Optimization of an Organic Rankine Cycle–Vapor Compression Cycle System for Electricity and Cooling Production from Low-Grade Waste Heat

• Published in: Energies (Basel) Vol. 17; no. 22; p. 5566
• Main Author: Witanowski, Lukasz
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2024
• Subjects: Air conditioning; Algorithms; Climate change; Cooling; Electric power production; Electricity generation; Emerging markets; Emissions; Energy consumption; Energy efficiency; Greenhouse gases; Heat; Heat recovery; Mathematical optimization; Methods; multi-objective optimization; Non-Dominated Sorting Genetic Algorithm II; Optimization techniques; Organic Rankine Cycle; Performance evaluation; Temperature; Thermodynamic cycles; Vapor Compression Cycle; Waste heat; Poland; United States > US; China; Indonesia
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17225566

In light of the intensifying global climate crisis and the increasing demand for efficient electricity and cooling systems, the exploration of advanced power generation technologies has become crucial. This paper presents a comprehensive analysis of Organic Rankine Cycle–Vapor Compression Cycle (ORC-VCC) systems utilizing low-grade waste heat for the dual purpose of electricity and cooling production. The study focuses on systems that harness waste heat below 90 °C with thermal inputs up to 500 kW. An in-house Python code was developed to calculate cycle parameters and perform multi-objective optimization targeting the maximization of both ORC-VCC efficiency and power output. The optimization was conducted for 10 different cases by evaluating five working fluids across two different ambient temperatures. The analysis reveals that the optimized system achieved an impressive overall cycle efficiency exceeding 90%, demonstrating the significant potential of ORC-VCC technology in waste heat recovery applications. The Non-Dominated Sorting Genetic Algorithm II (NSGA-II) multi-objective optimization approach was found to be particularly effective at navigating the multi-dimensional solution space and identifying the global optimum. This study provides valuable insights into system performance across a range of operating conditions and design parameters. Sensitivity analyses highlight key factors influencing cycle efficiency and power output. These findings have important implications for the development and deployment of ORC-VCC systems as a sustainable and efficient solution to meet growing energy needs while reducing greenhouse gas emissions.