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This study delineates the development of a solar energy system that leverages concentrated solar power (CSP) technology to supply both electricity and potable water for residential applications. The proposed thermal architecture uniquely integrates heliostat solar fields with a dual-loop power generation cycle, augmented by a seawater desalination system that employs reverse osmosis (RO) membranes. To bolster electricity production, a thermoelectric generator (TEG) has been incorporated into the system's design framework. A comprehensive analysis of the system has been performed, encompassing thermodynamic and economic evaluations. Furthermore, a parametric analysis has been executed to investigate the effects of critical parameters on the system's operational efficiency. The efficacy of the system was rigorously assessed through a case study that examined its capabilities for daily production outputs. This research, grounded in the analytical projections from Saudi Arabia and the favorable environmental conditions characteristic of the region, explores the operational performance of the system within this specific geographical context. The primary objective of this inquiry is to determine the ideal operational parameters by employing multi-criteria optimization methods tailored to the established system. Variations in compressor pressure ratios were found to significantly affect the performance of the Brayton cycle and the exergetic efficiency of the system, with optimal economic efficiency being realized at a specific pressure ratio. Furthermore, increasing the inlet temperatures in the organic Rankine cycle has been shown to improve system efficiency up to a certain limit, beyond which potential reliability issues could arise. The case study demonstrated that electricity generation peaks during the summer months, particularly in June, aligning with a high volume of freshwater production totaling 264,530 m³. The optimization efforts achieved an exergetic efficiency of 17.69 % and an overall cost of $359.58 per hour.

As the global energy demand continues to rise, there is an urgent need to improve the efficiency and sustainability of power generation systems. This study integrated a modified supercritical carbon dioxide (S-CO2) and multi-effect desalination (MED) units to recover residual heat from a gas turbine cycle (GTC) in two stages, significantly enhancing electricity production while reducing the environmental footprint of the GTC. The significance of this study lies in its comprehensive approach, combining thermodynamic, environmental, and thermoeconomic analyses alongside thorough sensitivity evaluations. A triple optimization framework was implemented to optimize the system's performance, focusing on key metrics such as exergy efficiency, CO2 reduction rates, and levelized energy cost, utilizing the NSGA-II and the TOPSIS decision-making method in MATLAB software. Economic viability was assessed through a net present value (NPV) analysis, demonstrating substantial profitability. Finally, a comparison study of the devised system CO2 emissions rate was performed for different renewable energy sources. A specific application of the devised system is its capacity to generate 1.415 m³/h of distilled water while generating 1441 kW of electricity. Sensitivity analysis identified the combustion chamber temperature as the most critical design parameter, with a sensitivity index of 0.328. The optimum economic indicators showed marked improvement, with the NPV increasing from 2.371 M$ to 10.75 M$ and the payback period decreasing from 13.28 years to 7.18 years.

This study aims to develop an environmentally friendly multi-energy system for sustainable production of electricity, cooling and hydrogen. The study introduces a pioneering geothermal-based system, integrating an ejector refrigeration cycle, a dual-loop organic Rankine cycle, and a hydrogen production unit with proton exchange membrane electrolyzers. The study provides a thorough analysis of the system's energy and exergy performance, as well as its economic feasibility. Through sensitivity and parametric analyses, the research identifies key parameters that significantly influence system performance. The system's innovative design promises minimal environmental impact while delivering multifaceted performance: generating 1.38 MW of electricity, supplying 436 kW of cooling load, and producing 5.39 kg/h of hydrogen. In the exergy analysis, Evaporator1 is identified as the primary contributor to exergy loss, representing 34 % of the total exergy destruction. This is followed by the electrolysis unit, the condenser, and the ejector refrigeration cycle, which contribute 18 %, 14 %, and 12 %, respectively. The system achieves optimal efficiency at an organic Rankine cycle turbine1 inlet temperature of 387 K, yielding a power generation of 885.4 kW and an exergy efficiency of 26.7 %. Beyond this temperature, any further increase leads to a decline in power output due to operational disturbances. A multi-criteria optimization using genetic algorithm is applied, resulting in an optimized system with a cost rate of 18.13 $/h and an exergy efficiency of 38.96 %.

The proposed system uses a dual-loop organic Rankine cycle, a reverse osmosis desalination unit, an absorption cooling unit, and a thermoelectric generator to produce electricity and freshwater for urban areas. A thorough assessment of the system's thermodynamic and economic performance has been conducted, with a parameter-based investigation to assess the effect of key variables on the system performance. The parametric study indicates that rising the geothermal mass flow rate enhances the energy efficiency, but lowers the energy efficiency and affects the cooling requirements. Moreover, the optimum inlet temperature in turbine 1 increases the desalination efficiency up to 105.02 kg/s at 115 °C, and higher temperatures reduce the performance and system efficiency. Adjusting the temperature difference at the pinch point at Evaporator1 is crucial for system efficiency, with trade-offs between freshwater output, expenses, and exergy efficiency. The capability of the system to produce up to 6,048,000 L of potable water daily signifies a monumental leap towards meeting the water demands of nearly 42,000 individuals, based on European consumption standards. Lastly, the application of genetic algorithms in the optimization process results in an exergetic efficiency of 32.79 % and a cost rate of 58.05 $/h, demonstrating the system's enhanced operational effectiveness.

Microchannel heat sinks (MCHSs) are advanced cooling systems used in various applications such as electronics cooling, thermal management of high-power microprocessors, and microelectronic devices. These heat sinks are designed to efficiently dissipate heat generated by electronic components to maintain optimal operating temperatures and ensure device reliability. Employing appropriate fins within MCHSs is a crucial strategy to enhance heat removal efficiency. Since few investigations have been performed on the simultaneous thermo-hydraulic features in microchannels with Tesla valves, this research addresses this gap by suggesting that the microchannels of a heat sink be reinforced with novel Tesla valve-shaped fins. Tesla valves are designed to offer different levels of resistance depending on the flow direction. This characteristic allows for selective control of pressure drop and heat transfer efficiency by simply switching the flow direction. Utilizing Tesla valve-shaped fins is an almost new approach that differentiates the current research from conventional designs. The advantage of this structure is that under different circumstances, depending on whether more heat transfer is required or less pressure drop (ΔP), the flow direction can be changed, and the maximum efficiency of the MCHS can be accessed. The other innovative aspect of this study lies in the integration of artificial neural network (ANN) models to optimize the efficiency of MCHSs equipped with Tesla valve-shaped fins. The simulations were conducted to characterize the flow and thermal features of the MCHS. Then, these data were used as the benchmark for training the ANN models. Four ANN models were introduced to anticipate the Nusselt number (Nu) and ΔP in each forward and reverse direction. The divider's acute angle (α), the divider's obtuse angle (β), and the divider's radius (R) were selected as the input data. Multi-objective optimization and genetic algorithms were used to achieve the optimal conditions. In the optimal thermal design (Setting 2: MCHS with fin variables of α = 60 β = 180, and R = 80 μm in reverse flow direction), the overall performance had a significant enhancement of 57.1 % compared to the device lacking fins. Furthermore, in Setting 2, the overall performance of the MCHS and its diodicity experienced notable improvements of approximately 12.1 % and 38.5 %, respectively. Implementing the parameters of Setting 5 (MCHS with fin variables of α = 60, β = 152.32, and R = 73.10 μm in forward direction) during fin production resulted in a notable enhancement of about 41.1 % in the device's overall performance.

This study conducts an evaluation of an integrated power, heat, and cooling system reliant on geothermal energy. The process involves integrating both the Organic Rankine Cycle (ORC) and the Goswami cycle. The main objective of this investigation is to analyze the effects of different variables on the system's operation. Additionally, a comprehensive analysis is undertaken to examine how the presence of operational fluids in the ORC affects the overall efficiency of the system. Subsequently, an in-depth economic evaluation is conducted, taking into account the initial investment for system implementation, operational capital, and the levelized cost of goods. The calculation process involves determining the system's income, cash flow, and pay-back time, which heavily rely on the selling prices of the products. By utilizing a genetic algorithm in conjunction with an artificial intelligence algorithm, the system can be optimized to minimize pay-back time and levelized cost of energy, while simultaneously maximizing net production power. The results of this optimization process are then compared with the baseline state of the system to assess the effectiveness of the proposed enhancements. The multifaceted approach enables the evaluation of the system's performance across different conditions and facilitates the optimization of its parameters to improve efficiency and economic feasibility. The comparative optimization results demonstrated a 21 % reduction in payback time, a 14 % increase in net power, and a 12 % improvement in cooling when compared to the reference system state. The payback periods for different operating fluids ranged from 10 to 15 years. Isobutane demonstrated the shortest payback period of approximately 10 years, which was further optimized to 8.6 years. The system demonstrated a power output of 655.8 kW, a cooling output of 578.3 kW, and a heat output of 3042 kW.