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This study presents an in-depth analysis and evaluation of the performance of a standard 200 W solar cell, focusing on the energy and exergy aspects. A significant research gap exists in the comprehensive integration of numerical models with advanced machine-learning approaches, specifically emotional artificial neural networks (EANN), to simulate and optimize the electrical characteristics and efficiency of solar panels. To address this gap, a numerical model alongside a novel EANN was employed to simulate the system's electrical characteristics, including open-circuit voltage, short-circuit current, system resistances, maximum power point characteristics, and characteristic curves. Mathematical equations for calculating efficiency levels under varying operational conditions were developed. The system's operational and electrical parameters, alongside environmental conditions such as solar radiation, wind speed, and ambient temperature, were empirically observed and documented over a day. A comparative analysis was conducted to validate the model by comparing its results with manufacturer data and experimental observations. During the trial from 7:00 to 17:00, energy efficiency varied from 10.34 to 14.00%, averaging 13.6%, while exergy efficiency ranged from 13.57 to 16.41%, with an average of 15.70%. The results from the EANN model indicate that the proposed method for forecasting energy, exergy, and power is feasible, offering a significant reduction in computational expense compared to traditional numerical models. The integration of numerical modeling with EANN enhances simulation accuracy and the developed equations enable real-time efficiency calculations. Empirical validation under varying environmental conditions improves predictive capabilities for solar panel performance. Additionally, operational efficiency assessments aid in better design and deployment of solar energy systems, and computational costs for large-scale solar energy simulations are reduced. [ABSTRACT FROM AUTHOR]

Clean energy and environmental pollution are two key concerns of modern society and are pivotal necessities for the economic, social, and sustainable development of the world. Today around 80% of energy is generated using nonrenewable resources and fossil fuels (oil, gas, coal) which ultimately results in hazardous global emissions. As a clean substitute for fossil fuels, hydrogen has emerged as a promising and renewable energy resource. Utilization of this energy resource requires the development of active, stable, low‐cost environmentally friendly techniques. Water splitting electrolysis is a method for producing clean and efficient hydrogen using an environmentally benign technique that is currently at its most mature stage. Electrolysis is attracting ever‐increasing attention, as it is a promising electrochemical device for hydrogen production from water due to the high conversion efficiency and relatively low energy input required when compared to thermochemical and photocatalytic methods. This paper will outline the need, performance, and insight of anion exchange membrane (AEM) electrolyzer. Recent developments in the design and preparation of AEM. New strategies for activity, stability, and efficiency improvement of AEM. Membrane types, and factors affecting AEM performance in an electrolyzer. This review also discusses the effects, operating characteristics, and energy consumption of electrocatalysts in the AEM electrolyzer. Hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) pathways and mechanisms in acidic and alkaline media. This study seeks to provide a detailed overview of recent accomplishments in the field of the hydrogen economy, particularly electrolysis, to inspire further research and development to address the technology's obstacles. [ABSTRACT FROM AUTHOR]

Hybrid oils ensure multifaceted technological (self‐lubricating and favorable fatty acid properties), and sustainable (environmental, economic, and societal) benefits towards biodiesel conversions. The hybrid oils (Hydnocarpus wightiana oil and waste cooking oils [40:60 v/v]) were synthesized to methyl ester with alkaline catalyst sodium hydroxide through the base‐transesterification process. The resulting hybrid oil methyl ester (HOME) is 96.68%. The crude oil (CO) and its HOME underwent characterization using gas chromatography–mass spectrometry, Fourier transform infrared spectroscopy, and hydrogen‐1 nuclear magnetic resonance spectroscopy. The physicochemical properties of crude oils and hybrid biodiesel were analyzed and compared to pure diesel. Different blends of biodiesel–diesel, including binary (20% HOME + 80% diesel [D]), ternary (60% D + 20% HOME + 20% heptanol [H]), and quaternary (20% HOME + 20% H + 10% CO + 50% D) blends, were tested in a single‐cylinder compression ignition (CI) engine under various load conditions to assess their performance, emissions, and combustion properties. Experimental findings indicate that the addition of heptanol to diesel or hybrid biodiesel (ternary blend) enhances brake thermal efficiency, reduces brake‐specific fuel consumption, and leads to longer ignition delays, resulting in higher internal combustion pressure and thermal energy release rates compared to the quaternary blend. Additionally, compared to pure diesel, the ternary blend exhibits decreased emissions of carbon monoxide (CO) and hydrocarbon (HC), with a slight increase in nitrous oxide (NOx) and carbon dioxide (CO2). Notably, the ternary blend emerges as a distinct alternative biodiesel blend suitable for direct use in CI engines without requiring any engine modifications. [ABSTRACT FROM AUTHOR]

This study investigates the impact of cooling methods on the electrical efficiency of photovoltaic panels (PVs). The efficiency of four cooling techniques is experimentally analyzed. The most effective approach is identified as water-spray cooling on the front surface of PVs, which increases efficiency by 3.9% compared to the case without cooling. The results show that water-spray cooling raises the PV's temperature to 41°C, while improving its average daytime efficiency to 22%. Air-cooling, water-cooling in the tubes behind the PV, and aluminum oxide-water nanofluid cooling in the tubes behind the PV improve efficiency by 1.1%, 1.9%, and 2.7%, respectively. The findings highlight the potential of water-spray cooling as a cost-effective and efficient method to enhance PV efficiency and contribute to the global effort towards renewable energy. [ABSTRACT FROM AUTHOR]

The prevalence of air conditioners (ACs) in regions with hot and arid climates raises significant health concerns for users. This study investigates the impact of AC usage on health and indoor air quality in Kenadsa, Bechar, located in southwest Algeria. A questionnaire survey was conducted among 300 residents to identify symptoms associated with AC use. Additionally, dust accumulation in filters was measured in 20 households with varying occupancy types to assess the relationship between particle generation and room usage. Temperature and relative humidity were also recorded in two rooms, one equipped with an evaporative cooler and the other with a split-type AC, to compare their thermal performance. The findings reveal that occupant behavior and building usage significantly contribute to particle generation, with AC operating speed notably impacting filter contamination. Evaporative coolers increase temperature and humidity levels compared to split-type ACs, with their effectiveness depending on proper ventilation. Most respondents begin using ACs in May, prefer operating them during midday and nighttime, and express concerns about thermal shock and AC lifespan. Furthermore, many participants are lax in dust removal, potentially worsening indoor air pollution. A significant portion of respondents report various health issues linked to prolonged exposure to conditioned air but show limited awareness of AC filter maintenance and its health implications. This study highlights the complex interplay between human behavior, AC usage patterns, and indoor air quality, emphasizing the need for targeted interventions to promote responsible AC use, improve filter maintenance practices, and mitigate associated health risks in hot and dry climates. [ABSTRACT FROM AUTHOR]

Knowing the effect of different load distributions on the optimized sizes of the hybrid renewable energy systems (HRES) and the fixed price of Electric Vehicle (EV) charging station can be useful to potential investors in this sector. Additionally, the amount of CO2 emission under different load demands can help decision-makers in the installation of environmentally friendly EV charging stations. Investigation of fixed prices of installing power stations under different load demands leads to know whether electricity sell prices, and serves by the station owners are economically or not. This would also lead to give a reliable and economical plan to customers by station owners, where both time of arrival and charging prices have a pivotal role. Unlike previous similar studies that mostly consider one type of load demand, this study investigates the optimized HRESs including Photovoltaic panel (PV), Wind Turbine (WT), Diesel Generator (DG), and Battery (Bat) under five different proposed load demands (as electricity serving plans), reaching to the best economic, environmental, and space optimality systems. Three main sensitivity analyses upon the capacity shortage/inflation rate, capital cost of PV/WT, and solar global horizontal irradiation (GHI)/wind speed show the effect of changing the mentioned parameters on the economic, environment, and optimized systems' sizes. It is observed that increase in the solar radiation and/or wind speed leads to reduction in the optimal size of designed systems. [ABSTRACT FROM AUTHOR]

Phase change materials (PCMs) have garnered significant attention in the realm of smart buildings due to their transformative impact on building structures and energy efficiency. In the context of smart buildings, incorporating PCMs into construction elements, such as walls or ceilings, enables them to act as thermal energy storage units. This dynamic thermal behavior helps regulate indoor temperatures by absorbing excess heat during warmer periods and releasing it when the environment cools. As a result, smart buildings equipped with PCM technologies exhibit enhanced energy efficiency, reduced reliance on traditional heating, ventilation, and air conditioning (HVAC) systems and a more sustainable overall operation. Using EnergyPlus numerical simulation and a novel hybrid multilevel particle swarm optimization and convolutional neural network (H-MPSO-CNN) model, the performance of PCM in walls and ceilings of Namangan, Uzbekistan and Najran, Saudi Arabia climates was investigated in this study. The study assessed the impact of variables such as melting temperature and optimal location of PCM on heating and cooling load consumption. The results showed that PCM with melting temperatures of 23°C and 25°C had the greatest impact in the Namangan climate, while PCM with a temperature of 25°C had the greatest impact in Najran. The study also determined the best location for PCM on walls and roofs. It was determined that such a system is better suited to Najran's hot and dry climate. Heating and cooling loads in Namangan can be reduced by 12.39 and 16.01%, respectively, by installing PCM systems in the building's roof and walls. Similarly, a single-layer PCM system in Najran can reduce heating and cooling energy consumption by 9.97 and 12.11%, respectively. The goal of this study was to reduce the number of hours when the building was not thermally comfortable and to optimize heating and cooling load consumption. [ABSTRACT FROM AUTHOR]

The modern era has witnessed a surge in energy consumption and its dependence on fossil fuels, which are harmful to the environment, prompting researchers to examine techniques for regulating energy usage in buildings, specifically with regard to residential electricity consumption. The pursuit of net-zero energy consumption and low carbon emission buildings is a significant undertaking that nations across the globe are actively endeavoring to accomplish. In order to accomplish this goal, the structure in question must efficiently oversee its overall energy usage while concurrently capitalizing on sustainable energy sources. The precise estimation of future electricity usage in buildings is an essential element in the process of energy efficiency planning and optimization. The present study introduces a soft computing methodology and data decomposition as approaches for evaluating the energy usage of residential structures. An innovative machine learning approach is introduced for the purpose of estimating the initial cost required to construct a green structure that consumes no net energy. By utilizing wavelet decomposition, it is possible to determine how to transform the structure into one that is intelligent and energy efficient. Following wavelet parallel converter analysis, the data were processed with an estimator model based on an ideal neural network. The results indicate that the mean estimation errors for recurrent neural network, Autoregressive fractionally integrated moving average (ARFIMA), and gene expression programming (GEP) were reduced by 72%, 65%, and 77%, respectively, using this method. Conversely, when the proposed methodology is applied to the smart management of building energy consumption, the examined structures experience an average reduction of 8% in energy consumption. Moreover, the outcomes of CO2 gas emissions demonstrate that the suggested model possesses the capability to accurately forecast CO2 emissions. The study highlights the necessity of employing innovative techniques such as machine learning to decrease building energy usage and CO2 emissions. The discovery of these results can assist policymakers and stakeholders in the energy sector in advancing the adoption of smart building technologies. [ABSTRACT FROM AUTHOR]

The utilization of solar energy, an abundant and renewable resource, shows great promise. Various technologies have been employed to capture this vast energy potential. In this study, the effectiveness of natural dye-sensitized solar cells (DSSCs) was explored through experimental and computational methods. Organic dyes derived from specific plant species were examined, with a solvent mixture of ethanol, methanol, and tetrahydrofuran used for extraction. A UV–vis spectrophotometer was utilized to measure the sensitizers' optical characteristics. The study employed density functional theory (DFT), implemented in Gaussian 09 W software, to perform computational calculations. The B3LYP method and a basis set of 6-31G++(d,p) were chosen for optimizing the geometries and energetics of dye molecules. The DFT results indicated that the dye molecules had a bandgap in the range of 2.16–2.38 eV. The photochemical performance of all four fabricated DSSC-based devices was evaluated to be between 0.33% and 1.04%, and the Euryops pectinatus sensitizer demonstrated the highest efficiency of 1.04% among the studied sensitizers. [ABSTRACT FROM AUTHOR]

Metal–air batteries have higher theoretical specific energies than existing rechargeable batteries including Li-ion batteries. Among metal–air batteries, the Na–O2 battery has gained much attention due to its low discharge/charge overpotentials (∼100 mV) at relatively high current densities (0.2 mA/cm2), high electrical energy efficiency (90%), high theoretical energy density, and low cost. However, there is no information reported regarding the effect of CO2 contamination in non-aqueous Na–air batteries. Density functional theory has, here, been applied to study the effect of low concentrations of CO2 contamination on NaO2 and Na2O2 growth/depletion reaction pathways and overpotentials. This was done on step surfaces of discharge products in non-aqueous Na–air batteries. Adsorption energies of CO2 at various nucleation sites for both step surfaces were determined, and results revealed that CO2 preferentially binds at the step valley sites of (001) NaO2 and 1 1 ¯ 00 Na2O2 surfaces with binding energies of −0.65 eV and −2.67 eV, respectively. CO2 blocks the step nucleation site and influences the reaction pathways and overpotentials due to carbonate formation. The discharge electrochemical overpotential increases remarkably from 0.14 V to 0.30 V and from 0.69 V to 1.26 V for NaO2 and Na2O2 surfaces, respectively. CO2 contamination is thus drastically impeding the growth/depletion mechanism pathways and increases the overpotentials of the surface reaction mechanism, hampering the performance of the battery. Avoiding CO2 contamination from intake of gas and electrolyte decomposition is thus critical in development of Na–air batteries. [ABSTRACT FROM AUTHOR]

The precise assessment and evaluation of global solar radiation (GSR) is crucial for designing effective solar energy systems. However, in developing countries like Ethiopia, the cost and maintenance of measuring devices are inadequate. As a result, researchers have explored alternative methods such as empirical models to estimate GSR. This article proposes using artificial neural networks (ANN) to predict daily and monthly averaged horizontal GSR (HGSR) around Fiche town of Ethiopia, using various network types. The input variables were divided into training (70%) and testing (30%) sets to evaluate the network types, with the sigmoid function used as the activation function at the hidden layer and a linear function for the output layer. The predicted mean daily and monthly HGSR ranges from 3.282 kWh/m²/day to 6.967 kWh/m²/day and 4.628 kWh/m² to 6.613 kWh/m² respectively. The values obtained were compared to those provided by NASA observation data and were found to be within acceptable limits. Statistical metrics of MAPE, MSE, and RMSE show that CFBP, FFBP, LR, and EBP are better network types for estimating mean daily HGSR, while EBP, FFBP, CFBP, and LR are better for estimating mean monthly HGSR. Overall, all network types of ANN accurately predicted the mean daily and monthly HGSR. In general, the findings of this study indicated that the location had promising solar energy for producing electricity and for various uses. [ABSTRACT FROM AUTHOR]

Due to a lack of solar radiation measurement instruments in various regions across the world, particularly in Ethiopia, many empirical models have been developed to estimate global solar radiation. Reliable global solar radiation infor- mation is essential for the design and development of solar energy systems.Thus, the aim of this paper is to investigate the feasibility of using Artificial Neural Network (ANN) to predict mean daily global solar radiation (GSR) and to compare the performance of ANN and empirical models based on sunshine in estimating mean daily global solar irradiation on a horizontal surface.For the ANN model, network inputs were daily data for a number of days, average daily data (sunshine hours, maximum temperature, minimum temperature, wind speed, relative humidity, and pressure), and while daily averaged global solar radiation was the network output.After training, the model had R values of 0.891 for training, 0.932 for validation, 0.920 for testing, and 0.903 for all, with validation performance of 0.0797.The results of the ANN model were compared to the empirical equations using statistical error evaluation (MBEANN = 0.0005, RMSEANN = 0.3310, and R2ANN = 0.7998).The agreement between the values of NASA and estimated values reveals that the ANN exhibits good performance in studying daily GSR. The result also shows that the mean daily GSR is varying between 3.675KWh/m² (August-1) and 6.935 KWh/m² (April-19) in the study site. [ABSTRACT FROM AUTHOR]

AbstractThe aim of the current study is to examine the effect of an ellipse-protruded twisted strip on thermo-hydraulic performancein a circular tube. When elliptically protruded twisted tape tube is compared to plain twisted tape tube, the deviation is between 30% and 33%. The modeling results of Nusselt number vs Reynolds number of the plain twisted tape tube with actual data show a variance of 9.5%. A plain tube has a friction factor of one, whereas a plain twisted tape tube has seven times the friction factor. An elliptical protruding twisted tape tube has 1.5 times the thermal performance factor of a regular twisted tape tube. A correlation between the Nusselt number and the friction factor for the elliptical protruded twisted tape domain has been given; the expected and numerical results are 7.5–8.5 and 6–8% of each other. [ABSTRACT FROM AUTHOR]

High theoretical capacity, high thermal stability, the low cost of production, abundance, and environmental friendliness are among the potential attractiveness of Li 2 MnSiO 4 as a positive electrode (cathode) material for rechargeable lithium-ion batteries. However, the experimental results indicated poor electrochemical performance in its bulk phase due to high intrinsic charge transfer resistance and capacity fading during cycling, which limit its large-scale commercial applications. Herein, we explore the surface stability and various lithium-ion diffusion pathways of Li 2 MnSiO 4 surfaces using the density functional theory (DFT) framework. Results revealed that the stability of selected surfaces is in the following order: (210) > (001) > (010) > (100). Moreover, the Wulff-constructed equilibrium shape revealed that the Li 2 MnSiO 4 (001) surface is the most predominant facet, and thus, preferentially exposed to electrochemical activities. The Hubbard-corrected DFT (DFT + U , with U = 3 eV) results indicated that the bulk insulator with a wide band gap ( E g = 3.42 eV) changed into narrow electronic ( E g = 0.6 eV) when it comes to the Li 2 MnSiO 4 (001) surface. Moreover, the nudged elastic band analysis shows that surface diffusion along the (001) channel was found to be unlimited and fast in all three dimensions with more than 12-order-of-magnitude enhancements compared with the bulk system. These findings suggest that the capacity limitation and poor electrochemical performance that arise from limited electronic and ionic conductivity in the bulk system could be remarkably improved on the surfaces of the Li 2 MnSiO 4 cathode material for rechargeable lithium-ion batteries., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)