Experimentally validated modeling of a turbo-compression cooling system for power plant waste heat recovery

Shane D. Garland, Jeff Noall and Todd M. Bandhauer

Energy, 2018, vol. 156, issue C, 32-44

Abstract: Waste heat recovery systems utilize exhaust heat from power generation systems to produce mechanical work, provide cooling, or create high temperature thermal energy. One system that provides a cooling effect is the turbo-compression cooling system, which operates by using low-grade waste heat to vaporize a fluid and spin a turbine in a recuperative Rankine cycle. The turbine power is used to directly drive a compressor in a traditional vapor-compression cycle. This study presents a theoretical modeling approach that uses compressor and turbine efficiency maps and a heat exchanger UA scaling methodology to make performance predictions over a range of ambient temperatures and cooling loads. The results of experimental testing for a 250 kWth TCCS showed good correlation (maximum error of 2.0%) for power and cooling cycle mass flow ranges of 0.35 kg s−1–0.5 kg s−1 and 0.65–0.85 kg s−1, respectively. The validated modeling approach was used to predict system performance for a Natural Gas Combined Cycle power plant application.

Keywords: Thermally activated cooling; Waste heat recovery; Power plant cooling (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544218308685
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:156:y:2018:i:c:p:32-44

DOI: 10.1016/j.energy.2018.05.048

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().