1. Application of machine learning to enhance the performance of a two-stage, two-temperature ejector cycle driven by the waste heat of exhaust gas.
- Author
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Megdouli, K., Tashtoush, Bourhan, and Cinnella, Paola
- Subjects
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WASTE heat , *WASTE gases , *MACHINE learning , *PARTICLE swarm optimization , *GLOBAL warming - Abstract
• - Sustainable 2-stage, 2-temp ejector cycle is presented to generate cooling, electricity while harnessing exhaust heat. • -An eco-friendly working fluid mixture is used to minimize the impact of global warming. • -The key innovation in the proposed cycle is that the ejector boosts refrigeration and cuts electricity. • -One-dimensional analysis and the Artificial Neural Network model are used to examine parameters affecting ejector efficiency. • -The proposed cycle, optimized for energy, exergy, and economics, shows potential for efficiency, cost-effectiveness, and sustainability. This article introduces an innovative two-stage, two-temperature ejector cycle (NTDERC) capable of simultaneously generating cooling and electricity. It harnesses waste heat from exhaust gases and is purposefully designed to operate with a neutral carbon footprint. To minimize global warming potential, we opted for a mixture of isobutane/propane (50 %/50 %) as the working fluid. In the NTDERC, the ejector is a crucial component, as it enhances refrigeration capacity and reduces electricity consumption by replacing the traditional compressor. Initially, we analyzed how geometric and operational parameters affect ejector efficiency using one-dimensional modeling and generated a database. An artificial neural networks (ANN) surrogate model is then developed to predict ejector performance, simplifying this otherwise laborious procedure. Finally, we conducted a comprehensive analysis of the NTDERC, considering energy, exergy, and exergoeconomic aspects through parameter evaluation. Results from the Multi-Objective Particle Swarm Optimization (MOPSO) indicate that the optimized total exergy destruction and the total unit cost of the product are 19.39 kW and 22.48 $/GJ, respectively. Implementation of this system in the refrigerated transportation industry has the potential to enhance efficiency, reliability, and sustainability, ultimately ensuring the safe and efficient transportation of temperature-sensitive goods. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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