer Systems

Citation

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

© Elsevier

Version

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

03/10/2017

Publication date

2018

Notes

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.2017.09.026

ISSN

0167-739X

Language

en

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