Counterflow heat exchangers have been extensively investigated and optimized. However, almost all the literature indicates that the investigations have been performed under the assumption of constant fluid properties. In this study, a dynamic simulation was performed for counterflow plate heat exchangers using MATLAB/SIMULINK modeling considering variable fluids properties. Temperature distribution of hot flow, cold flow, lower, inner, and top wall in counterflow was simulated under transient conditions in order to observe the effects of temperature difference and the errors due to the constant temperature assumption with disturbances in the inlet temperatures. A thermodynamic model of the counterflow plate heat exchanger divided into n cells imaginarily was developed. Then, equations defining heat and mass transfer were considered for two-dimensional heat transfer: between hot flow, cold flow, and heat exchangers' walls, regarding the variation of thermophysical properties of flows and heat exchanger materials by temperature. Hence, the differentiation of temperature distributions of cells in heat exchangers was instantly observed under transient operating conditions to discover the effects of input parameters such as wall material thermal properties, fluids thermal properties, and fluids flowrates in detail. According to the results obtained, 43% and 23% errors were observed in engine oil and ethylene glycol between fixed and variable thermophysical properties. In addition, heat exchanger wall temperatures with constant and variable thermophysical properties showed considerable differences in the first cells of approximately 20℃ for the upper wall, the hot side, and in the last cells of approximately 10℃ for the lower wall, the cold side.

The Heating, Ventilation, and Air Conditioning (HVAC) industry is always on the lookout for new and improved technology to help with their quest for environmentally friendly and economically viable cooling solutions. Investigating nanotechnology as a means to enhance HVAC system performance is an encouraging field of study. This research looks at the possibility of installing nano-fluid cooling jackets on the outside of condensers used in air conditioners. In particular, it examines how their distinct characteristics can improve the system's overall performance and speed up the transfer of heat. The primary area of study is the impact of copper and alumina nano-fluids on air cooling system efficiency. This investigation's primary objective is to determine the impact of these nano-fluids on heat transfer efficiency. The experimental strategy makes use of nano-fluids, which have unique features, to increase heat transfer rates by acting as an external medium around the condenser. By employing this methodology, the research aims to shed light on the potential uses of nano-fluids in HVAC systems. By conducting in-depth tests and analyses, this study aims to fill gaps in our knowledge about the pros and cons of using nano-fluids in HVAC systems. By taking a fresh tack, we can better understand the theoretical underpinnings of nano-fluid applications and highlight their real-world applications in enhancing air conditioning performance and efficiency. Aside from lowering the costs of pressure loss and pipe wall erosion, the results show that nano-fluids are more stable because they contain particles that are only micrometers or millimeters in size. They also conduct heat better than traditional models predict.

Efficient production management in energy storage systems requires accurate performance modeling of lithium-ion batteries (LIBs), especially under varying load conditions. This study presents a novel simplified lumped parameter approach that predicts battery performance with minimal reliance on internal design specifics. The approach uses a black-box modeling technique to estimate critical parameters—ohmic overpotential, diffusion time constant, and charge exchange current—via a Levenberg–Marquardt optimization algorithm, based on experimental voltage, current, and open circuit voltage data. Results demonstrate high accuracy in predicting cell voltage over dynamic load cycles, achieving standard deviations of 0.015 V and 0.014 V in parameter estimation and load prediction, respectively. These findings have significant implications for advancing energy storage systems by enabling more sustainable production management practices, reducing resource wastage, and improving operational efficiency. By enhancing the adaptability of production processes while maintaining high performance, this model contributes to achieving long-term goals of sustainability and scalability in energy storage applications.

Particulate matter, such as dust, soot, and dirt, are moved by wind and other air movements and accumulated on top of the PV panel surface, excluding solar penetration and hence affecting the extent of power produced. Six levels of dirt density were prepared for this experiment to evaluate the effects of dust and soot on PV efficiency in the Al-Doura district, Baghdad, where emissions from power plants and oil refineries are higher than a district located 10 km away in terms of University of Technology, Baghdad, Iraq. The research showed that soot and dust accumulation on the panels reduced the flow of sunlight and intensified hot spots, thereby reducing the power generation efficiency. Dust alone reduces efficiency by up to 48%, while soot by 54%. The greater soot is ascribed to its deposit, which increases panel temperatures more than dust does. The effect of deposition density on temperature rise was proportional, to be exact, 0.54℃ to 2℃ increase per dirty deposit.

This research examines the influence of anode surface area on the efficacy of aluminium-air batteries. Three varieties of aluminium anodes were produced: non-mesh, one-step nanomesh, and two-step nanomesh. The nanomesh structures were fabricated via a multi-step anodization procedure employing phosphoric acid, leading to enhanced surface area and pore density. Scanning electron microscopy demonstrated that the 2-step nanomesh anode possessed the highest average pore diameter of 180 nm, resulting in a substantial enhancement of active surface area. Electrochemical characterization methods, such as galvanostatic discharge testing, electrochemical impedance spectroscopy, and cyclic voltammetry, were utilized to assess battery performance. The findings indicated that the 2-step nanomesh anode had superior electron discharge rate, ionic conductivity, and oxidation stability relative to the 1-step nanomesh and non-mesh anodes. The 2-step nanomesh anode attained a specific capacity of 1.92 mAh and a power output of 59.71 mW, exceeding the performance of alternative anode topologies. The improved battery performance is due to the enlarged active surface area of the anode, which promotes more efficient ion transport and electrochemical processes. The findings underscore the importance of anode surface modification in enhancing the performance of aluminium-air batteries and offer insights for the design of high-capacity, high-power energy storage systems for diverse applications.

In this era of innovation, numerous researchers are striving to combat global warming and transform our planet into a greener and more sustainable environment. Their efforts focus primarily on reducing the release of harmful gases produced by conventional energy sources. This challenge can be mitigated by harnessing abundant renewable resources for various applications. This study explores the application of three distinct models, namely Convolutional Neural Network Long Short-Term Memory (CNN-LSTM), and Exponential Smoothing, for the prediction of solar wind patterns. The research aims to investigate the comparative performance of these forecasting techniques in capturing the dynamics of solar wind data. By leveraging the capabilities of deep learning through CNN-LSTM and the simplicity of Exponential Smoothing, we assess their effectiveness in providing accurate predictions for solar wind behavior. The results of this investigation have consequences for space weather forecasting and the understanding of solar-terrestrial interactions. Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R-squared (R²) are utilized for evaluating the manner in which a regression model performs. Wind speed and solar irradiation are predicted using four models are Exponential Smoothing model, LSTM model, CNN-LSTM model, and Smooth CNN-LSTM Model. The Exponential Smoothing model performs less well compared to the others, especially in terms of accuracy (MSE, RMSE) and explanatory power (R²). “LSTM” and “CNN-LSTM” models have similar performance, with “CNN-LSTM” slightly outperforming “LSTM” with regard to RMSE and R². The “Smooth CNN-LSTM” model outperforms the other algorithms across all metrics, showcasing superior accuracy, precision, and explanatory power.

The demand for vehicles has increased significantly in the last two decades as a result of the rise in the global population and improved living capacity. The popularity of using products from bio-based sources has also increased due to the need to reduce air pollution resulting from burning fossil fuels while maintaining or increasing the efficiency of engines. In this study, biodiesel (produced from restaurant waste oil) with small amounts of butanol alcohol was added to conventional Iraqi diesel and tested. Adding butanol as a low-dose stimulant to the diesel-biodiesel mixture to improve engine performance and eliminate pollutants is a modern method that has not yet been approved and requires many studies before it is accepted as a vehicle fuel. The engine showed good performance when operating with the proposed mixtures under different load conditions. The D90W5B5 mixture provided the highest cylinder pressure, which was superior to diesel. The tested blends, D90W5B5, D80W10B10, D70W15B15, and W100, caused a decrease in NOx emissions compared to diesel by 16.57%, 25.48%, 33.14%, and 39.76%, respectively. As well as reduced the total suspended particles by 19.1%, 22.02%, 34.66% and 49.7%, respectively. One of the most important results obtained is that these mixtures reduced the Sulfur dioxide (SO₂) and Hydrogen sulfide (H₂S) emissions by 3.9%, 8.66%, 10.98%, and 97.7%, for the first pollutant and by 6.15%, 8.89%, 15.57%, and 97.8%, for the second one, respectively.

Investigating energy use is critical because it addresses the decreasing energy supply. The majority of global energy use is nonrenewable, with much of it coming from fossil fuels emitting greenhouse gases. As a result, energy consumption research is vital for understanding energy usage trends and developing methods to reduce energy use or employ renewable energy sources. This study investigates the impact of industry, service sectors, urbanization, exports, and inflation on energy consumption in a panel of 38 nations from 2019 to 2023. Based on the static panel approach, the key findings of the Pool model reveal that the industrial and service sectors have a positive and significant impact on energy consumption, emphasizing the vitality of these sectors as major energy users. The Fixed Effects model (FEM) suggests that the industrial and service sectors have a significant and negative impact on energy usage. Furthermore, the FE model reveals that urbanization and export significantly and negatively impact energy consumption. In the Pool model, inflation is associated positively with energy consumption. The dynamic panel approach additionally suggests that the industrial and service sectors significantly impact energy consumption in the investigated countries. Exports have a significant and negative impact on energy consumption. The CPI, a measure of inflation, significantly and positively impacts energy consumption. The findings of this study provide helpful policy recommendations for identifying the significant variables influencing world energy consumption. Policymakers in the examined countries must promote energy consumption efficiency initiatives and shift to renewable energy sources.

This study investigated the activities surrounding crude oil and its impact on the economic performance of Nigeria. Therefore, some economic variables surrounding crude oil in Nigeria was analysed. Most multivariate economic variables suffer the problem of multicollinearity, though often not tested or sometimes ignored by researchers. The presence of multicollinearity among predictor variables often leads to bias estimate. In this study, explorative data analyses were conducted on the data of petroleum variables and gross domestic product and modelled using the Cobb-Douglas Production Function. Multicollinearity was detected in the full model and corrected. The results showed that Real Gross Domestic Product (RGDP) have a significant positive relationship with crude oil Revenue and petroleum to GDP in the full model. The crude oil consumption, and Petroleum to GDP significantly impact the RGDP in the reduced model. Based on the findings of this study, it is recommended that the government implement policies to preserve and manage the oil sector effectively to encourage international trade and increase revenue at the same time make petroleum products available for local use in line with sustainable development goals (SDGs) 7, to ensure that there is affordable, sustainable and modern energy for all by 2030.

Thermoelectric are a very interesting source of electrical energy. There is a lot of exploration about the use of thermoelectric and the potential that exists. Thermoelectric materials are of particular concern to obtain better efficiency. This research aims to investigate the performance of a novel thermoelectric generator (TEG) design based on Bi₂S₃ sandwich materials through numerical investigation. Key focus areas include power output, efficiency, compatibility for future applications, and temperature distribution characteristics. Data shows that this design has increased efficiency by 4.2%. When performing experimental setup, it is important to offer more reliable data quality, these simulation results offer a pre-plenum data approach to minimize omissions.

This study addresses the critical challenge of enhancing thermal efficiency in industrial firetube boilers within the fishing industry, a sector burdened by significant fuel consumption and associated costs amidst rising fuel prices; achieving even marginal improvements in boiler efficiency can result in substantial economic savings and environmental benefits. Utilizing the Peruvian technical standard for efficiency determination, alongside recommendations from boiler manufacturers and operational conditions, this research employs artificial neural networks (ANNs) to model and predict efficiency outcomes based on various operational parameters, including fuel type and combustion conditions specifically, the study explores the impact of excess air and fuel regulation on thermal efficiency and pollutant emissions, employing applied research methods and a comprehensive analysis of boiler operation at 80% and 100% load conditions. Results demonstrate the capability of neural network models to accurately predict thermal efficiency, with optimized configurations achieving significant reductions in CO₂ and CO emissions by 43% and 55%, respectively. The findings underscore the potential for neural network applications in optimizing boiler operations, offering a pathway to economic and environmental improvements in industrial processes. The study concludes with optimal operational parameters that balance efficiency gains with emission reductions, highlighting the practical implications for the fishing industry and beyond.

Population growth and technological development are fueling the increasing demand for electricity in Indonesia. By 2023, electricity consumption in Indonesia has reached 1,285 KWH, mostly met by non-renewable energy. This condition raises concerns about the sustainability of energy supply. On the other hand, Indonesia has great potential to utilize ocean wave energy as a source of electricity. The novelty of this research lies in the Generalized Linear Model-based Gamma Regression modelling approach to evaluate the electrical energy potential of ocean wave energy in 175 Indonesian waters. The focus of this research lies on the specific analysis of the impact of wave type on power potential, while wind speed and weather factors have no significant influence. In addition, the selection of the best model was conducted using the Root Mean Square Error (RMSE) approach, which shows that the model predictions are getting closer to the actual values. The results show that low and medium wave types significantly reduce the power potential compared to calm waves, by 0.0000083% and 0.0000113%, respectively. These findings make an important contribution to understanding the potential of ocean wave energy as a renewable energy source in Indonesia.

India's power sector is witnessing unprecedented growth, driving the need for increased generation capacity. To support this demand, a robust and efficient transmission system is essential. As the construction of new transmission lines becomes more frequent, it is vital to optimize their design to remain profitable in a deregulated market. This paper presents a cutting-edge method for optimal transmission expansion planning, utilizing the MW-KM method and a Cost/Benefit index for enhanced optimization. The proposed approach effectively identifies the most economically viable expansion strategies. Additionally, the paper explores the use of High Temperature Low Sag (HTLS) conductors as a strategic solution in scenarios where traditional methods are either costly or hindered by Right of Way challenges (RoW). This holistic approach ensures that India’s growing energy needs are met with both efficiency and cost-effectiveness. In this paper, a case study on the 5-bus system test system carried out and income of each line calculated on MW-KM method, the number of new transmission lines required is decreased to three from seven by using cost benefit analysis and increased the avg line revenue of the system by 25%. The RoW issues of the planned system successfully addressed in this paper.

Hydrogen sulphide (H2S) corrosion is a significant problem in the oil industry. It affects pipeline integrity and generates high maintenance and repair costs. This work aims to evaluate global trends and the effectiveness of different types of H2S corrosion inhibitors applied in oil pipelines through bibliometrics and a systematic review, analysing their future implications for developing anticorrosion strategies during the last decade. This process was developed in three phases: (i) baseline data and focusing, (ii) scientific metrics, and (iii) literature review using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method. The results show sustained growth in publications, focusing on green inhibitors and nanotechnology-based technologies that achieve efficiencies of more than 90% in the laboratory. However, gaps persist in field validation and designing multifunctional composites for extreme environments. These findings suggest prioritising applied research into new self-healing materials and coatings, as well as industrial-scale evaluation protocols to optimise the protection of critical infrastructure in the oil industry.