Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

In this paper, the exergy method is combined with the prediction heat strain model to establish a worker exergy model and study whether exergy transfer can express the thermal comfort of workers. The results show the exergy transfer changes in a 'single peak' curve with the temperature increase. Exergy transfer reflects the influence of temperature, humidity, wind speed, and labour intensity. A method was proposed to judge workers' thermal comfort based on the difference between the current exergy transfer and the maximum exergy transfer. This provides a new idea for studying workplace thermal comfort and ventilation cooling.

Household energy consumption is rising, with refrigerators being major contributors due to compressor load. This study explores enhancing refrigerator performance using ceria (CeO2) nanoparticle-enhanced refrigerants. Four blends - R0 (R134a), R1, R2, and R3 (with 0.05%, 0.10%, and 0.15% CeO2 by volume) - were tested. Results showed R3 improved cooling capacity by 7.8% (58.4 W) and COP by 14.5% (2.93) compared to R0. The exergy defect decreased by 5.5%, and exergy efficiency improved by 8.7% (23.92%). Economic analysis revealed operational costs dominate total cost. These findings highlight CeO2 additives' potential in improving refrigeration efficiency, energy use, and sustainability.

This manuscript introduces the COA-HDNN technique, which combines Hamiltonian deep neural networks (HDNN) with the crayfish optimisation algorithm (COA). The COA is to optimise energy management systems (EMS) for micro-combined heat and power (μCHP) systems with fuel cells (FC) and thermoelectric devices (TED). HDNN predicts energy demand and system performance metrics. Implemented in MATLAB, the COA-HDNN method outperforms existing approaches, including the salp swarm algorithm (SSA), particle swarm optimisation (PSO), and heap based optimisation (HBO). The results demonstrate its efficiency at 98%, cost of 2.6 × 105$, and computation time of 2.19 s, highlighting significant improvements in performance and operational efficiency.

This study investigates the potential of dual-fuel engines using natural gas and diesel to improve performance and reduce emissions. It examines the effects of natural gas ratios and injection duration under constant load conditions. Results showed a significant reduction in carbon monoxide (CO) and smoke emissions by up to 86% and 60%, respectively. An economic analysis evaluated the feasibility of this fuel blend, calculating the internal rate of return (IRR), net present value (NPV), and payback period (PB). Additionally, an exergy analysis assessed energy conversion efficiency, while global warming potential (GWP) calculations indicated a blend of 22% natural gas had the lowest GWP.

This study examines both conventional and advanced exergy analyses of a modified ejector-enhanced auto-cascade refrigeration (MEACR) cycle. Conventional exergy analysis shows that the proposed model significantly improves exergy efficiency compared to similar ejector-enhanced auto-cascade refrigeration cycles in the literature. Advanced exergy analysis reveals that 63.47% of the total exergy destruction is avoidable. When the components of the MEACR cycle are investigated, 56.31% of the exergy destruction is attributed to the endogenous part. The low-temperature cycle (LTC) compressor has the highest avoidable endogenous exergy destruction rate of 12.64 kW with 38.5%.

The study comprehensively examines bio-exergetics to establish exergy as a key thermodynamic tool for quantifying human performance. Using an integrative bibliographical research approach and a 'pearl growing' reference tracking method, the study categorises research articles, focusing on thermophysiological and human exergy models. It identifies concepts such as the destroyed exergy rate, exergy transfer to the environment, and exergy efficiency in thermal comfort and physical activities, exploring potential medical applications. Despite its potential, exergy analysis remains relatively unfamiliar in bioenergetics, including human potential measurement.

A novel trigeneration system powered by helically coiled tubes embedded solar collector is proposed. A study using ANSYS-FLUENT simulation was conducted to investigate the impact of coil torsion and coil curvature ratio. The EES software examined the system from energy and exergy perspectives, which showed that coil curvature ratio has positive impact on temperature of solar fluid, whereas coil torsion has negative effect. At 0.032 coil torsion, temperature increased by 28.9% as curvature ratio increased from 0.063 to 0.095 at 1,200 W/m2. From solar exergy supplied, 13.29% is produced exergy, 1.98% is lost exergy, and 84.73% is the exergy destroyed.