The thermochemical water-splitting method is a promising technology for efficiently converting renewable thermal energy sources into green hydrogen. This technique is primarily based on recirculating an active material, capable of experiencing multiple reduction-oxidation (redox) steps through an integrated cycle to convert water into separate streams of hydrogen and oxygen. The thermochemical cycles are divided into two main categories according to their operating temperatures, namely low-temperature cycles (<1100 °C) and high-temperature cycles (<1100 °C). The copper chlorine cycle offers relatively higher efficiency and lower costs for hydrogen production among the low-temperature processes. In contrast, the zinc oxide and ferrite cycles show great potential for developing large-scale high-temperature cycles. Although, several challenges, such as energy storage capacity, durability, cost-effectiveness, etc., should be addressed before scaling up these technologies into commerc... [more]

Efficiency and emission reduction are the primary targets of internal combustion engine research due the large number of vehicles in operation and the impact of emissions-related pollution on human and ecosystem health. Harmful components of engine exhaust gases include nitrous oxides (NOx), carbon dioxide, hydrocarbons, and particulate matter. NOx emissions in particular are associated with significant health threats. The recirculation of exhaust gases can reduce NOx emissions and improve engine efficiency when combined with other advanced techniques. On the other hand, the residual exhaust gas also effects on the quality of lubricating engine oil and therefore causes an increase in engine piston ring wear. In this review paper, the effects of external and internal exhaust gas recirculation on the performance and emission characteristics of diesel, gasoline, and alternative fuel engines are summarized and discussed in detail. Because it is difficult to estimate the internal residual e... [more]

With the rapid development of wind and photovoltaic power generation, hydro-turbine generator units have to operate in a challenging way, resulting in obvious vibration problems. Because of the significant impact of vibration on safety and economical operation, it is of great significance to study the causal relationship between vibration and other variables. The complexity of the hydro-turbine generator unit makes it difficult to analyze the causality of the mechanism. This paper studied the correlation based on a data-driven method, then transformed the correlation into causality based on the mechanism. In terms of correlation, traditional research only judges whether there is a correlation between all data. When the data with correlation are interfered with by the data without correlation, the traditional methods cannot accurately identify the correlation. A piecewise correlation method based on change point detection was proposed to fill this research gap. The proposed method segme... [more]

Various industrial operations involve frequent heating and cooling of electrical systems. In such circumstances, the development of relevant thermal devices is of extreme importance. During the development of thermal devices, the second law of thermodynamics plays an important role by means of entropy generation. Entropy generation should be reduced significantly for the efficient performance of the devices. The current paper reports an analytical study on micropolar fluid with entropy generation over a stretching surface. The influence of various physical parameters on velocity profile, microrotation profile, and temperature profile is investigated graphically. The impact of thermal radiation, porous medium, magnetic field, and viscous dissipation are also analyzed. Moreover, entropy generation and Bejan number are also illustrated graphically. Furthermore, the governing equations are solved by using HAM and code in MATHEMATICA software. It is concluded from this study that velocity a... [more]

This paper aims to develop an efficient pattern recognition method for engine fault end-to-end detection based on the echo state network (ESN) and multi-verse optimizer (MVO). Bispectrum is employed to transform the one-dimensional time-dependent vibration signal into a two-dimensional matrix with more impact features. A sparse input weight-generating algorithm is designed for the ESN. Furthermore, a deep ESN model is built by fusing fixed convolution kernels and an autoencoder (AE). A novel traveling distance rate (TDR) and collapse mechanism are studied to optimize the local search of the MVO and speed it up. The improved MVO is employed to optimize the hyper-parameters of the deep ESN for the two-dimensional matrix recognition. The experiment result shows that the proposed method can obtain a recognition rate of 93.10% in complex engine faults. Compared with traditional deep belief networks (DBNs), convolutional neural networks (CNNs), the long short-term memory (LSTM) network, and... [more]

The traction drive system of a high-speed train has a vital role in the safe and efficient operation of the train. This paper established an electromechanical coupling model of a high-speed train. The model considers the interaction of the gear pair, the equivalent connecting device of the transmission system, the equivalent circuit of the traction motor, and the direct torque control strategy. Moreover, the numerical simulation of the high-speed train model includes constant speed, traction, and braking conditions. The results indicate that the meshing frequency and the high harmonics rotation frequency constitute the stator current. Furthermore, both frequencies are evident during constant speed. However, they are blurry among other conditions except for twice the rotation frequency. Meanwhile, the rotor and stator currents’ root-mean-square (RMS) values during traction are less than the RMS value during braking. The initiation of traction and braking causes a significant increase in... [more]

In recent times, wireless energy transfer has become an effective solution to charge devices due to its efficiency and reliability. In a typical Wireless Rechargeable Sensor Networks (WRSN), wireless energy transfer technique can solve the energy depletion problem with the aid of a Wireless Charging Vehicle (WCV), thereby enabling the network to extend its lifetime. However, sensor nodes in a WRSN still have their energies depleted before it gets replenished by the WCV. In this paper, we proposed a scheme that prioritizes sensor nodes for charging and also developed efficient algorithms to improve on existing charging schemes so as to extend the lifetime of the WRSN. Firstly, an inspection algorithm was developed to visit and inspect sensor nodes in the network so as to determine the sensor nodes to charge. Secondly, a greedy charge algorithm was introduced to ascertain the shortest distance the WCV needs to travel and, lastly, an energy for nodes’ algorithm was proposed to determine t... [more]

Universal access to energy is a global challenge for sustainable development that requires granting last-mile access to energy services to rural and isolated communities. However, achieving access is not sufficient: it must be done affordably, reliably and with an adequate quality. Universal access to energy goes beyond the mere selection of a technical solution or infrastructure; it demands being able to design management models for projects aiming to guarantee that households may access energy services in a sustainable way. This study analyzes the main elements (i.e., governance, technological and business models) of management models in universal access to energy projects and their impact on the different dimensions of sustainability (i.e., social, environmental, and economic). The study then presents three case studies of rural electrification projects having different configurations of the management model, with special focus on the differences in the business model, and it analyz... [more]

There is no denying fact that nanoparticles of carbon nanotubes are employed to improve the performance of thermal stability in comparison with traditional nanoparticles, this is because nanoparticles of carbon nanotubes possess outstanding material properties. In this manuscript, a mathematical model of mixed convective flow based on carbon nanotubes suspended in ethylene glycol has been developed and derived by means of Fourier Sine transform. In order to analyze the thermophysical properties of nanofluid, the temperature and velocity profiles have been investigated through fractional derivative and integral transforms. The comparative analysis of single and multi-walled carbon nanotubes has been presented for the sake of enhancement of heat transfer. It is worth mentioning that embedded rheological parameters have shown the sensitivity for the enhancement of heat transfer with and without fractional techniques through graphical illustration.

Continually using fossil fuels as the main source for producing electricity is one of the main factors causing global warming. Through the past years, several efforts have been made, looking for sustainable, environmentally friendly, and clean energy alternatives. Harvesting geothermal energy from roadway pavement is one of the alternatives that have been developed and investigated recently. Herein, a systematic review and bibliometric analysis were conducted to provide a comprehensive overview of the potentials of harvesting thermal energy from asphalt pavement and to assess the level of achievement being attained towards developed technologies. A total of 713 articles were initially collected, considering the period between 2006 and 2021; later, a series of filtration processes were performed to reach 47 publications. The thermal energy harvesting technologies were categorized into three main sectors, at which their basics and principles were discussed. In addition, a detailed descri... [more]

Waterflooding is a widely used secondary oil recovery technique. The oil and gas industry uses a complex reservoir numerical simulation and reservoir engineering analysis to forecast production curves from waterflooding projects. The application of such standard methods at the stage of assessing the potential of a huge number of projects could be computationally inefficient and requires a lot of effort. This paper demonstrates the applicability of machine learning to rate the outcome of waterflooding applied to an oil reservoir. We also explore the relationship of project evaluations by operators at the final stages with several performance metrics for forecasting. Real data about several thousand waterflooding projects in Texas are used in the current study. We compare the ML models rankings of the waterflooding efficiency and the expert rankings. Linear regression models along with neural networks and gradient boosting on decision threes are considered. We show that machine learning... [more]

This paper presents the results of a field study undertaken all over the Punjab, Pakistan, to evaluate the socio-economic and climatic impact of photovoltaic-operated high-efficiency irrigation systems (HEIS), i.e., drip and sprinkler irrigation systems. Nearly half of the rural population relies on agriculture for a living, and the recent energy crisis has had a negative impact on rural communities. Farmers’ reliance on fossil fuels for the operation of irrigation systems has increased exponentially, resulting in the high costs of agricultural production. Primary data regarding on-farm agriculture and irrigation practices used in this study were collected through an intensive on-farm survey, while secondary data were taken from published reports and statistics. The results of the current investigation show that the installation of PV systems has resulted in the increased adoption of high-efficiency irrigation systems, a reduction in the high operational costs incurred on account of ol... [more]

When an induction motor is running at stable speed and low slip, the fault signal of the induction motor’s broken bar faults are easily submerged by the power frequency (50 Hz) signal. Thus, it is difficult to extract fault characteristics. The left-side harmonic component representing the fault characteristics can be distinguished from power frequency owing to V-shaped trajectory of the fault component in time-frequency (t-f) domain during motor startup. This article proposed a scheme to detect broken bar faults and discriminate the severity of faults under starting conditions. In this scheme, successive variable mode decomposition (SVMD) is applied to analyze the stator starting current to extract the fault component, and the signal reconstruction is proposed to maximize the energy of the fault component. Then, the quadratic regression curve method of instantaneous frequency square value of the fault component is utilized to discriminate whether the fault occurs. In addition, accordi... [more]

Greenhouse gas (GHG) emissions from agriculture contribute to climate change. The consequences of unsustainable agricultural activity are polluted water, soil, air, and food. The agricultural sector has become one of the major contributors to global GHG emissions and is the world’s second largest emitter after the energy sector, which includes emissions from power generation and transport. Latvian and Lithuanian agriculture generates about one fifth of GHG emissions, while Estonia generates only about one tenth of the country’s GHG emissions. This paper investigates the GHG trends in agriculture from 1995 to 2019 and the driving forces of changes in GHG emissions from the agricultural sectors in the Baltic States (Lithuania, Latvia, and Estonia), which are helpful for formulating effective carbon reduction policies and strategies. The impact factors have on GHG emissions was analysed by using the Logarithmic Mean Divisia Index (LMDI) method based on Kaya identity. The aim of this study... [more]

Controlling the cost of electricity consumption remains a major concern, particularly in the residential sector. Smart home electricity management systems (HEMS) are becoming increasingly popular for providing uninterrupted power and improved power quality, as well as for reducing the cost of electricity consumption. When power transfer is required between a storage system and the AC grid, and vice versa, these HEMS require the use of a bidirectional DC−AC converter. This paper emphasizes the potential value of an almost unexplored topology, the design of which was based on the generation of sinusoidal signals from sinusoidal half waves. A DC−DC stage, which behaved as a configurable voltage source, was in series with a DC−AC stage, i.e., an H-bridge, to achieve an architecture that could operate in both grid and off-grid configurations. Wide bandgap power switches (silicon carbide metal-oxide-semiconductor field-effect transistors [MOSFETs]), combined with appropriate control strategi... [more]

High voltage direct current (HVDC) technology has begun to gather a high degree of interest in the last few decades, showing a fast evolution of achievable voltage levels, transfer capacities, and transmission lengths. All these changes occurred in a context in which power system applications are highly dependent on HVDC technologies such as energy generation from renewable sources (e.g., energy generated in offshore wind power plants), power exchanges between asynchronous networks, submarine cables, and long-length transmission overhead lines have become more common worldwide. This paper tries to summarize the current state of HVDC technologies, both voltage-source converters and current-source converters, the main components of converter substations, control strategies, key challenges arising from their use, as well as the future prospects and trends of HVDC applications. This paper represents the first step in setting the background information for analyzing the impact of a VSC-HVDC... [more]

We report results concerning the functionalization of graphene-based nanoplatelets for improving the thermal energy storage capacity of commonly used phase change materials (PCMs). The goal of this study was to enhance the low thermal conductivity of the PCMs, while preserving their specific and latent heats. We focused on wax-based PCMs, and we tested several types of graphene nanoparticles (GNPs) at a set of different concentrations. Both the size and shape of the GNPs were found to be important factors affecting the PCM’s thermal properties. These were evaluated using differential scanning calorimetry measurements and a modified enthalpy-based water bath method. We found that a small addition of GNPs (1% weight) with high aspect ratio is sufficient to double the thermal conductivity of several widely used PCMs. Our results suggest a simple and efficient procedure for improving the thermal properties of PCMs used in thermal energy storage applications.

Ti3C2Tx-based aerogels have attracted widespread attention for three-dimensional porous structures, which are promising to realize high-rate energy storage. However, disordered Ti3C2Tx aerogels with highly tortuous porosity fabricated by conventional unidirectional freeze-casting substantially increase ion diffusion lengths and hinder electrolyte ions transport. Herein we demonstrate a new bidirectional ice-templated approach to synthesize porous ordered Ti3C2Tx aerogel with straight and aligned channels, straight and short ion diffusion pathways, leading to better ion accessibility. The aligned Ti3C2Tx aerogel exhibits the high specific capacitance of 345 F g−1 at 20 mV s−1 and rate capability of 52.2% from 10 to 5000 mV s−1. The specific capacitance is insensitive of mass loadings even at 10 mg cm−2 and an excellent power density of 137.3 mW cm−2 is obtained in symmetric supercapacitors. The electrochemical properties of Ti3C2Tx aerogel supercapacitors at sub-zero (to −30 °C) tempera... [more]

When a meteorological disaster occurs and equipment becomes damaged, a significant amount of time is required to repair the damaged components as it is impossible to repair several components simultaneously. Therefore, the determination of repair priority is a significant aspect of a distribution system’s resilience. This study proposes a technique to identify the unserved areas of a radial distribution system based on the bus injection to the branch current (BIBC) matrix, as opposed to a complex optimization technique, for evaluating the repair priority determination strategy for all the possible disaster scenarios. Generally, most resilience metrics include the concept of duration; therefore, the strategy for resilience enhancement must optimize the recovery priority using an objective function that consists of the recovered capacity increment, rather than the recovered capacity. To verify the proposed method, in this paper, the resilience is evaluated under all the disaster scenario... [more]

The paper introduces a novel structure of a dual-band filtering Wilkinson power divider (WPD). Its essential component is a dual-band bandpass filter (BPF), implemented using coupling lines and two T-shaped resonators. The BPF is incorporated into the divider structure to suppress the unwanted harmonics within the circuit. The latter is achieved owing to a wide stopband of the filter. The designed dual-band WPD can suppress third unwanted harmonics in both channels with high levels of attenuation. The designed dual-band WPD operates at 2.6 GHz and 3.3 GHz with a return loss of 22.1 dB and 22.3 dB at the operating frequencies. Furthermore, the insertion loss and isolation are 0.3 dB and 20.2 dB at 2.6 GHz and 0.9 dB and 24.5 dB at 3.3 GHz. The analysis and simulation results are corroborated by the measurements of the fabricated divider prototype. The competitive performance of the proposed circuit is also demonstrated through comparisons with state-of-the-art divider circuits from the... [more]

The aim of this study was to compare and evaluate the production of exhaust emissions from a vehicle with a petrol engine with the Euro 4 emission standard and powered by petrol and LPG (liquefied petroleum gas). The paper presents new possibilities for monitoring exhaust emissions using an exhaust gas analyzer. At the same time, it points out the topicality and significance of the issue in the monitored area. It examines the impact of a change in fuel on emissions. This change is monitored in various areas of vehicle operation. Measurements were performed during real operation, which means that the results are fully usable and applicable in practice. The driving simulation as well as the test conditions correspond to the RDE (Real Driving Emissions) test standard. A commercially available car was first selected to perform the tests, which was first measured in the original configuration (petrol drive). Based on real-time RDE driving tests, it is possible to determine the number of exh... [more]

Water inrush disasters from the coal seam floor occur frequently due to the high water pressure of the Ordovician limestone aquifer, multiple aquifers and strong mining disturbance. We presented a model of water-resisting key strata (WRKS) to investigate the mechanism of floor water inrush from multiple aquifers in deep coal mines. Roof hydraulic fracturing (RHF) for controlling floor water inrush and multi-parameter monitoring were proposed and validated in the Xingdong coal mine in Xingtai, Hebei Province. The results indicated that the periodic weighting step of the test working face after RHF was 9.53 m, which was 61.42% less than that of the working face without RHF (24.7 m). The floor failure depth was 30 m, which was 34.4% less than that of the zones without RHF (45.7 m). Hydraulic fracturing weakened the strength of the overlying strata to control the weighting step and reduce the mining disturbance stress, and the stability of the floor WRKS was enhanced, thereby preventing wa... [more]

Deployment of Electric Vehicles (EV) is increasing in recent years due to economic and environmental advantages compared with fossil fuel-based vehicles. As the market of EVs grows, new challenges to the electric grid are emerging to accommodate the EVs demand, especially in the distribution networks. In this paper, we investigate the impact of EVs deployment on the electricity demand and distributed network. We propose a model to generate EV demand profiles that consider the EV users’ driving pattern such as daily energy consumption and charging schedule, in addition to the EV’s charging characteristics. The EV demand model uses data we obtained from a survey to evaluate the model’s parameters. We use the EV demand model to simulate and evaluate the impact of EVs demand on the distribution network. We present a case study with an actual model for a distribution network to evaluate the impact of EVs on the distribution network in Saudi Arabia. We analyze the simulation results and show... [more]

The hot exhaust gas generated by a downhole combustion heater directly heats the formation, which can avoid the heat loss caused by the injection of high-temperature fluid on the ground. However, if the temperature of the exhaust gas is too high, it may lead to the carbonization of organic matter in the formation, which is not conducive to oil production. This paper proposes the use of low-temperature catalytic combustion of a mixture of methane and air to produce a suitable exhaust gas temperature. The simulation studies the influence of different parameters on the catalytic combustion characteristics of methane and the influence of downhole high-pressure conditions. The results show that under high-pressure conditions, using a smaller concentration of methane (4%) for catalytic combustion can obtain a higher conversion efficiency (88.75%), and the exhaust temperature is 1097 K. It is found that the high-pressure conditions in the well can promote the catalytic combustion process of t... [more]

Intelligent and pragmatic state-of-health (SOH) estimation is critical for the safe and reliable operation of Li-ion batteries, which recently have become ubiquitous for applications such as electrified vehicles, smart grids, smartphones, as well as manned and unmanned aerial vehicles. This paper introduces a convolutional neural network (CNN)-based framework for directly estimating SOH from voltage, current, and temperature measured while the battery is charging. The CNN is trained with data from as many as 28 cells, which were aged at two temperatures using randomized usage profiles. CNNs with between 1 and 6 layers and between 32 and 256 neurons were investigated, and the training data was augmented with noise and error as well to improve accuracy. Importantly, the algorithm was validated for partial charges, as would be common for many applications. Full charges starting between 0 and 95% SOC as well as for multiple ranges ending at less than 100% SOC were tested. The proposed CNN... [more]