4.7 Article

Mathematical modelling and structural optimization of a micro-channel liquid separation condenser in organic Rankine cycle and refrigeration cycle

期刊

APPLIED THERMAL ENGINEERING
卷 152, 期 -, 页码 231-246

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.applthermaleng.2019.02.050

关键词

Micro-channel; Liquid separation condenser; Optimization; MINLP; Organic Rankine cycle

资金

  1. State Key Program of National Natural Science Foundation of China [51736005]
  2. National Natural Science Foundation of China [51876043]
  3. Science and Technology Program of Guangzhou [201704030108]

向作者/读者索取更多资源

The decrease in fossil energy reserves and increase in energy prices have resulted in a strong interest in utilizing low-grade energy or improving the efficiency of traditional energy system. Air-cooled condenser is a key component in thermodynamic cycles, such as organic Rankine cycle and vapor compression refrigeration cycle. Liquid separation condenser (LSC) is a newly developed fin-and-tube condenser with excellent comprehensive performance. In this study, a micro-channel LSC (MLSC) is applied in organic Rankine cycle and vapor compression refrigeration cycle. A mathematical model is developed for the numerical simulation and structural optimization of MLSC. The objective function of the structural optimization is to minimize the total annual cost, which is composed of investment and operating costs. Unlike the traditional optimization method for heat exchangers, an improved operating cost indicator is derived and modeled according to the impact of pressure drop (PD) during condensation in the context of thermodynamic cycles. A case study is elaborated to test the proposed methodology. The simulation and optimization results show that the performance of MLSC are sensible to the cycle context. The total cost of the studied MLSC in the context of vapor compression refrigeration cycle (VCRC) is 52.46% higher than that in the context of organic Rankine cycle (ORC). Compare with the benchmark MLSC, the total cost of the optimal MLSC can be reduced by up to 45.2% in the context of VCRC and 34.9% in the context of ORC. These results indicate that the present study provides a methodology for better understanding the importance and the performance improvement of the MLSC in the related cycle than the previous simplified method.

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