Explore the vast range of titles available.

The demand for fossil fuels is increasing because of globalization and rising energy demands. As a result, many nations are exploring alternative energy sources, and hydrogen is an efficient and practical alternative fuel. In the transportation industry, the development of hydrogen-powered cars aims to maximize fuel efficiency and significantly reduce exhaust gas emission and concentration. The impact of using hydrogen as a supplementary fuel for spark ignition (SI) and compression ignition (CI) engines on engine performance and gas emissions was investigated in this study. By adding hydrogen as a fuel in internal combustion engines, the torque, power, and brake thermal efficiency of the engines decrease, while their brake-specific fuel consumption increase. This study suggests that using hydrogen will reduce the emissions of CO, UHC, CO2, and soot; however, NOx emission is expected to increase. Due to the reduction of environmental pollutants for most engines and the related environmental benefits, hydrogen fuel is a clean and sustainable energy source, and its use should be expanded.

Adoption of hydrogen energy as an alternative to fossil fuels could be a major step towards decarbonising and fulfilling the needs of the energy sector. Hydrogen can be an ideal alternative for many fields compared with other alternatives. However, there are many potential environmental challenges that are not limited to production and distribution systems, but they also focus on how hydrogen is used through fuel cells and combustion pathways. The use of hydrogen has received little attention in research and policy, which may explain the widely claimed belief that nothing but water is released as a by-product when hydrogen energy is used. We adopt systems thinking and system dynamics approaches to construct a conceptual model for hydrogen energy, with a special focus on the pathways of hydrogen use, to assess the potential unintended consequences, and possible interventions; to highlight the possible growth of hydrogen energy by 2050. The results indicate that the combustion pathway may increase the risk of the adoption of hydrogen as a combustion fuel, as it produces NOx, which is a key air pollutant that causes environmental deterioration, which may limit the application of a combustion pathway if no intervention is made. The results indicate that the potential range of global hydrogen demand is rising, ranging from 73 to 158 Mt in 2030, 73 to 300 Mt in 2040, and 73 to 568 Mt in 2050, depending on the scenario presented.

The damaging effects of corona faults have made them a major concern in metal-clad switchgear, requiring extreme caution during operation. Corona faults are also the primary cause of flashovers in medium-voltage metal-clad electrical equipment. The root cause of this issue is an electrical breakdown of the air due to electrical stress and poor air quality within the switchgear. Without proper preventative measures, a flashover can occur, resulting in serious harm to workers and equipment. As a result, detecting corona faults in switchgear and preventing electrical stress buildup in switches is critical. Recent years have seen the successful use of Deep Learning (DL) applications for corona and non-corona detection, owing to their autonomous feature learning capability. This paper systematically analyzes three deep learning techniques, namely 1D-CNN, LSTM, and 1D-CNN-LSTM hybrid models, to identify the most effective model for detecting corona faults. The hybrid 1D-CNN-LSTM model is deemed the best due to its high accuracy in both the time and frequency domains. This model analyzes the sound waves generated in switchgear to detect faults. The study examines model performance in both the time and frequency domains. In the time domain analysis (TDA), 1D-CNN achieved success rates of 98%, 98.4%, and 93.9%, while LSTM obtained success rates of 97.3%, 98.4%, and 92.4%. The most suitable model, the 1D-CNN-LSTM, achieved success rates of 99.3%, 98.4%, and 98.4% in differentiating corona and non-corona cases during training, validation, and testing. In the frequency domain analysis (FDA), 1D-CNN achieved success rates of 100%, 95.8%, and 95.8%, while LSTM obtained success rates of 100%, 100%, and 100%. The 1D-CNN-LSTM model achieved a 100%, 100%, and 100% success rate during training, validation, and testing. Hence, the developed algorithms achieved high performance in identifying corona faults in switchgear, particularly the 1D-CNN-LSTM model due to its accuracy in detecting corona faults in both the time and frequency domains.

The authors wish to make the following correction to this paper [...]

The best Weibull distribution methods for the assessment of wind energy potential at different altitudes in desired locations are statistically diagnosed in this study. Seven different methods, namely graphical method (GM), method of moments (MOM), standard deviation method (STDM), maximum likelihood method (MLM), power density method (PDM), modified maximum likelihood method (MMLM) and equivalent energy method (EEM) were used to estimate the Weibull parameters and six statistical tools, namely relative percentage of error, root mean square error (RMSE), mean percentage of error, mean absolute percentage of error, chi-square error and analysis of variance were used to precisely rank the methods. The statistical fittings of the measured and calculated wind speed data are assessed for justifying the performance of the methods. The capacity factor and total energy generated by a small model wind turbine is calculated by numerical integration using Trapezoidal sums and Simpson’s rules. The results show that MOM and MLM are the most efficient methods for determining the value of k and c to fit Weibull distribution curves.

In the present research work, a neural network model has been developed to predict the exhaust emissions and performance of a compression ignition engine. The significance and novelty of the work, with respect to existing literature, is the application of sensitivity analysis and an artificial neural network (ANN) simultaneously in order to predict the engine parameters. The inputs of the model were engine load (0, 25, 50, 75 and 100%), engine speed (1700, 2100, 2500 and 2900 rpm) and the percent of biodiesel fuel derived from waste cooking oil in diesel fuel (B0, B5, B10, B15 and B20). The relationship between the input parameters and engine cylinder performance and emissions can be determined by the network. The global sensitivity analysis results show that all the investigated factors are effective on the created model and cannot be ignored. In addition, it is found that the most emissions decreased while using biodiesel fuel in the compression ignition engine.

Graphene, with its amazing prospects and nonpareil aspects, has enticed scientists and researchers all over the globe in a significant fashion. Graphene, the super material, endlessly demonstrates some of the substantial, as well as desired, mechanical, thermal, optical, and chemical characteristics which are just about to bring about an unprecedented transformation in the science and technology field. Being derived from graphite, graphene is made of one-atom-thick, two-dimensional carbon atoms arranged in a honeycomb lattice. This Nobel-prize-winning phenomenon includes properties that may result in a new dawn of technology. Graphene, the European Union’s (EU) largest pledged project, has been extensively researched since its discovery. Several stable procedures have been developed to produce graphene nanoparticles in laboratories worldwide. Consequently, miscellaneous applications and futuristic approaches in artificial intelligence (AI)-based technology, biomedical and nanomedicine, defence and tactics, desalination, and sports are ruling over the next generation’s fast-paced world and are making the existing market competitive and transformative. This review sheds light upon the ideology of the preparation and versatile application of graphene and foretells the upcoming advancements of graphene nanoparticles with the challenges rearing ahead. The study also considers graphene nanoparticles’ diverse fields and portends their sustainability with the possibility of their acceptance in the commercial market as well as in common usage.

The developments in the field of nano-additives have increased in the recent years due to the desire to reduce the level of exhaust emissions in diesel engines. The soot characteristics of particulate matter (PM) and nitrogen oxides (NOX) were experimentally investigated using two concentrations of titanium dioxide (TiO2) as nano-additives (25 ppm and 40 ppm) blended with C20D (composed of 20% castor oil methyl ester and 80% diesel fuel) and 30% exhaust gas recirculation (EGR). The combustion of C20D + TiO2 increases brake thermal efficiency (BTE) by 2.8% in comparison with neat C20D, while a significant reduction was obtained in BSFC 6.5% and NOX emissions were maintained at a level parallel with diesel. The results indicated that the technique involving a high EGR rate and the addition of 25 ppm and 40 ppm of TiO2 nanoparticles to the C20D exhibits better reductions in NOX emissions by 17.34% and 21.83%, respectively, compared to the technique comprising the use of C20D + TiO2 and C20D. The reduction in the total concentration of PM via the addition of TiO2 nanoparticles to the C20D was 26.74% greater than neat C20D and diesel. In contrast, the incorporation of a high rate of EGR with C20D +TiO2 increased the PM concentrations by 16.85% compared to the technique without EGR. Furthermore, the high concentrations of TiO2 nanoparticles (40 ppm) in the C20D produced 19 nm smaller soot nanoparticles compared to the 23 nm larger soot nanoparticles produced from the low concentrations of TiO2 nanoparticles (25 ppm) added into the C20D. The current investigation reveals that the reduction in NOX emissions and the production of soot nanoparticles notably improved due to the synergic effect of EGR, the TiO2 nanoparticles, and biodiesel.

This paper addresses recycling of waste engine oils treated using acetic acid. A recycling process was developed which eventually led to comparable results with some of the conventional methods. This gives the recycled oil the potential to be reused in cars’ engines after adding the required additives. The advantage of using the acetic acid is that it does not react or only reacts slightly with base oils. The recycling process takes place at room temperature. It has been shown that base oils and oils’ additives are slightly affected by the acetic acid. Upon adding 0.8 vol% of acetic acid to the used oil, two layers were separated, a transparent dark red colored oil and a black dark sludge at the bottom of the container. The base oils resulting from other recycling methods were compared to the results of this paper. The comparison showed that the recycled oil produced by acetic acid treatment is comparable to those recycled by the other conventional methods.

The development of a complex and dynamic system such as the energy sector requires a comprehensive understanding of its constituent components and their interactions, and thus requires approaches that can adapt to the dynamic complexity in systems. Previous efforts mainly used reductionist approaches, which examine the components of the system in isolation, neglecting their interdependent nature. Such approaches reduce our ability to understand the system and/or mitigate undesirable outcomes. We adopt a system dynamics approach to construct an integrated model for analysing the behaviour of the energy sector. Although the Australian energy sector is used as a case study, the model can be applied in other context elsewhere around the world The results indicate that the current trajectory of the Australian energy sector is unsustainable and growth is not being controlled. Limits to growth are fast approaching due to excessive fossil fuel extraction, high emissions and high energy dependency. With the current growth, Australia’s global CO2 emissions footprint will increase to unprecedented levels reaching 12% by 2030 (9.5% for exports and 2.5% for domestic). Oil dependency will account for 43% and 47% of total consumption by 2030 and 2050. By 2032, coal will be the only fossil fuel resource available in Australia. Expansion of investment in coal and gas production is a large risk.