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|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.|
Yau, Kai Hin
Conway, Paul P.
West, Andrew A.
|Keywords: ||Wireless networks|
|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|
|Publisher Link: ||https://doi.org/10.1016/j.future.2017.09.026|
|Appears in Collections:||Closed Access (Mechanical, Electrical and Manufacturing Engineering)|
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