Energies

Silicon is one of the most used semiconductor materials around the world. This research was conducted on silicon to improve its opto-electrical properties including bandgap and optical conductivity using direct ultrafast pulsed nanostructure formation (DUPNF). The power and frequency of high-intensity picosecond laser pulses were varied, and nanostructures were formed. The optical properties were examined using optical spectroscopy in both VIS and NIR. The structural characteristics were examined using EDX and SEM imaging. It was found that varying the power and frequency of the laser processing caused a change in the bandgap, as well as the structural characteristics of the silicon semiconductor. Increasing the frequency and power showed the formation of nanostructures of silicon and their variation in the properties of the formed nanostructures. The critical breakdown field of the silicon nanostructures was higher than the bulk silicon. Full article

In order to decarbonize the rail industry, the development of innovative locomotives with the ability to use multiple energy sources, constituting hybrid powertrains, plays a central role in transitioning from conventional diesel trains. In this paper, four configurations based on suitable combinations of fuel cells and/or batteries are designed to replace or supplement a diesel/overhead line powertrain on a real passenger train (the Hitachi Blues) tested on an existing regional track, the Catanzaro Lido–Reggio Calabria line (Italy), managed by Trenitalia SpA. (Italy). The configurations (namely battery–electrified line, full-battery, fuel cell–battery–electrified line, and fuel cell–battery) are first sized with the intention of completing a round trip, then integrated on board with diesel engine replacement in mind, and finally occupy a portion of the passenger area within two locomotives. The achieved performance is thoroughly examined in terms of fuel cell efficiency (greater than 47%), hydrogen consumption (less than 72 kg), braking energy recovery (approximately 300 kWh), and battery interval SOC. Full article

Pulling chains are one of the most critical components of a plough longwall system. Their failure is hard to predict and results in costly and lengthy downtimes. Measurements of the actual forces acting on a plough chain are needed as they can provide important information about the nature of their operation and contribute to preventing their wear and failure by supplementing data gathered by a plough control station. In this work, a strain gauge system for the measurement of the forces in a plough chain is designed, manufactured and tested under real conditions for the first time. Constructed using a cylindrical strain gauge and widely available electronic components, the finished sensor was calibrated on a testing machine and used to measure forces in a 42 × 137 plough chain at LW Bogdanka. Measurements conducted under real conditions show that forces in the chain during steady ploughing does not exceed 400 kN. However, the forces peaked to up to 728.6 kN at certain points in the operation. The implications of such events are discussed. The device allowed for successful measurements of forces in difficult conditions of a mining plough longwall and provides new information on their course and characteristics, which is very valuable for plough operators and chain-life predictions. This work suggests the further integration of force sensors into longwall plough system. Full article

Now that many countries have set goals for reaching net zero emissions by the middle of the century, it is important to clarify the role of each country in achieving the 1.5 °C target of the Paris Agreement. Here, we evaluated China’s role by calculating the global temperature impacts caused by China’s emission pathways available in global emissions scenarios toward the 1.5 °C target. Our results show that China’s contribution to global warming in 2050 (since 2005) is 0.17 °C on average, with a range of 0.1 °C to 0.22 °C. The peak contributions of China vary from 0.1 °C to 0.23 °C, with the years reached distributing between 2036 and 2065. The large difference in peak temperatures arises from the differences in emission pathways of carbon dioxide (CO₂), methane (CH₄), and sulfur dioxide (SO₂). We further analyzed the effect of the different mix of CO₂ and CH₄ mitigation trajectories in China’s pathways on the global mean temperature. We found that China’s near-term CH₄ mitigation reduces the peak temperature in the middle of the century, whereas it plays a less important role in determining the end-of-the-century temperature. Early CH₄ mitigation action in China is an effective way to shave the peak temperature, further contributing to reducing the temperature overshoot along the way toward the 1.5 °C target. This underscores the necessity for early CO₂ mitigation to ultimately achieve the long-term temperature goal. Full article

In the field of permanent magnet synchronous motor (PMSM) control, the sliding mode observer (SMO)-based sensorless control is widely used; however, the actual control input of the current observation function is asymmetric. It can lead to different velocities of the estimated currents approaching to the actual currents and will make the current and back EMF fluctuations more severe, and result in more skewed angle and speed estimates, especially at a lower carrier ratio. In response to the above problems, this paper proposes a variable weighting coefficient of an EMF-based sliding mode observer (VWC-SMO). Unlike the traditional sliding mode observers, the weighted sliding mode switching variables and their bandpass-filtered values are used as the input of the current observer in the VWC-SMO. Thereby, the asymmetry of the control input in the current observation function can be well-suppressed, and almost the same approaching velocity on the two sides of the sliding surface can be obtained. Therefore, chattering near the sliding surface can also be suppressed. The method is verified on a motor controller experimental platform, and the comparative results shows that the VWC-SMO can reduce chattering of the observed currents and mitigate back EMFs fluctuations and improve the dynamic and steady-state performance. Full article

Condition-based monitoring (CBM) has emerged as a critical instrument for lowering the cost of unplanned operations while also improving the efficacy, execution, and dependability of tools. Thermal abnormalities can be thoroughly examined using thermography for condition monitoring. Thanks to the advent of high-resolution infrared cameras, researchers are paying more attention to thermography as a non-contact approach for monitoring the temperature rise of objects and as a technique in great experiments to analyze processes thermally. It also allows for the early identification of weaknesses and failures in equipment while it is in use, decreasing system downtime, catastrophic failure, and maintenance expenses. In many applications, the usage of IRT as a condition monitoring approach has steadily increased during the previous three decades. Infrared cameras are steadily finding use in research and development, in addition to their routine use in condition monitoring and preventative maintenance. This study focuses on infrared crucial thermographic theoretical stages, experimental methodologies, relative and absolute temperature requirements, and infrared essential thermographic theoretical processes for electrical and electronics energy applications. Furthermore, this article addresses the major concerns and obstacles and makes some specific recommendations for future development. With developments in artificial intelligence, particularly computer fiction, depending on the present deep learning algorithm, IRT can boost CBM analysis. Full article

Due to the global warming and resulting problems, attention has been paid to greenhouse gases released into the atmosphere since the 1980s and 1990s. For this reason, the Montreal Protocol and the Kyoto Protocol have tightened regulations on the use of gaseous refrigerants in both HVAC systems and industrial refrigeration. Gradually, new generations of gaseous refrigerants, that theoretically have much less negative environmental impact than their predecessors, are introduced into the market. The key parameter describing environmental impact is the GWP index, which is most often defined on a time horizon of 100 years. The long-term use of new generations of gaseous refrigerants in HVAC systems reduces CO₂ emissions into the atmosphere; however, given that new generation gases often have a short lifetime, it seems that the adopted assessment may not be applicable. The aim of the article was to show how emissions of CO₂ equivalent to the atmosphere differs in the short and long time horizon. The article presents the results of calculations of equivalent CO₂ emissions to the atmosphere caused by the operation of compressor cooling devices used in HVAC systems, where cooling is done with the use of water or a water-glycol solution. The analysis was carried out for 28 commonly used devices on the world market. The analyzed devices work with refrigerants: R513A, R454B, R290, R1234ze, R32, R134a, R410A. The equivalent emissions values for GWP 100 and GWP 20 were analyzed in relation to the unit power of the devices depends on refrigerant mass and number of fans. The study showed that in the case of new generation refrigerants with a very short lifetime, the use of GWP 100 indicators is misleading and does not fully reflect the effects of environmental impact, especially in the area of refrigeration equipment application. The article shows that the unit value of the cooling load related to the number of fans or the unit would be helpful in assessing the environmental impact of a cooling device. Full article

The Daniudi gas field is located in the Ordos Basin’s northern section of the Yishan slope. The intertidal–subtidal depositional environment dominates the lithology of the Ma55 sub-member, resulting in a stable, thick-layered dark gray–gray–black limestone and lime dolomite. The stratum is stable laterally as well as dolomite, with an average thickness of 26.8 m. Fractures, dissolution expansion pores, and inter-crystalline dissolved pores are the primary reservoir space kinds, with a minor number of karst caves and fractures generated as well. The main distribution ranges for porosity and permeability are 1–8 percent and 0.01–1 mD, respectively. Low porosity, tightness, and ultra-low permeability are common characteristics, and a single well typically has no natural productivity. Production stimulation technologies like pre-fluid acid fracturing, compound sand addition acid fracturing, and multi-stage injection + temporary plugging volumetric acid fracturing have been gradually optimized using the horizontal well development method, and breakthroughs in the development of tight and low-permeability carbonate rock reservoirs have been made. However, the conditions of different types of reservoirs are quite different, and the acid fracturing process is not matched and imperfect, resulting in large differences in the productivity of different horizontal wells after fracturing, as well as a high proportion of low-yield wells, which cannot meet the needs of cost-effective and effective development of this type of gas reservoir. In light of the aforementioned issues, a series of laboratory tests have been carried out to explore the stimulation effects of acid fracturing on different types of reservoirs and to optimize the acid fracturing process in the Daniudi gas field. The results show that the rock mechanical performances and the acid etching conductivities of the rock specimens are related to the types of reservoirs. The rock mechanical properties can be deteriorated after acidizing, but different types of reservoirs have different degrees of deterioration. According to the results of acid etching conductivity of different types of reservoirs, conductivities obtained by high and low viscosity and cross-link-gelled acid (two stage injection) processes are higher than those of high viscosity systems. The experimental results of process suitability suggest adopting high and low viscidity acid systems for pore type and fracture-dissolved pore type reservoirs, and cross-linked acid systems for fracture-pore type reservoirs. The findings of this study can help form a better understanding of the performance of different types of reservoirs under the various acidified conditions that can be used for the optimization of acid fracturing processes in carbonate formations. Full article

In a developing country such as Malaysia, studies of determinants which influence residential consumers of the Battery Energy Storage System (BESS) are limited. This paucity of studies was the catalyst for this study and its aim to investigate the factors affecting acceptance by Malaysian residential consumers of BESS as it relates to the Technology Acceptance Model Theory. A sample of 331 residential consumers indicated that consumer attitudes, social norms and self-efficacy, or the perception of behavioral control, had a positive and significant relationship with the intention to use BESS. Additionally, trust was a factor that had a significant effect on the consumers’ perceptions of cost, benefits and anticipated effects. All these variables significantly affect consumer attitudes. These findings provide important insights into BESS and facilitate the development of policies and practices relating to BESS in developing countries such as Malaysia. Full article

Energy transition in islands constitutes a major challenge. Apart from a necessity, it can also be a great opportunity for sustainable social and economic development. Toward this direction, a new, promising movement has emerged recently in Greek islands. Straight from the roots of the insular population, development of energy communities comes as the result of increased awareness of local people, raised also by the legacy of lighthouse projects and initiatives. Kythnos, Ikaria, Sifnos, Tilos, Agios Efstratios, Crete, and Chalki, are all islands that have embraced the implementation of successful, local-scale innovation projects and/or initiatives, generating meaningful results across different energy aspects and contributing to positive social change. Our study provides an overview of the broader energy transition aspects in Greek islands, discusses the impact of the aforementioned exemplary cases, and further elaborates on the model of energy communities. According to our analysis, leveraging on the experience of lighthouse projects and initiatives, and on the dynamics of the emerging energy community movement, could lead to increased social and economic benefits for the insular populations, to broad public acceptance, and to minimum environmental impacts for the islands’ natural ecosystems. Full article

Renewable and clean energy sources are being integrated into the United States’ modern energy industry to mitigate climate change effects, creating a more complex network of energy production, distribution, and consumption. This study defines the state of Maryland’s energy industry as a network of producers and consumers and analyzes the network’s characteristics by using ecological network analysis (ENA), an analytical tool useful for identifying a system’s indirect effects. The energy industry within Maryland is analyzed over a nine-year time span to understand how its evolution is influencing the network’s characteristics. Maryland’s renewable portfolio standard (RPS) for the year 2030 is then simulated by adjusting renewable and non-renewable energy sources according to energy trends and related state policy. Results from the ENA over the nine-year period of 2010–2019 indicate that the energy industry is highly linear. While typical cycling indices range from 5–15% in ecological energy flow models, cycling indices in this study ranged from 0.007% to 0.0082%. Maryland’s energy industry in the year 2030 is simulated and displays increased cycling because renewable sources typically feed the electricity sector for energy distribution, increasing indirect pathways within the system. The percentage of electricity generated by renewable energy increased from 9.71% in 2019 to 50% in 2030, as mandated in the RPS. Network analyses here emphasize the large gap between Maryland’s current energy infrastructure and what is necessary to meet its renewable targets in 2030. Furthermore, they indicate that a more uniform distribution of energy to consumers may increase efficiency in modern energy industries. Full article

In this paper, renewable resources, namely photovoltaic panels (PV), are placed in a specific configuration to obtain the maximum reliability and availability of a microgrid and study the subcomponent-level reliability and availability. The reliability of components can be increased by trying different configurations of the components. We identify the preferred configuration used for the PV panels as bridged linked. The overall reliability of the microgrid is increased when component-wise reliability is considered. Even components are further divided into subcomponents, and the multiple faults of each component are considered. The method used for the reliability evaluation and availability study is Markov state transition modeling. The microgrid’s reliability and availability are plotted concerning time using Matlab. The optimization of reliability and availability is conducted through optimization techniques such as the genetic algorithm (GA) and artificial neural networks (ANN). The results are compared and validated for the optimal values of mean time to failure (MTTF) and mean time to repair (MTTR). Using a genetic algorithm, there is a 96% of improvement in the reliability, and after applying the neural networks, a significant improvement of 97% along with quick results is achieved. Full article

Occupant behavior based on natural ventilation has a significant impact on building energy consumption. It is important for the quantification of occupant-behavior models to select observed variables, i.e., features that affect the state of window opening and closing, and to consider machine learning models that are effective in predicting this state. In this study, thermal comfort was investigated, and machine learning data were analyzed for 30 houses in Gifu, Japan. Among the selected machine learning models, the logistic regression and deep neural network models produced consistently excellent results. The accuracy of the prediction of open and closed windows differed among the models, and the factors influencing the window-opening behaviors of the occupants differed from those influencing their window-closing behavior. In the selection of features, the analysis using thermal indices representative of the room and cooling features showed excellent results, indicating that cooling features, which have conflicting relationships with natural ventilation, are useful for improving the accuracy of occupant-behavior prediction. The present study indicates that building designers should incorporate occupant behavior based on natural ventilation into their designs. Full article

Applications of the Internet of Things (IoT) are rapidly utilized in smart buildings and smart cities to reduce energy consumption. This advancement has caused a knowledge gap in applying IoT effectively by experts in the built environment to achieve energy efficiency. The study aims to provide an extensive review of IoT applications for energy savings in buildings and cities. This study contributes to the field of IoT by guiding and supporting built environment experts to utilize IoT technologies. This paper performed a thorough study using a systematic review that covered an overview of IoT concepts, models, applications, trends and challenges that can be encountered in the built environment. The findings indicated limitations in developing IoT strategies in buildings and cities by professionals in this field due to insufficient comprehension of technologies and their applied methods. Additionally, the study found an indefinite implementation and constraints on using IoT when integrated into the built environment. Finally, the study provides critical arguments and the next steps to effectively utilize IoT in terms of energy efficiency. Full article

Membrane distillation (MD) is a thermal-based membrane operation with high potential for the treatment of aqueous streams. However, its implementation is limited and only few examples of MD pilots can be found in desalination. One of the reasons behind this is that MD requires thermal energy for promoting the evaporation of water, which implies higher energy consumption with respect to pressure-driven membrane operations, like reverse osmosis (RO). Recently, among the different methods investigated to improve the thermal efficiency of MD, attempts for obtaining a localized heating of the feed, close to the membrane surface, were carried out. This work reviews experimental activities on the topic, dealing with both modified membranes, used under solar irradiation or coupled to an electric source, and specifically designed heated modules. The main results are reported and points of action for further optimization are identified. In particular, although at an early stage, this type of approach led to improvements in membrane flux and to a reduction of energy consumption with respect to conventional MD. Nevertheless, long tests to ensure a stable performance time, the optimization of operating conditions, the development of methods to control fouling issues, and the identification of the best module design, together with the scale-up of membranes/modules developed, represent the main research efforts needed for future implementation of localized heating strategy. Full article

This paper presents a new metaheuristic approach based on a self-adaptive genetic algorithm (SAGA) for solving the short-term hydro-thermal-solar scheduling with combined-cycle (CCGT) units. First of all, the proposed approach is applied to a test system with different characteristics, considering the valve-point effect. The simulation results obtained from the new SAGA are compared with the results obtained from some other metaheuristic methods, such as AIS, DE, and EP to reveal the validity and verify the feasibility of the proposed approach. The test results show that the proposed metaheuristic approach proves the effectiveness and superiority of the SAGA algorithm for solving the short-term hydro-thermal-solar scheduling (SHTSS) problem. Full article

The technical focus of drilling operations is changing to oil and gas reservoirs with higher difficulty factors such as low permeability and fracture. During the drilling process, drilling operations in deep complex formations are prone to overflow and leakage complications. Leakage and overflow problems will change the performance of the drilling fluid in the wellbore, impacting the wellbore pressure, and causing complex accidents such as stuck drilling and collapse. In order to improve the level of control over the risk of wellbore overflow and leakage, it is necessary to predict the mud overflow and leakage situation and to arrange and control the risk of leakage and overflow that may occur in advance to ensure the safety of drilling. By using a genetic algorithm to optimize the multi-layer feedforward neural network, this paper establishes a GA-BP Neural Network Drilling overflow and leakage prediction model based on multi-parameter fusion. Through the optimization training of 14 parameters that may affect the occurrence of complex downhole accidents, the mud overflow and leakage are predicted. The prediction results of the model are compared with the prediction results of a conventional BP neural network, and verified by the real drilling data. The results show that the MAE, MSE, and RMSE of the GA-BP neural network model are improved by 2.91%, 4.48%, and 10.93%, respectively, compared with the BP neural network model, and the prediction quality is higher. Moreover, the amount of mud overflow and leakage predicted by using the GA-BP neural network matches well with the pattern of mud overflow and leakage data in real drilling, which proves the effectiveness and accuracy of the GA-BP neural network in overflow and leakage prediction. Full article

The use of electric vehicles and their adoption have become imperative due to the policies and goals to reduce harmful emissions emitted by fossil fuel vehicles. The adoption and acceptance of electric vehicles by the public may be determined by several factors, such as the price of the vehicles, the environmental and political costs of vehicle production, infrastructure availability, etc. However, given the diverse context of each country, different factors may have a different impact on customer attitudes. According to that fact, the purpose of this research is to identify which factors influence attitudes towards the purchase of electric vehicles in the Republic of Croatia. In order to identify those factors, this paper is based on a survey in which 578 randomly selected respondents took part. The research was conducted between 1 October 2021 and 1 May 2022. The obtained research results were analyzed using linear regression, i.e., moderation analysis. The results indicated that the most significant factors influencing attitudes towards the purchase of electric cars are difficulties in servicing, the cost of battery replacement, lack of charging infrastructure, and the reduced impact on environmental pollution. The basic conclusion of this research is that it is necessary to invest more in the development of infrastructure that supports the use of electric vehicles and in the development of electric vehicles themselves and their characteristics. Full article