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Compact modeling of compressed air distribution network for usage forecasting and energy optimization.

Number: 1473

Author(s) : YAZAWA K., LAORANGE G., VOORHIS M., SHAKOURI A.

Summary

We analyze power consumption time series for a compressed dry air (CDA) system. The seasonal and organizational variations are studied through the frequency spectrum. The power consumption can be predicted once the component information is extracted from the historical data. A key factor is Weekly and Daily patterns from the signals. These characteristics are controlled by compressed air demand. An additional factor impacting the power consumption is the efficiency of the compressor, which decreases as intake air density increases. This is a direct function of temperature and an indirect function of relative humidity. We developed a compact analytic model for the compressed air distribution system using the analogy with an electrical network. The nonlinear and transient behavior of the air movement across the pipeline was modeled based on the acoustic system. We calculated the time-dependent response using an electrical circuit simulator, SPICE. The simplified model demonstrates that the compressor electrical power consumption signals contain the characteristic information of pneumatic terminals at different distances with different time constants. This compact model still needs validation with higher time resolution power consumption data from the compressors every second or less. Future work will focus on correlating transient power consumption patterns with the production or usage schedule in different locations inside the factory and in forecasting compressed air energy utilization.

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Pages: 10 p.

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  • Original title: Compact modeling of compressed air distribution network for usage forecasting and energy optimization.
  • Record ID : 30033110
  • Languages: English
  • Subject: Figures, economy
  • Source: 2024 Purdue Conferences. 20th International Refrigeration and Air-Conditioning Conference at Purdue.
  • Publication date: 2024/07/17

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