2023
DOI: 10.1016/j.applthermaleng.2023.120455
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A synergistic multi-objective optimization mixed nonlinear dynamic modeling approach for organic Rankine cycle (ORC) under driving cycle

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Cited by 12 publications
(2 citation statements)
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“…To address the challenges posed by variable temperature waste heat sources, Ping et al [16] introduced a synergistic multi-objective optimization approach within the context of dynamic driving cycles. By combining nonlinear dynamic modelling, they reduced the number of decision variables and construction time, ultimately enhancing the efficiency and reducing uncertainty and hysteresis in ORC operation.…”
Section: Power Cyclesmentioning
confidence: 99%
“…To address the challenges posed by variable temperature waste heat sources, Ping et al [16] introduced a synergistic multi-objective optimization approach within the context of dynamic driving cycles. By combining nonlinear dynamic modelling, they reduced the number of decision variables and construction time, ultimately enhancing the efficiency and reducing uncertainty and hysteresis in ORC operation.…”
Section: Power Cyclesmentioning
confidence: 99%
“…Tooli et al [23] conducted a comparative study of different types of supercritical CO2 integration with ORC using high-temperature heat sources from energy, exergy, and economic (3E) perspectives on different types of supercritical CO2 integrated ORC using a high-temperature heat source. Xu et al [24] performed a synergistic multi-objective optimization of ORC comprehensive performance under a driving cycle with thermodynamic performance, economic performance, thermoeconomic performance, and environmental impact as optimization objectives. Ashwn et al [25] analyzed the ORC system using environmentally friendly working fluids and optimized its performance using the non-dominated sorting algorithm-II.…”
Section: Introductionmentioning
confidence: 99%