Towards Techno-Economics of Green Hydrogen as a Primary Combustion Fuel for Recreational Vehicle Vapor Absorption Refrigeration System

Zun, Moe Thiri; Bibi, Saira; Ahmed, Muhammad Shakeel; Zahid, Taiba; Selvaraj, Jeyraj; Ghafoor, Usman

doi:10.2139/ssrn.4086406

Cited by 1 publication

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“…There are various technologies available for automobile air conditioning, each with its own set of benefits and drawbacks [10]. One such technology is the absorption refrigeration system, which has been of significant interest owing to its potential to reduce greenhouse gas emissions [11]. This system operates by absorbing the refrigerant into a liquid or solid absorbent, usually lithium bromide or ammonia, which is then heated to release the refrigerant vapor [12].…”

Section: Introductionmentioning

confidence: 99%

Improving Automotive Air Conditioning System Performance Using Composite Nano-Lubricants and Fuzzy Modeling Optimization

Alahmer

Ghoniem

2023

Sustainability

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This study aims to enhance the effectiveness of automobile air conditioning (AAC) systems through the use of composite nano-lubricants and fuzzy modeling optimization techniques. Composite nano-lubricants, which consist of varied metal oxide ingredients and content ratios, are projected to surpass single-component nano-lubricants in terms of improving the performance of AAC systems. Fuzzy modeling is used to simulate the AAC system based on experimental data using three input parameters: volume concentration of nano-lubricants (%), the refrigerant charge (g), and compressor speed (rpm). The output performance of the AAC system is measured using four parameters: cooling capacity (CC) in kW, compressor work (CW) in kJ/kg, coefficient of performance (COP), and power consumption (PC) in kW. Optimization is performed using the marine predators algorithm (MPA) to identify the best values for the input control parameters. The objective function is to minimize CW, COP, and PC while simultaneously maximizing CC and COP. Results showed that the performance of the AAC system improved from 85% to 88% compared to the experimental dataset, highlighting the potential benefits of using composite nano-lubricants and fuzzy modeling optimization for improving the energy efficiency of AAC systems. Furthermore, a comprehensive comparison with ANOVA was performed to demonstrate the superiority of the fuzzy modeling approach. The results indicate that the fuzzy model outperforms ANOVA, as evidenced by a reduced root mean square error (RMSE) for all data, from 0.412 using ANOVA to 0.0572 using fuzzy. Additionally, the coefficient of determination for training increased from 0.9207 with ANOVA to 1.0 with fuzzy, further substantiating the success of the fuzzy modeling phase.

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Section: Introductionmentioning

confidence: 99%