Exergy-Based Multi-Objective Optimization of an Organic Rankine Cycle with a Zeotropic Mixture

Abstract

In this paper, the performance of an organic Rankine cycle with a zeotropic mixture as a working fluid was evaluated using exergy-based methods: exergy, exergoeconomic, and exergoenvironmental analyses. The effect of system operation parameters and mixtures on the organic Rankine cycle’s performance was evaluated as well. The considered performances were the following: exergy efficiency, specific cost, and specific environmental effect of the net power generation. A multi-objective optimization approach was applied for parametric optimization. The approach was based on the particle swarm algorithm to find a set of Pareto optimal solutions. One final optimal solution was selected using a decision-making method. The optimization results indicated that the zeotropic mixture of cyclohexane/toluene had a higher thermodynamic and economic performance, while the benzene/toluene zeotropic mixture had the highest environmental performance. Finally, a comparative analysis of zeotropic mixtures and pure fluids was conducted. The organic Rankine cycle with the mixtures as working fluids showed significant improvement in energetic, economic, and environmental performances.

Four-Objective Optimization for an Irreversible Porous Medium Cycle with Linear Variation in Working Fluid’s Specific Heat

“…With the increase in OOs, there are contradictions among different OOs. To select the optimal result under the coexistence of multiple OOs, many scholars have carried out multi-objective optimization (MOO) [ 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 ] by NSGA-II [ 78 ]. Li et al [ 68 ] established a regenerative Brayton cycle model and carried out MOO on the , and dimensionless thermal economic performance.…”

“…Chen et al [ 69 ] conducted MOO research on an irreversible modified closed Brayton cycle with four OOs of , , and . Fergani et al [ 70 ] performed MOO on the cyclohexane, toluene and benzene of an organic Rankine cycle using a multi-objective particle swarm optimizer. Teng et al [ 71 ] performed MOO on the multiple systems under the conditions of different heat source temperatures of an organic Rankine cycle.…”

Maximum Efficient Power Performance Analysis and Multi-Objective Optimization of Two-Stage Thermoelectric Generators

“…Sun et al [ 39 ] used the operating current, lower TE element module height and ratio of the HEX channel width to fin thickness as optimization variables, and carried out two-objective optimization of the exergy efficiency and irreversibility of two-stage series and parallel TE refrigerators. The MOO of NSGA-II is also widely used in the Brayton cycle [ 40 ], Stirling–Otto combined cycle [ 41 ], Organic Rankine cycle [ 42 ], Stirling cycle [ 43 , 44 ], etc.…”

Multi-Objective Optimization of the Basic and Regenerative ORC Integrated with Working Fluid Selection

“…Hu et al [ 24 ] also compared the ORC performance using hydrofluorolefins (HFOs) through multi-objective optimization and reported that the evaporation temperature is the most relevant decision variable and R1234ze(E) is optimal to offer the largest power output with the weight of economic performance (W 1 < 0.2). Fergani et al [ 25 ] performed an exergy-based multi-objective optimization of an ORC with zeotropic mixtures and found that the mixtures could provide a significant improvement in energetic, economic, and environmental performances. Xia et al [ 26 ] proposed a method combing multi-objective optimization with improved grey relational analysis (GRA) to select working fluids for the dual-loop ORC system.…”