Experimental Study on Performance Enhancement of a Photovoltaic Module Incorporated with CPU Heat Pipe-A 5E Analysis.

As is already known, solar photovoltaic (PV) technology is a widely accepted technology for power generation worldwide. However, it is scientifically proven that its power output decreases with an increase in the temperature of the PV module. Such an important issue is controlled by adopting a number of cooling mechanisms for the PV module. The present experimental study assesses the effect of a fanless CPU heat pipe on the performance of a PV module. The experiment was conducted in June in real weather conditions in Yekaterinburg, Russian Federation. The comparative analysis of two PV panels (i.e., cooled, and uncooled) based on the electrical energy, exergy performance, economic, embodied energy and energy payback (5E) for the two systems is presented and discussed. The key results from the study are that the average temperature reduction from the cooling process is 6.72 °C. The average power for the cooled panel is 11.39 W against 9.73 W for the uncooled PV panel; this represents an increase of 1.66 W for the cooled module. Moreover, the average improvements in the electrical efficiency, and embodied energy recorded for a cooled PV panel 2.98%, and 438.52 kWh, respectively. Furthermore, the calculations of the levelized cost of energy (LCE) for the cooled PV panel indicate that it can range from 0.277-0.964 USD/kWh, while that for the uncooled PV panel also ranges from 0.205-0.698 USD/kWh based on the number of days of operation of the plant.

Modeling of Efficient Control Strategies for LCC-HVDC Systems: A Case Study of Matiari-Lahore HVDC Power Transmission Line.

With the recent development in power electronic devices, HVDC (High Voltage Direct Current) systems have been recognized as the most prominent solution to transmit electric power economically. Today, several HVDC projects have been implemented physically. The conventional HVDC systems use grid commutation converters, and its commutation relies on an AC system for the provision of voltage. Due to this reason, there are possibilities of commutation failure during fault. Furthermore, once the DC (Direct Current) system power is interrupted momentarily, the reversal of work power is likely to cause transient over-voltage, which will endanger the safety of power grid operation. Hence, it is necessary to study the commutation failure and transient over-voltage issues. To tackle the above issues, in this paper, the dynamic and transient characteristics of Pakistan's first HVDC project, i.e., the Matiari-Lahore ±660 kV transmission line has been analyzed in an electromagnetic transient model of PSCAD/EMTDC. Based on the characteristics of the DC and the off-angle after the failure, a new control strategy has been proposed. The HVDC system along with its proposed control strategy has been tested under various operating conditions. The proposed controller increases the speed of fault detection, reduces the drop of AC voltage and DC and suppresses the commutation failure probability of LCC-HVDC (line commutated converter- high voltage direct current).

Exploring the 3E, hydrogen, and ammonia creation potentials of a concentrated solar power (CSP) plant using an air-cooled condenser.

The need to move to more sustainable energy generation has become a major concern among world leaders due to the debilitating effect of greenhouse gases on the environment. Africa has the greatest potential to transition to more sustainable energy sources due to its enormous renewable energy resource potential, particularly solar. This study thus assessed the potential of generating power using a concentrated solar tower power plant (CSTP) at three different locations in Algeria. The study evaluated the system's technical, environmental, economic, and employment creation potential and analyzed the hydrogen and ammonia creation potential using the electricity produced by the CSTP system. Naama, Laghouat, and Ghardaia recorded annual energies of 507 GWh, 502 GWh, and 547 GWh, with capacity factors of 57.6%, 57.6%, and 62%, respectively. A real levelized cost of energy ranging between 7.72 and 8.47 cent$/kWh was obtained. A total of 8530 tons of nitrogen and 1844 tons of hydrogen will be theoretically needed to produce ammonia (fertilizer) for 500,000 hectares of arable land for agricultural activities. In addition, using hydrogen from the CSTP system to produce the estimated ammonia will save 6124.56 tons of CO2 emissions from polluting the environment annually. The creation of thousands of direct and indirect jobs will significantly benefit Algerians. The study concluded with some policy recommendations based on its findings.

Enhanced multi-layer perceptron for CO2 emission prediction with worst moth disrupted moth fly optimization (WMFO).

This study introduces the Worst Moth Disruption Strategy (WMFO) to enhance the Moth Fly Optimization (MFO) algorithm, specifically addressing challenges related to population stagnation and low diversity. The WMFO aims to prevent local trapping of moths, fostering improved global search capabilities. Demonstrating a remarkable efficiency of 66.6 %, WMFO outperforms the MFO on CEC15 benchmark test functions. The Friedman and Wilcoxon tests further confirm WMFO's superiority over state-of-the-art algorithms. Introducing a hybrid model, WMFO-MLP, combining WMFO with a Multi-Layer Perceptron (MLP), facilitates effective parameter tuning for carbon emission prediction, achieving an outstanding total accuracy of 97.8 %. Comparative analysis indicates that the MLP-WMFO model surpasses alternative techniques in precision, reliability, and efficiency. Feature importance analysis reveals that variables such as Oil Efficiency and Economic Growth significantly impact MLP-WMFO's predictive power, contributing up to 40 %. Additionally, Gas Efficiency, Renewable Energy, Financial Risk, and Political Risk explain 26.5 %, 13.6 %, 8 %, and 6.5 %, respectively. Finally, WMFO-MLP performance offers advancements in optimization and predictive modeling with practical applications in carbon emission prediction.

Retraction notice to "Seasonal correlation of aerosols with soil moisture, evapotranspiration, and vegetation over Pakistan using remote sensing" [Heliyon 9 (2023) e20635].

[This retracts the article DOI: 10.1016/j.heliyon.2023.e20635.].

Review of the marine energy environment-a combination of traditional, bibliometric and PESTEL analysis.

Marine renewable energy is regarded as a nascent renewable energy resource that is less utilized due to a number of challenges in the sector. This paper focused on using both traditional and bibliometric analysis approaches to review the marine energy industry. It also assessed the various opportunities and challenges in the sector beyond technological challenges using PESTEL analysis. The results from the study identified the availability of renewable energy targets, international and national greenhouse gas (GHG) emissions reduction targets, job creation, skill transfer from offshore industries, renewable support, and low GHG emissions as the major opportunities for the sector. The challenges in the sector include the lack of commonality in device designs, high initial capital costs, lack of appropriate legal and regulatory frameworks, lack of funding, fragmentations in regulatory institutions, bad macro-economic indicators in some countries, environmental challenges, the survivability of the various technologies in the harsh oceanic environment, and strong competition from other renewable energy sources. The outcome of the bibliometric analysis spanning from 2013 to 2023 shows that tidal power is the focus of research in the field, and most studies are either focused on ways to improve its efficiency in terms of technology or on the identification of resource potentials for the siting of the various marine renewable power systems. Recommendations such as strong cooperation between the government and private sector, increased public education, collaboration with existing players in the marine sector, and increased research and development, among others, were proposed for the development of the sector.

S-shaped grey wolf optimizer-based FOX algorithm for feature selection.

The FOX algorithm is a recently developed metaheuristic approach inspired by the behavior of foxes in their natural habitat. While the FOX algorithm exhibits commendable performance, its basic version, in complex problem scenarios, may become trapped in local optima, failing to identify the optimal solution due to its weak exploitation capabilities. This research addresses a high-dimensional feature selection problem. In feature selection, the most informative features are retained while discarding irrelevant ones. An enhanced version of the FOX algorithm is proposed, aiming to mitigate its drawbacks in feature selection. The improved approach referred to as S-shaped Grey Wolf Optimizer-based FOX (FOX-GWO), which focuses on augmenting the local search capabilities of the FOX algorithm via the integration of GWO. Additionally, the introduction of an S-shaped transfer function enables the population to explore both binary options throughout the search process. Through a series of experiments on 18 datasets with varying dimensions, FOX-GWO outperforms in 83.33 % of datasets for average accuracy, 61.11 % for reduced feature dimensionality, and 72.22 % for average fitness value across the 18 datasets. Meaning it efficiently explores high-dimensional spaces. These findings highlight its practical value and potential to advance feature selection in complex data analysis, enhancing model prediction accuracy.

DGS-SCSO: Enhancing Sand Cat Swarm Optimization with Dynamic Pinhole Imaging and Golden Sine Algorithm for improved numerical optimization performance.

This paper introduces DGS-SCSO, a novel optimizer derived from Sand Cat Swarm Optimization (SCSO), aiming to overcome inherent limitations in the original SCSO algorithm. The proposed optimizer integrates Dynamic Pinhole Imaging and Golden Sine Algorithm to mitigate issues like local optima entrapment, premature convergence, and delayed convergence. By leveraging the Dynamic Pinhole Imaging technique, DGS-SCSO enhances the optimizer's global exploration capability, while the Golden Sine Algorithm strategy improves exploitation, facilitating convergence towards optimal solutions. The algorithm's performance is systematically assessed across 20 standard benchmark functions, CEC2019 test functions, and two practical engineering problems. The outcome proves DGS-SCSO's superiority over the original SCSO algorithm, achieving an overall efficiency of 59.66% in 30 dimensions and 76.92% in 50 and 100 dimensions for optimization functions. It also demonstrated competitive results on engineering problems. Statistical analysis, including the Wilcoxon Rank Sum Test and Friedman Test, validate DGS-SCSO efficiency and significant improvement to the compared algorithms.

Towards greener futures: SVR-based CO2 prediction model boosted by SCMSSA algorithm.

This research presents the utilization of an enhanced Sine cosine perturbation with Chaotic perturbation and Mirror imaging strategy-based Salp Swarm Algorithm (SCMSSA), which incorporates three improvement mechanisms, to enhance the convergence accuracy and speed of the optimization algorithm. The study assesses the SCMSSA algorithm's performance against other optimization algorithms using six test functions to show the efficacy of the enhancement strategies. Furthermore, its efficacy in improving Support Vector Regression (SVR) models for CO2 prediction is assessed. The results reveal that the SVR-SCMSSA hybrid model surpasses other hybrid models and standard SVR in terms of training and prediction accuracy by obtaining 95 % accuracy. Its swift convergence, precision, and resistance to local optima position make it an excellent choice for addressing complex problems such as CO2 prediction, with critical implications for sustainability efforts. Moreover, feature importance analysis by SVR-SCMSSA offers valuable insights into the key contributors to CO2 prediction in the dataset, emphasizing the significance and impact of factors such as fossil fuel, Biomass, and Wood as major contributors to CO2 emission. The research suggests the adoption of the SVR-SCMSSA hybrid model for more accurate and reliable CO2 prediction to researchers and policymakers, which is essential for environmental sustainability and climate change mitigation.

Chaotic opposition learning with mirror reflection and worst individual disturbance grey wolf optimizer for continuous global numerical optimization.

The effective meta-heuristic technique known as the grey wolf optimizer (GWO) has shown its proficiency. However, due to its reliance on the alpha wolf for guiding the position updates of search agents, the risk of being trapped in a local optimal solution is notable. Furthermore, during stagnation, the convergence of other search wolves towards this alpha wolf results in a lack of diversity within the population. Hence, this research introduces an enhanced version of the GWO algorithm designed to tackle numerical optimization challenges. The enhanced GWO incorporates innovative approaches such as Chaotic Opposition Learning (COL), Mirror Reflection Strategy (MRS), and Worst Individual Disturbance (WID), and it's called CMWGWO. MRS, in particular, empowers certain wolves to extend their exploration range, thus enhancing the global search capability. By employing COL, diversification is intensified, leading to reduced solution stagnation, improved search precision, and an overall boost in accuracy. The integration of WID fosters more effective information exchange between the least and most successful wolves, facilitating a successful exit from local optima and significantly enhancing exploration potential. To validate the superiority of CMWGWO, a comprehensive evaluation is conducted. A wide array of 23 benchmark functions, spanning dimensions from 30 to 500, ten CEC19 functions, and three engineering problems are used for experimentation. The empirical findings vividly demonstrate that CMWGWO surpasses the original GWO in terms of convergence accuracy and robust optimization capabilities.

Insights from BRICS-T economies on the impact of human capital and renewable electricity consumption on environmental quality.

This paper evaluates the impact of electricity consumption from renewable and nonrenewable sources on the load capacity factor for BRICS-T nations using data from 1990 to 2018. The paper used linear and nonlinear autoregressive distributed lag (ARDL) approaches to explore these associations. The results of the Westerlund co-integration show long-run co-integration between load capacity factor and the independent variables. The results show that renewable electricity energy and human capital contribute to the sustainability of the environment, while electricity consumption, economic growth, and industrialization impede environmental sustainability. Similarly, the nonlinear effect of renewable electricity energy on LCF shows interesting findings. The positive (negative) shift in renewable electricity energy increases ecological sustainability in the BRICS-T nations. Furthermore, the Dumitrescu Hurlin panel causality gives credence to both linear and nonlinear ARDL results. The study suggests policy recommendations based on these results.

Transportation in Africa under Paris Agreement 2 °C goal-a review of electric vehicle potentials, cleaner alternative fuels for the sector, challenges, and opportunities.

Currently, internal combustion engines and fossil fuels are the major powertrains and fuels for the transportation sector, despite their enormous emissions. This study reviews the status of electric vehicles (EVs) in Africa, the potential barriers that affect their large-scale adoption, and the continent's potential to produce cleaner alternative fuels for transportation and find the strengths, weaknesses, opportunities, and threats (SWOT) to produce alternative fuels in Africa. First, the review looked at challenges confronting the adoption of EVs in Africa, some of which include high upfront costs, poor grid systems, frequent blackouts, inadequate infrastructure (roads and charging systems), and the dominance of used conventional vehicles. The various cleaner alternative fuels, i.e., hydrogen, biogas, ethanol, methanol, ammonia, biodiesel, and vegetable oils, and their potential on the African continent were also reviewed. The last section of the study employed the SWOT analytical tool to assess the strengths, weaknesses, opportunities, and threats in the alternative fuel industry in Africa. Factors such as competition from existing technologies, inadequate funding, feeble linkages between research and production, unsustainable policies for the sector, cultural constraints and lack of awareness, volatile financial systems, and low levels of foreign direct investment are some of the identified threats that could affect the development of alternative fuels in Africa. Similarly, factors such as the continuous decline in the cost of renewable energy technologies and heightened awareness of the adverse effects of GHG on the environment were identified as opportunities for the development of alternative fuels for the transport sector.

Performance analysis and socio-enviro-economic feasibility study of a new hybrid energy system-based decarbonization approach for coal mine sites.

The mining sector contributes to 4-7 % of global GHG emissions, of which 1 % are from scope 1 and scope 2 emissions, caused by operations such as electricity consumption used for the mining process. China heavily relies on coal for power generation, and the energy demand for coal production in the country is primarily met by fossil-based electricity. In addition, the transportation of the mined coal to various destinations within the supply chain is achieved by fossil fuel-powered transport systems. These daily activities of the Chinese coal sector further compound foreign and domestic pressure on China to limit its carbon emissions. The current study attempts to provide a solution to the situation by investigating the feasibility of adopting renewable energy sources for the process of coal mining in Northern China. The selected coal mine is one out of 643 coal mines in Shanxi Province, with a combined production capacity of ∼1 billion tonnes of coal per annum. In addition, the excess electricity generated has been designated to produce hydrogen on-site as a refueling source for hydrogen fuelled-trucks to replace diesel fuelled-trucks in transporting coal. The analysis has been completed using HOMER Pro software, and the key contributions are summarized as follows. 4 different scenarios comprising of standalone solar photovoltaic, wind turbine, and diesel generator have been designed in the current study to serve a daily load of 215 MWh and 2.4 t of electricity for coal mining and hydrogen for transport of 100 % of the mined coal by road using hydrogen fuel cell trucks, respectively. A technical, economic, environmental, and social feasibility analysis have been investigated in the present work. A grid-tied system is subsequently added to the base scenario and the results are compared against the base system in an attempt to identify the more feasible option between the two systems. Also, a sensitivity analysis has been conducted to reveal the performance of the base system amidst future uncertainties. The findings in the current work could prove beneficial to China's quest to reach carbon peak by 2030 and achieve carbon neutrality by 2060.

Antenna S-parameter optimization based on golden sine mechanism based honey badger algorithm with tent chaos.

This work proposed a new method to optimize the antenna S-parameter using a Golden Sine mechanism-based Honey Badger Algorithm that employs Tent chaos (GST-HBA). The Honey Badger Algorithm (HBA) is a promising optimization method that similar to other metaheuristic algorithms, is prone to premature convergence and lacks diversity in the population. The Honey Badger Algorithm is inspired by the behavior of honey badgers who use their sense of smell and honeyguide birds to move toward the honeycomb. Our proposed approach aims to improve the performance of HBA and enhance the accuracy of the optimization process for antenna S-parameter optimization. The approach we propose in this study leverages the strengths of both tent chaos and the golden sine mechanism to achieve fast convergence, population diversity, and a good tradeoff between exploitation and exploration. We begin by testing our approach on 20 standard benchmark functions, and then we apply it to a test suite of 8 S-parameter functions. We perform tests comparing the outcomes to those of other optimization algorithms, the result shows that the suggested algorithm is superior.

Single-tuned passive filter (STPF) for mitigating harmonics in a 3-phase power system.

Numerous integrals of the fundamental frequency are known as harmonics and can be found in power systems or electrical circuitry systems. Non-linear loads occasionally drain current or contains a varying impedance with each period of the AC voltage are often responsible for power system harmonics. This can result in system overheating, system losses, and equipment or system damage. In order to achieve the IEEE 519 power quality standard, filters are routinely employed to lower harmonic levels. In this work, we designed a single tuned passive filter (STPF) to minimize harmonics of sequence 5th, 7th, 11th, 13th, 17th, and 19th in a three (3) phase power system. The measurements were taken at the point of common coupling. To test the filter performance, the system and STPF were designed in MATLAB/Simulink, and the simulated results produced without and with STPF were compared. The [Formula: see text] was reduced from 14.93% down to 4.87% when STPF was connected which is within the IEEE 519-2022 standard; proving that the STPF was effective in decreasing the harmonics to the desired level.

What is the role of remittance and education for environmental pollution? - Analyzing in the presence of financial inclusion and natural resource extraction.

This study assessed the impact of gross domestic product (GDP), education, natural resources, remittances, and financial inclusion on carbon emissions in G-11 countries from 1990 to 2021. Based on the negative impact of pollution and the need for sustainable development, this study examined factors affecting CO2 emissions in G-11 countries using non-linear panel ARDL model. The study found that a positive GDP shock increases CO2 emissions in the short and long term, while a negative shock decreases emissions in the short term and increases emissions in the long term. Education was found to increase CO2 emissions in the long term but decrease them in the short term, emphasizing the need for education on combating emissions. Natural resources were also found to increase emissions in the long term, highlighting the need for government-defined institutions to minimize extraction effects and enforce transparency and accountability. Positive changes in personal remittances and financial inclusion were found to increase emissions in both the short and long term, suggesting the need for policies that encourage renewable energy sources and energy efficiency improvement. The study concludes that policymakers should prioritize efficient resource allocation, promote renewable energy usage, and enhance environmental awareness to achieve sustainable development goals in G-11 countries. The possible applications of this study include the use of the models to investigate the asymmetric effects on CO2 emissions. This model can be applied in future studies to examine the relationship between GDP, education, natural resources, personal remittances, financial inclusion, and CO2 emissions in other countries.

Evaluating the impact of digitalization, renewable energy use, and technological innovation on load capacity factor in G8 nations.

Ecosystems are in danger due to human-caused air, water, and soil pollution, so it is important to find the underlying causes of this issue and develop practical solutions. This study adds to environmental research gap by suggesting the load capability factor (LCF) and using it to look at the factors affectting environmental health. The load capacity factor simplifies monitoring environmental health by illustrating the distinction between ecological footprint and biocapacity. We examine the interplay between mobile phone users (Digitalization DIG), technological advancements (TEC), renewable energy use, economic growth, and financial development. This study assesses G8 economies' data from 1990 to 2018, using a Cross-Section Improved Autoregressive Distributed Lag CS-ARDL estimator and a cointegration test. The data shows that green energy, TEC innovation, and DIG are all beneficial for natural health. Based on the results of this study, the G8 governments should focus on environmental policies that promote economic growth, increase the use of renewable energy sources, guide technological progress in key areas, and encourage the development of digital information and communications technologies that are better for the environment.

Assessing the linkages of economic freedom and environmental quality in South Asian Countries: application of CS-ARDL.

We examine the linkages of economic freedom (ECF), energy use, and CO2 emissions in selected South Asian countries of Pakistan, India, Bangladesh, and Sri Lanka. Annual data from 1995 to 2018 are analyzed by employing second-generation methodologies. Cross-sectional autoregressive distributed lag (CS-ARDL) is used because this method incorporates the cross-sectional dependence among the data. This work uses three models, where the dependent variables are gross domestic product (GDP), CO2 emissions, and energy use. The findings reveal that ECF and energy use contributes to more economic development. ECF is improving air quality by lowering CO2 emissions. The findings suggest that these countries need to increase the percentage of renewable energy in their energy generation mix. At the same time, there is a need to integrate ECF with environmental awareness programs. This will not only increase air quality but also increase economic growth. GDP is found to be dependent on energy use; however, increased energy use from non-renewable also contaminates the environment. Therefore, South Asian countries need to invest more in research and development projects to promote renewable energy.

Toward a sustainable environment and economic growth in BRICS economies: do innovation and globalization matter?

Over the last few decades, environmental deterioration has accelerated significantly. Environmental degradation has been a subject of research across the world because of its impact on billions of people. However, there has been no international agreement on lowering the utilization of energy and CO2 emissions (CO2), while demand for fossil fuels grows in emerging economies. On the other hand, the recent COP26 summit brought all parties together to accelerate action toward reaching the goals of the Paris Agreement and the UN Framework Convention on Climate Change. Although previous research shows that international trade promotes positive socioeconomic outcomes, other experts argue that it contributes to natural resource shortages and ecological deterioration. Thus, the current research considers the effect of international trade, renewable energy use and technological innovation on consumption-based carbon emissions (CCO2), coupled with the role of financial development and economic growth in the BRICS economies between 1990 and 2018. Moreover, this research utilizes the common correlated effects mean group (CCEMG), augmented mean group (AMG) and Dumitrescu and Hurlin (2012) causality methods to assess these interrelationships. The study findings reveal that renewable energy use, exports and technological innovation mitigate CCO2, whereas economic growth and imports trigger CCO2 in the BRICS economies. The panel causality outcomes also reveal that all the variables except financial development can predict CCO2 emissions. Based on the study findings, we recommend the adoption of policies, regulations and the development of legislative frameworks that promote technological innovation and the shift toward sustainable energy.

A Critical Review of Renewable Hydrogen Production Methods: Factors Affecting Their Scale-Up and Its Role in Future Energy Generation.

An increase in human activities and population growth have significantly increased the world's energy demands. The major source of energy for the world today is from fossil fuels, which are polluting and degrading the environment due to the emission of greenhouse gases. Hydrogen is an identified efficient energy carrier and can be obtained through renewable and non-renewable sources. An overview of renewable sources of hydrogen production which focuses on water splitting (electrolysis, thermolysis, and photolysis) and biomass (biological and thermochemical) mechanisms is presented in this study. The limitations associated with these mechanisms are discussed. The study also looks at some critical factors that hinders the scaling up of the hydrogen economy globally. Key among these factors are issues relating to the absence of a value chain for clean hydrogen, storage and transportation of hydrogen, high cost of production, lack of international standards, and risks in investment. The study ends with some future research recommendations for researchers to help enhance the technical efficiencies of some production mechanisms, and policy direction to governments to reduce investment risks in the sector to scale the hydrogen economy up.