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Management of Compressed Air to Reduce Energy Consumption Using Intelligent Systems

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Intelligent Systems and Applications (IntelliSys 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1252))

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Abstract

This research investigated the use of intelligent systems for reducing energy consumption in compressed air systems. An initial literature review has been completed and mathematical models that describe typical compressed air components (compressor, tank, piping network, etc.) were created. The investigations suggested that energy used or wasted in connection with compressed air was a valuable research area to attempt to save energy. The research progressed to investigating ways of minimising energy use for air compressors based on real-time conditions (including anticipated future requirements), using intelligent systems to monitor and make decisions.

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Abbreviations

P comp :

Compressor input power

P air in :

Absolute air pressure at compressor inlet

Vdot air :

Volumetric flow rate of air

\( \upeta \) :

Compressor efficiency (including drive system)

P air out :

Absolute air pressure at compressor outlet

n :

Polytropic coefficient

ṁ air in :

Air mass flow at compressor inlet

T air in :

Temperature of Air at compressor inlet

R :

Gas Constant

Q CA :

Heat content of compressed air

C p :

Air specific heat

T CA :

Temperature of compressed air at compressor outlet

ṁ v :

Vapor water mass flow rate

h v :

Latent heat of condensation for water vapor

U r :

Relative Humidity

ρ s, in :

Water vapor content in saturation at compressor inlet conditions

ρ s, out :

Water vapor content in saturation at compressor outlet conditions

ɛ cooler :

Cooler heat transfer effectiveness

T h,i :

Temperature of hot fluid entering the cooler

T h,o :

Temperature of hot fluid exiting the cooler

T c,i :

Temperature of cold fluid entering the cooler

P f,out :

Pressure of Air at filter outlet

P f,in :

Pressure of Air at filter inlet

P drop :

Pressure drop in filter

ṁ tank in :

Air mass flow rate at storage tank inlet

ṁ tank out :

Air mass flow rate at storage tank outlet

m tank intial :

Initial mass of air in storage tank

T tank :

Temperature of Air in storage tank

V tank :

Volume of storage tank

f :

Piper friction factor

ρ air :

Air density

L :

Pipe length

D :

Pipe Diameter

V air :

Air velocity in pipe

Re :

Reynolds Number

ɛ :

Pipe Roughness

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Acknowledgment

Research in this paper was funded by the DTA3/COFUND Marie Skłodowska-Curie PhD Fellowship programme partly funded by the Horizon 2020 European Programme.

Author information

Authors and Affiliations

  1. University of Portsmouth, Portsmouth, PO1 2UP, UK

    Mohamad Thabet, David Sanders, Malik Haddad, Nils Bausch, Giles Tewkesbury & Victor Becarra

  2. Gems Sensors, Lennox Road, Basingstoke, RG22 4AW, UK

    Tom Barker

  3. InTandem, Watton Farm, Southampton, SO32 3HA, UK

    Jake Piner

Authors
  1. Mohamad Thabet
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  2. David Sanders
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  3. Malik Haddad
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  4. Nils Bausch
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  5. Giles Tewkesbury
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  6. Victor Becarra
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  7. Tom Barker
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Corresponding author

Correspondence to Malik Haddad .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Thabet, M. et al. (2021). Management of Compressed Air to Reduce Energy Consumption Using Intelligent Systems. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1252. Springer, Cham. https://doi.org/10.1007/978-3-030-55190-2_16

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