The slow dynamic response of a proton exchange membrane fuel cell (PEMFC) to high load change during deficit periods must be considered. Therefore, integrating the hybrid system with energy storage devices like battery storage and/or a supercapacitor is necessary. To reduce the consumed hydrogen, an energy management strategy (EMS) based on the white shark optimizer (WSO) for photovoltaic/PEMFC/lithium-ion batteries/supercapacitors microgrid has been developed. The EMSs distribute the load demand among the photovoltaic, PEMFC, lithium-ion batteries, and supercapacitors. The design of EMSs must be such that it minimizes the use of hydrogen while simultaneously ensuring that each energy source performs inside its own parameters. The recommended EMS-based-WSO was evaluated in regard to other EMSs regarding hydrogen fuel consumption and effectiveness. The considered EMSs are state machine control strategy (SMCS), classical external energy maximization strategy (EEMS), and optimized EEMS-based particle swarm optimization (PSO). Thanks to the proposed EEMS-based WSO, hydrogen utilization has been reduced by 34.17%, 29.47%, and 2.1%, respectively, compared with SMCS, EEMS, and PSO. In addition, the efficiency increased by 6.05%, 9.5%, and 0.33%, respectively, compared with SMCS, EEMS, and PSO.

The study investigates the phenomena involved in the crystal violet (CV) removal using the Khulays natural bentonite from the wastewater. The batch technique was utilized for performing the adsorption experiments. The operating systems were used for the investigation of the adsorption behaviour in the study, which included the initial CV concentration, time taken for shaking, the dosage of adsorbent, and the initial solution pH. The Freundlich isotherm framework and the Langmuir data were assessed in the experiment. The study outcome revealed that the equilibrium in the study was reached when shaking takes place for about 40 minutes. Additionally, the data of the sorption revealed that the enhancement of the CV concentration at the start mitigates the percentage of the CV removal as a result of which saturation integration in the Khulays bentonite dye occurs. The initial improvement in the solution pH led to improved CV adsorption. The data achieved at the isotherm adsorption were found adequate with the frameworks of Freundlich isotherm and Langmuir. Along with it, the model of pseudo-second-order kinetics was used to exhibit the adsorption of crystal violet with the Khulays natural bentonite. The Khulays natural bentonite adsorption of CV was demonstrated by the thermodynamic data exhibiting its spontaneous as well as endothermic nature. The study concludes that basic dyes can be effectively removed from the wastewater by the use of Khulays natural bentonite.

Recently, a swarm-based method called Artificial Hummingbird Algorithm (AHA) has been proposed for solving optimization problems. The AHA algorithm mimics the unique flight capabilities and intelligent foraging techniques of hummingbirds in their environment. In this paper, we propose a modified version of the AHA combined with genetic operators called mAHA. The experimental results show that the proposed mAHA improved the convergence speed and achieved better effective search results. Consequently, the proposed mAHA was used for the first time to find the global maximum power point (MPP). Low efficiency is a drawback of photovoltaic (PV) systems that explicitly use shading. Normally, the PV characteristic curve has an MPP when irradiance is uniform. Therefore, this MPP can be easily achieved with conventional tracking systems. With shadows, however, the conditions are completely different, and the PV characteristic has multiple MPPs (i.e., some local MPPs and a single global MPP). Traditional MPP tracking approaches cannot distinguish between local MPPs and global MPPs, and thus simply get stuck at the local MPP. Consequently, an optimized MPPT with a metaheuristic algorithm is required to determine the global MPP. Most MPPT techniques require more than one sensor, e.g., voltage, current, irradiance, and temperature sensors. This increases the cost of the control system. In the current research, a simple global MPPT method with only one sensor is proposed for PV systems considering the shadow conditions. Two shadow scenarios are considered to evaluate the superiority of the proposed mAHA. The obtained results show the superiority of the proposed single sensor based MPPT method for PV systems.

Scarcity has made fresh water too economically and socially too valuable to be used by the processing industry without restriction. Wet evaporative cooling cycles offer competitive advantages in terms of CoP compared to other cooling cycles with relatively low cost but requiring extensive quantities of water. Dry cooling, on the other hand, requires large heat-transfer areas, in addition to high power requirements. In this study, a hybrid cycle is proposed for high-end cooling loads of 215 MW. The proposed cycle combines the benefits of phase change to make dry cycles competitive. Furthermore, the proposed cycle also diminishes the extensive use of various chemicals used in wet cooling cycles. The applicable dry bulb temperature range is 25–50 °C. Variations in cooling fluid cold temperature due to ambient conditions are curtailed to a maximum of 2 °C by the proposed cycle. A technoeconomic comparison of the proposed solution to wet evaporative cooling is presented, and the effects are summarized without providing extensive design calculations. ASPEN modules are used design and simulation.

This study explores the possible use of ionic liquids as a solvent in a commercial high-pressure CO₂ removal process, to gain environmental and energy benefits. There are two main constraints in realizing this: ionic liquids can be corrosive, specifically when mixed with a water/amine solution with dissolved O₂ & CO₂; and CO₂ absorption within this process is not very well understood. Therefore, scavenging CO₂ to ppm levels from process gas comes with several risks. We used 1-butyl-3-methylimidazoium methane sulphonate [bmim][MS] as an ionic liquid because of its high corrosiveness (due to its acidic nature) to estimate the ranges of expected corrosion in the process. TAFEL technique was used to determine these rates. Further, the process was simulated based on the conventional absorption–desorption process using ASPEN HYSYS v 8.6. After preliminary model validation with the amine solution, [bmim][MS] was modeled based on the properties found in the literature. The energy comparison was then provided and the optimum ratio of the ionic liquid/amine solution was calculated.

An efficient electrochemical energy conversion system with little to no environmental impact is the fuel cell (FC). FCs have demonstrated encouraging results in various applications and can even run on biofuel, such as bio-glycerol, a by-product of biodiesel. The most effective ways to operate FCs can significantly enhance their effectiveness. Incorporating fuzzy modeling and metaheuristic methods, this work used artificial intelligence to determine the ideal operating parameters for a microfluidic fuel cell (MFC). The concentrations of the following four variables were considered: bio-glycerol concentration, anode electrocatalyst loading, anode electrolyte concentration, and cathode electrolyte concentration. The output power density of the MFC was used to assess its performance. The output power density of the MFC was modeled using fuzzy logic, taking into account the aforementioned operational parameters. A jellyfish search optimizer (JSO) was then used to find the ideal operating conditions. The results were contrasted with response surface methodology (RSM) and experimental datasets to demonstrate the superiority of the proposed integration between fuzzy modeling and the JSO. In comparison with the measured and RSM approaches, the suggested strategy boosted the power density of the MFC by 9.38% and 8.6%, respectively.