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Supercapacitors (SCs) are gaining attention for Internet of Things (IoT) devices because of their impressive characteristics, including their high power and energy density, extended lifespan, significant cycling stability, and quick charge–discharge cycles. Hence, it is essential to make precise predictions about the capacitance and lifespan of supercapacitors to choose the appropriate materials and develop plans for replacement. Carbon-based supercapacitor electrodes are crucial for the advancement of contemporary technology, serving as a key component among numerous types of electrode materials. Moreover, accurately forecasting the lifespan of energy storage devices may greatly improve the efficient handling of system malfunctions. Researchers worldwide have increasingly shown interest in using machine learning (ML) approaches for predicting the performance of energy storage materials. The interest in machine learning is driven by its noteworthy benefits, such as improved accuracy in predictions, time efficiency, and cost-effectiveness. This paper reviews different charge storage processes, categorizes SCs, and investigates frequently employed carbon electrode components. The performance of supercapacitors, which is crucial for Internet of Things (IoT) applications, is affected by a number of their characteristics, including their power density, charge storage capacity, and cycle longevity. Additionally, we provide an in-depth review of several recently developed ML-driven models used for predicting energy substance properties and optimizing supercapacitor effectiveness. The purpose of these proposed ML algorithms is to validate their anticipated accuracies, aid in the selection of models, and highlight future research topics in the field of scientific computing. Overall, this research highlights the possibility of using ML techniques to make significant advancements in the field of energy-storing device development. [ABSTRACT FROM AUTHOR]

In the present study, a power, cooling, and freshwater tri-generation system is proposed to meet the global requirements sustainably. A Kalina cycle (KC), an absorption refrigeration cycle (AC), and an organic Rankine cycle (ORC) coupled with reverse osmosis (RO) are integrated to form the proposed system. The performance of the proposed system is analyzed using thermodynamic and economic viewpoint. An integrated system combines the refrigerant loop of the water–ammonia absorption chiller, consisting of an evaporator and throttling valves with KC. The Kalina turbine discharges the heat and combines with generator to loop the ORC and generate power which drives the RO module. A portion of the mass flowrate enters the evaporator to generate cooling after being condensed in the AC unit. The effect of key thermodynamic parameters on system performance is studied using parametric analysis. The results show that the system is capable to generate 1,725 kW of power, 665 kW of cooling, and 3.42 m³/h of freshwater. The parametric analysis results indicate that the flash tank pressure has an optimum value which should be selected wisely. It is concluded that the parameters related to the KC are dominant ones because they can affect both the KC and the ORC. The proposed system is a flexible adapting power, cooling, and freshwater tri-generation demand. [ABSTRACT FROM AUTHOR]

The hybrid desalination of reverse osmosis (RO) with forward osmosis (FO) is an innovative technology for freshwater production worldwide, which provides many advantages such as RO fouling and scaling, recovery of the energy of RO brine, minimizing the power consumption and minimizing the use of chemicals required for conventional pretreatment steps. For this reason, breakdowns, blockage of the membrane, pressure losses and preventive maintenance have to be minimized in duration and frequency to ensure the maximum availability and indeed improving the availability or reliability of the system leads objectively to the reduction of operating costs. In this study, hybrid fault tree analysis method based fuzzy set theory was considered to evaluate the availability and reliability of the integrated RO–FO desalination system. For assessing availability, the system was divided into four subsystems such as seawater intake pump, pressurization stage, FO stage and RO stage. The data were collected from reliability data bank and probabilities estimated by considering the fuzzy set theory, wherever detailed data were not available. The overall unavailability of the hybrid system was 0.0156. [ABSTRACT FROM AUTHOR]