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Projects: Projects for Investigator
Reference Number	NIA_SGN0120
Title	Universal Temperature Controller using Artificial Intelligence
Status	Completed
Energy Categories	Fossil Fuels: Oil Gas and Coal(Oil and Gas, Refining, transport and storage of oil and gas) 100%;
Research Types	Applied Research and Development 100%
Science and Technology Fields	PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 75%; ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering) 25%;
UKERC Cross Cutting Characterisation	Not Cross-cutting 100%
Principal Investigator	Project Contact No email address given SGN
Award Type	Network Innovation Allowance
Funding Source	Ofgem
Start Date	01 March 2018
End Date	01 March 2019
Duration	ENA months
Total Grant Value	£57,700
Industrial Sectors	Energy
Region	South East
Programme	Network Innovation Allowance
Investigators	Principal Investigator	Project Contact , SGN (100.000%)
	Industrial Collaborator	Project Contact , SGN (0.000%)
Web Site	https://smarter.energynetworks.org/projects/NIA_SGN0120
Objectives	This project will review the feasibility of a universal preheat controller to act as an intermediary between a traditional station exit temperature signal and an installed preheat asset. The controller will be programmed to learn and anticipate site specific dynamics through a desktop exercise learning stage using historical site operating data. The desktop exercise will explore patterns of energy usage and to identify predictable and unpredictable patterns using historic site operation data. This data will be used to identify process optimisation opportunities, bottlenecks and act as a training tool for an Artificial Intelligence (AI) based learning and control algorithm. In parallel a hardware specification of a universal controller which meet safe operating requirements, supporting processing needs of the AI software and offer potential for retrofits across existing network preheat assets.  This project aims to assess the potential application of a universal controller based on AI to act as an intermediary between a traditional station exit temperature signal and an installed preheat asset. By using AI combined with historical data, it is hoped that it will be possible to recognise patterns and learn to predict upcoming events such as variations in demand, and impact of environmental variables such as temperature and humidity. This project will include: Teaching the AI controller: Select a sample of 3 sites with different operating characteristics. Use data mining techniques to identify process bottlenecks, energy saving opportunities and process variability issues. Train the AI to assess performance with the minimum number of inputs for maximum potential application (Inlet Pressure, Outlet Pressure, Station Outlet Temperature, Historic Weather). Development of a virtual system expert to advise on “optimal operation” to help guide process performance from past operating data. Optimise computation for data efficiency. Assess reliability and robustness for suitability on critical infrastructure. Hardware Specification: Determine computation requirement (Central Processing Unit (CPU)/ Graphic Processing Unit (GPU). Develop physical specifications (considering any restrictions). Input / Output specifications and protocols. Review SGN specific requirements / Applicable standards. Determine power requirements. Review connectivity (Global System for Mobile Communications (GSM)/ Wi-Fi etc.). Determine appropriate security & cyber-security protocols. Remote update and control. Implementation: Development of a project implementation roadmap for site integration.
Abstract	This project aims to assess the potential application of a universal controller based on Artifical Intelligence (AI) to act as an intermediary between a traditional station exit temperature signal and an installed preheat asset.
Publications	(none)
Final Report	(none)
Added to Database	08/11/22