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Targeting and design of CWNs (cooling water networks)

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  • Shenoy, Akshay U.
  • Shenoy, Uday V.

Abstract

CWNs (cooling water networks) are targeted and designed in this work by an analytical approach requiring no graphical procedures. The key basis for the approach is the conversion of the cooling duties to equivalent inlet–outlet (demand–source) pairs. The UTA (Unified Targeting Algorithm) is first used to establish the minimum CW (cooling water) flowrate and identify the pinch temperature. The Enhanced NNA (Nearest-Neighbors Algorithm) is then utilized to systematically synthesize CWNs by giving priority to LR (local-recycle) matches. The flowrate of the LR match is maximized wherever possible by choosing the neighbor source as the cleanest available source rather than the nearest neighbor. This is advantageous because LR matches can be eliminated for fixed load operations to yield relatively simple network designs that not only meet the minimum CW flow target but also minimize the flowrate through the coolers resulting in reduced network cost. Two case studies are presented to illustrate the versatility of the approach in generating multiple optimum network designs for maximum reuse, temperature constraints and debottlenecking. Importantly, it is demonstrated that superior practical networks can be designed for temperature constrained CWNs with pinch migration as well as without pinch recognition.

Suggested Citation

  • Shenoy, Akshay U. & Shenoy, Uday V., 2013. "Targeting and design of CWNs (cooling water networks)," Energy, Elsevier, vol. 55(C), pages 1033-1043.
    • Handle: RePEc:eee:energy:v:55:y:2013:i:c:p:1033-1043
    DOI: 10.1016/j.energy.2013.03.095
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    References listed on IDEAS

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    1. Picón-Núnez, Martín & Polley, Graham T. & Canizalez-Dávalos, Lázaro & Medina-Flores, José Martín, 2011. "Short cut performance method for the design of flexible cooling systems," Energy, Elsevier, vol. 36(8), pages 4646-4653.
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    Cited by:

    1. Ma, Jiaze & Wang, Yufei & Feng, Xiao, 2017. "Energy recovery in cooling water system by hydro turbines," Energy, Elsevier, vol. 139(C), pages 329-340.
    2. Foo, Dominic C.Y. & Ng, Denny K.S. & Leong, Malwynn K.Y. & Chew, Irene M.L. & Subramaniam, Mahendran & Aziz, Ramlan & Lee, Jui-Yuan, 2014. "Targeting and design of chilled water network," Applied Energy, Elsevier, vol. 134(C), pages 589-599.
    3. Diban, Pitchaimuthu & Foo, Dominic C.Y., 2018. "Targeting and design of heating utility system for offshore platform," Energy, Elsevier, vol. 146(C), pages 98-111.
    4. Ma, Jiaze & Wang, Yufei & Feng, Xiao, 2018. "Optimization of multi-plants cooling water system," Energy, Elsevier, vol. 150(C), pages 797-815.
    5. Zhang, Haitian & Feng, Xiao & Wang, Yufei & Zhang, Zhen, 2019. "Sequential optimization of cooler and pump networks with different types of cooling," Energy, Elsevier, vol. 179(C), pages 815-822.

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