Entropy generation minimization: A practical approach for performance evaluation of temperature cascaded co-generation plants
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Water Desalination and Reuse Research Center (WDRC)
Permanent link to this recordhttp://hdl.handle.net/10754/562345
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AbstractWe present a practical tool that employs entropy generation minimization (EGM) approach for an in-depth performance evaluation of a co-generation plant with a temperature-cascaded concept. Co-generation plant produces useful effect production sequentially, i.e., (i) electricity from the micro-turbines, (ii) low pressure steam at 250 °C or about 8-10 bars, (iii) cooling capacity of 4 refrigeration tones (Rtons) and (iv) dehumidification of outdoor air for air conditioned space. The main objective is to configure the most efficient configuration of producing power and heat. We employed entropy generation minimization (EGM) which reflects to minimize the dissipative losses and maximize the cycle efficiency of the individual thermally activated systems. The minimization of dissipative losses or EGM is performed in two steps namely, (i) adjusting heat source temperatures for the heat-fired cycles and (ii) the use of Genetic Algorithm (GA), to seek out the sensitivity of heat transfer areas, flow rates of working fluids, inlet temperatures of heat sources and coolant, etc., over the anticipated range of operation to achieve maximum efficiency. With EGM equipped with GA, we verified that the local minimization of entropy generation individually at each of the heat-activated processes would lead to the maximum efficiency of the system. © 2012.
CitationMyat, A., Thu, K., Kim, Y. D., Saha, B. B., & Choon Ng, K. (2012). Entropy generation minimization: A practical approach for performance evaluation of temperature cascaded co-generation plants. Energy, 46(1), 493–521. doi:10.1016/j.energy.2012.07.062
SponsorsThe authors gratefully express the gratitude to Agency of Science, Technology and Research (A*STAR) for their generous financial support for the project (Grant Number R-265-000-287-305).