کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11027973 | 1666144 | 2018 | 48 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
An innovative Organic Rankine Cycle (ORC) based Ocean Thermal Energy Conversion (OTEC) system with performance simulation and multi-objective optimization
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
جریان سیال و فرایندهای انتقال
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Based on multi-objective particle swarm optimization (MOPSO) algorithm, with considering levelized cost of energy (LCOE) and exergy efficiency as two different objective functions, an innovative Organic Rankine Cycle (ORC) model based Ocean Thermal Energy Conversion (OTEC) system is investigated for trade-off Pareto optimization. In the present study, six key parameters including evaporating temperature, condensing temperature, warm seawater temperature at the outlet of evaporator, cool seawater temperature at the outlet of condenser, degree of superheat, and depth of cool seawater have been selected as decision variables. R717, R152a, R134a, R227ea, R600a and R601 are chosen as working fluids. Meanwhile, Linear Programming Technique for Multi-dimensional Analysis of Preference (LINMAP) is introduced in order to make decision for Pareto frontier. The results indicate that LCOE and exergy efficiency are two conflicting objectives, which are impossible to both achieve their optimal values simultaneously. According to the non-dominated sorting of Pareto optimal solution (POS) for the six working fluids, R717 and R601 have the best performance with 0.34â¯$/kWh of LCOE, 28.17% of exergy efficiency and 0.52â¯$/kWh of LCOE, 28.47% of exergy efficiency, respectively, followed by R152a, R600a and R134a which have relatively poor performance, but better than R227ea.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Thermal Engineering - Volume 145, 25 December 2018, Pages 743-754
Journal: Applied Thermal Engineering - Volume 145, 25 December 2018, Pages 743-754
نویسندگان
Meng Wang, Rui Jing, Haoran Zhang, Chao Meng, Ning Li, Yingru Zhao,