This research investigates the thermodynamic performance of Organic Rankine Cycle (ORC) systems designed for low-to-medium-grade waste heat recovery. Through a rigorous com- parative analysis of closed and direct-contact regenerative architectures, the study evaluates a diverse library of nine working fluids, including legacy hydrofluorocarbons (HFCs), fourth- generation hydrofluoroolefins (HFOs), and natural hydrocarbons (HCs). A computational framework, developed in Python and powered by the CoolProp library, ensures high-fidelity thermophysical modeling. A Particle Swarm Optimization (PSO) algorithm is used to in- dependently maximize the thermal and exergy efficiencies across a heat source temperature range of 140◦C to 180◦C. The findings demonstrate the efficacy of the regenerative config- uration in enhancing exergy recovery and provide a strategic roadmap for selecting environ- mentally sustainable fluids in modern energy applications.
Questa ricerca esamina le prestazioni termodinamiche dei sistemi a Ciclo Rankine Organ- ico (ORC) progettati per il recupero di calore di scarto di basso e medio grado. Attraverso una rigorosa analisi comparativa delle architetture rigenerative chiuse e a contatto diretto, lo studio valuta una vasta libreria di nove fluidi di lavoro, tra cui gli idrofluorocarburi (HFC) tradizionali, le idrofluoroolefine (HFO) di quarta generazione e gli idrocarburi (HC) naturali. Un framework computazionale, sviluppato in Python e supportato dalla libreria CoolProp, garantisce una modellazione termofisica ad alta fedelt`a. Un algoritmo di ottimizzazione a sciame di particelle (Particle Swarm Optimization - PSO) viene utilizzato per massimizzare in modo indipendente le efficienze termiche ed exergetiche in un intervallo di temperatura della fonte di calore compreso tra 140◦C e 180◦C. I risultati dimostrano l’efficacia della configurazione rigenerativa nel migliorare il recupero exergetico e forniscono una tabella di marcia strategica per la selezione di fluidi ecologicamente sostenibili nelle moderne appli- cazioni energetiche.
Thermodynamic optimization of regenerative organic Rankine cycles using particle swarm optimization
ALINEZHADSHIRAZI, MEHDI
2025/2026
Abstract
This research investigates the thermodynamic performance of Organic Rankine Cycle (ORC) systems designed for low-to-medium-grade waste heat recovery. Through a rigorous com- parative analysis of closed and direct-contact regenerative architectures, the study evaluates a diverse library of nine working fluids, including legacy hydrofluorocarbons (HFCs), fourth- generation hydrofluoroolefins (HFOs), and natural hydrocarbons (HCs). A computational framework, developed in Python and powered by the CoolProp library, ensures high-fidelity thermophysical modeling. A Particle Swarm Optimization (PSO) algorithm is used to in- dependently maximize the thermal and exergy efficiencies across a heat source temperature range of 140◦C to 180◦C. The findings demonstrate the efficacy of the regenerative config- uration in enhancing exergy recovery and provide a strategic roadmap for selecting environ- mentally sustainable fluids in modern energy applications.| File | Dimensione | Formato | |
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Thesis_Mehdi_Alinezhadshirazi.pdf
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https://hdl.handle.net/20.500.12608/109899