Multi-Objective Optimization of an Organic Rankine Cycle (ORC) for a Hybrid Solar–Waste Energy Plant

Lina Wang, Jun Yang (), Bing Qu and Chang Pang
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Lina Wang: School of Marine Technology and Environment, Dalian Ocean University, Dalian 116023, China
Jun Yang: School of Marine Technology and Environment, Dalian Ocean University, Dalian 116023, China
Bing Qu: School of Marine Technology and Environment, Dalian Ocean University, Dalian 116023, China
Chang Pang: Department of Basic, Dalian Naval Academy, Dalian 116013, China

Energies, 2024, vol. 17, issue 8, 1-15

Abstract: In pursuit of sustainable development and mitigation of the intermittency challenge associated with solar energy, this study proposes a hybrid solar system integrating waste heat incineration alongside solar power generation and distinct heat provision. Leveraging the superior energy efficiency of the organic Rankine cycle (ORC) in medium- and low-temperature scenarios, a parabolic trough collector (PTC) is selected for its cost-effectiveness and long-term operational reliability. Dowtherm A and toluene are identified as the optimal working fluids for the PTC and ORC, respectively. To optimize this complex system, a combination of artificial neural networks (ANNs) and multi-objective optimization via non-dominated sorting genetic algorithm II (NSGA-II) is employed, streamlining the optimization process. Thermal dynamic simulations are executed using Engineering Equation Solver (EES, V11) to validate the proposed system’s performance. TOPSIS is employed to identify the optimal solution from the Pareto frontier. The results indicate that the hourly cost of the system stands at USD 43.08, with an exergy efficiency of 22.98%. The economic analysis reveals that the solar collector constitutes the most significant portion of the total initial cost, representing 53.2%, followed by the turbine, thermoelectric generator (TEG), and waste heat incineration, in descending order of costliness.

Keywords: parabolic trough collector (PTC); organic Rankine cycle (ORC); artificial neural network (ANN); NSGA II; TOPSIS (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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