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Making decisions about saving energy in compressed air systems using ambient intelligence and artificial intelligence

Making decisions about saving energy in compressed air systems using ambient intelligence and artificial intelligence
Making decisions about saving energy in compressed air systems using ambient intelligence and artificial intelligence
Compressed air systems are often the most expensive and inefficient industrial systems. For every 10 units of energy, less than 1 unit turns into useful compressed air. Air compressors tend to be kept fully on even if they are not (all) needed. The research proposed in this short paper will combinereal time ambient sensing with Artificial Intelligence andKnowledge Management to automatically improve efficiency in energy intensive manufacturing. The research will minimise energy use for air compressors based on real-time manufacturing conditions (and anticipated future requirements). Ambient datawill provide detailed information on performance. Artificial Intelligence will make sense of that data and automatically act. Knowledge Management will facilitate the processing of information to advise human operators on actions to reduce energy use and maintain productivity. The aim is to create new intelligent techniques to save energy in compressed air systems.
1229-1236
Springer
Sanders, David Adrian
54f2f76a-f55a-42d7-ac56-2e9fd8d92de4
Robinson, David Charles
51c83678-96b0-4a0b-a7cb-1c249596fc36
Hassan, Mohamed
ce323212-f178-4d72-85cf-23cd30605cd8
Haddad, Malik
cdc55972-df6f-492d-8ed0-b022e19b912f
Gegov, Alexander
1016bf16-9fdf-4cdb-b31c-f1b474bf1442
Ahmed, Nadia
fa8b6d1a-4487-4197-8699-4b07dee9537d
Arai, Kohei
Kapoor, Supriya
Bhatia, Rahul
Sanders, David Adrian
54f2f76a-f55a-42d7-ac56-2e9fd8d92de4
Robinson, David Charles
51c83678-96b0-4a0b-a7cb-1c249596fc36
Hassan, Mohamed
ce323212-f178-4d72-85cf-23cd30605cd8
Haddad, Malik
cdc55972-df6f-492d-8ed0-b022e19b912f
Gegov, Alexander
1016bf16-9fdf-4cdb-b31c-f1b474bf1442
Ahmed, Nadia
fa8b6d1a-4487-4197-8699-4b07dee9537d
Arai, Kohei
Kapoor, Supriya
Bhatia, Rahul

Sanders, David Adrian, Robinson, David Charles, Hassan, Mohamed, Haddad, Malik, Gegov, Alexander and Ahmed, Nadia (2019) Making decisions about saving energy in compressed air systems using ambient intelligence and artificial intelligence. Arai, Kohei, Kapoor, Supriya and Bhatia, Rahul (eds.) In Intelligent Systems and Applications: IntelliSys 2018. vol. 869, Springer. pp. 1229-1236 . (doi:10.1007/978-3-030-01057-7_92).

Record type: Conference or Workshop Item (Paper)

Abstract

Compressed air systems are often the most expensive and inefficient industrial systems. For every 10 units of energy, less than 1 unit turns into useful compressed air. Air compressors tend to be kept fully on even if they are not (all) needed. The research proposed in this short paper will combinereal time ambient sensing with Artificial Intelligence andKnowledge Management to automatically improve efficiency in energy intensive manufacturing. The research will minimise energy use for air compressors based on real-time manufacturing conditions (and anticipated future requirements). Ambient datawill provide detailed information on performance. Artificial Intelligence will make sense of that data and automatically act. Knowledge Management will facilitate the processing of information to advise human operators on actions to reduce energy use and maintain productivity. The aim is to create new intelligent techniques to save energy in compressed air systems.

Full text not available from this repository.

More information

e-pub ahead of print date: 8 November 2018
Published date: 2019
Additional Information: expected pp, 1313-1316

Identifiers

Local EPrints ID: 438263
URI: http://eprints.soton.ac.uk/id/eprint/438263
PURE UUID: b61fdeee-694e-4a84-a5ca-43ea75af3ca5
ORCID for Mohamed Hassan: ORCID iD orcid.org/0000-0003-3729-4543

Catalogue record

Date deposited: 04 Mar 2020 17:32
Last modified: 20 May 2020 01:03

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Contributors

Author: David Adrian Sanders
Author: David Charles Robinson
Author: Mohamed Hassan ORCID iD
Author: Malik Haddad
Author: Alexander Gegov
Author: Nadia Ahmed
Editor: Kohei Arai
Editor: Supriya Kapoor
Editor: Rahul Bhatia

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