Intelligent energy management of compressed air systems

Mohamad Thabet; David Sanders; Victor Becerra; Giles Tewkesbury; Malik Haddad; Tom Barker

doi:10.1109/IS48319.2020.9199977

Intelligent energy management of compressed air systems

Mohamad Thabet, David Sanders, Victor Becerra, Giles Tewkesbury, Malik Haddad, Tom Barker

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

This work considers the use of real time sensing and AI (machine learning) to increase Compressed Air Systems (CAS) efficiency. Algorithms that automate the detection of energy inefficiencies and make decisions regarding suitable troubleshooting procedure will be created. Systems using compressed air are often inefficient and expensive to operate. Less than one unit of energy is turned into useful compressed air for every ten provided. Intelligent systems will be used to reduce energy consumption in compressors by considering real-time circumstances and the predicted needs. Sensor data will deliver information about the real time performance. AI will interpret the data and then act automatically. New intelligent techniques will be applied to save energy. This paper presents a review of the recent literature covering the topic of CAS energy efficiency. Some gaps in research were identified in the area of developing technologies and methods to detect and treat energy inefficiencies in CAS.

Original language	English
Title of host publication	2020 IEEE 10th International Conference on Intelligent Systems (IS)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	153-158
ISBN (Electronic)	978-1-7281-5456-5
ISBN (Print)	978-1-7281-5457-2
DOIs	https://doi.org/10.1109/IS48319.2020.9199977
Publication status	Published - 18 Sept 2020
Event	2020 IEEE 10th International Conference on Intelligent Systems - Varna, Bulgaria Duration: 28 Aug 2020 → 30 Aug 2020

Publication series

Name	IEEE IS Proceedings Series
Publisher	IEEE
ISSN (Print)	1541-1672

Conference

Conference	2020 IEEE 10th International Conference on Intelligent Systems
Country/Territory	Bulgaria
City	Varna
Period	28/08/20 → 30/08/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/IS48319.2020.9199977

Intelligent_Energy_Management
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Accepted author manuscript (Post-print), 322 KB

COFUND / DTA3 Marie Skłodowska-Curie PhD Fellowship "Making decisions about saving energy in Compressed Air Systems using Ambient Intelligence and AI"
Sanders, D., Tewkesbury, G. & Bausch, N.
1/04/19 → 31/03/22
Project: Research

File
DTA3/Cofund Marie Skłodowska-curie Phd Fellowship Programme
Becerra, V.
1/10/18 → 30/09/21
Project: Research
Management and Business Decision Making
Haddad, M. & Sanders, D.
26/09/16 → 29/04/22
Project: Research

Cite this

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title = "Intelligent energy management of compressed air systems",

abstract = "This work considers the use of real time sensing and AI (machine learning) to increase Compressed Air Systems (CAS) efficiency. Algorithms that automate the detection of energy inefficiencies and make decisions regarding suitable troubleshooting procedure will be created. Systems using compressed air are often inefficient and expensive to operate. Less than one unit of energy is turned into useful compressed air for every ten provided. Intelligent systems will be used to reduce energy consumption in compressors by considering real-time circumstances and the predicted needs. Sensor data will deliver information about the real time performance. AI will interpret the data and then act automatically. New intelligent techniques will be applied to save energy. This paper presents a review of the recent literature covering the topic of CAS energy efficiency. Some gaps in research were identified in the area of developing technologies and methods to detect and treat energy inefficiencies in CAS.",

author = "Mohamad Thabet and David Sanders and Victor Becerra and Giles Tewkesbury and Malik Haddad and Tom Barker",

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Thabet, M, Sanders, D , Becerra, V , Tewkesbury, G, Haddad, M & Barker, T 2020, Intelligent energy management of compressed air systems. in 2020 IEEE 10th International Conference on Intelligent Systems (IS). IEEE IS Proceedings Series, Institute of Electrical and Electronics Engineers Inc., pp. 153-158, 2020 IEEE 10th International Conference on Intelligent Systems, Varna, Bulgaria, 28/08/20. https://doi.org/10.1109/IS48319.2020.9199977

Intelligent energy management of compressed air systems. / Thabet, Mohamad; Sanders, David ; Becerra, Victor et al.
2020 IEEE 10th International Conference on Intelligent Systems (IS). Institute of Electrical and Electronics Engineers Inc., 2020. p. 153-158 (IEEE IS Proceedings Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Haddad, Malik

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AB - This work considers the use of real time sensing and AI (machine learning) to increase Compressed Air Systems (CAS) efficiency. Algorithms that automate the detection of energy inefficiencies and make decisions regarding suitable troubleshooting procedure will be created. Systems using compressed air are often inefficient and expensive to operate. Less than one unit of energy is turned into useful compressed air for every ten provided. Intelligent systems will be used to reduce energy consumption in compressors by considering real-time circumstances and the predicted needs. Sensor data will deliver information about the real time performance. AI will interpret the data and then act automatically. New intelligent techniques will be applied to save energy. This paper presents a review of the recent literature covering the topic of CAS energy efficiency. Some gaps in research were identified in the area of developing technologies and methods to detect and treat energy inefficiencies in CAS.

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Intelligent energy management of compressed air systems

Abstract

Publication series

Conference

UN SDGs

Access to Document

Fingerprint

Projects

COFUND / DTA3 Marie Skłodowska-Curie PhD Fellowship "Making decisions about saving energy in Compressed Air Systems using Ambient Intelligence and AI"

DTA3/Cofund Marie Skłodowska-curie Phd Fellowship Programme

Management and Business Decision Making

Cite this