Multi-Objective Optimization and Fluid Selection of Different Cogeneration of Heat and Power Systems Based on Organic Rankine Cycle

Abstract

Cogeneration of heat and power systems based on the organic Rankine cycle (ORC-CHP) has been proven to be an effective way to utilize waste heat at medium and low temperatures. In this work, three ORC-CHP (combined heat and power based on organic Rankine cycle) systems are simulated and compared, including the SS (serial system), the CS (the condensation system), and the SS/CS. The multi-objective genetic algorithm (MOGA) is used to optimize the three systems respectively to achieve higher exergy efficiency and profit ratio of investment (PRI). The optimal thermal-economic performance is obtained. Twelve organic fluids are adopted to evaluate their performance as working fluids. The calculation results show that SS has the highest exergy efficiency, while SS/CS has the best economic performance. Compared with the highest exergy efficiency of SS and the best economic performance of SS/CS, CS will be the optimal solution considering these two objective functions. Under the optimal working conditions, SS has the highest thermal efficiency because it has the highest net power output. The components with the largest proportion of exergy destruction are the heat exchangers, which also has the highest cost.

Dual fluid trigeneration combined organic Rankine-compound ejector-multi evaporator vapour compression system

“…Different waste heat configurations could benefit the overall ORC performance. Teng et al [24] compared three ORC arrangements for power-heating purposes: serial, condensation, and compound. The serial system presented the highest thermal and exergy efficiency, whereas the combination highlighted economic performance.…”

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.…”

“…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. Baghernejad et al [ 72 ] took exergy efficiency, overall cost rate and exergy unit cost of generated electricity as OOs, and performed MOO on the combined Brayton and Rankine cycle.…”

Multi-Objective Constructal Design for Quadrilateral Heat Generation Body with Vein-Shaped High Thermal Conductivity Channel

“…Therefore, the multi-objective optimization (MOO) not only adapts to the engineering design requirements but also promotes the update and replacement of the heat dissipation design strategy of electronic devices. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) [ 57 ] with an elite strategy has been successfully applied to many engineering designs [ 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 ]. In particular, some scholars apply the NSGA-II algorithm to the study of constructal design with different optimization objectives.…”