bluuebunny/crossref_metadata_2025 huggingface dataset:
Fast and High-Quality Monocular Depth Estimation with Optical Flow for Autonomous Drones
Authors: Shimada Tomoyasu , Nishikawa Hiroki , Kong Xiangbo , Tomiyama Hiroyuki
Published: February 2023

Recent years, autonomous drones have attracted attention in many fields due to their convenience. Autonomous drones require precise depth information so as to avoid collision to fly fast and both of RGB image and LiDAR point cloud are often employed in applications based on Convolutional Neural Networks (CNNs) to estimate the distance to obstacles. Such applications are implemented onboard embedded systems. In order to precisely estimate the depth, such CNN models are in general so complex to extract many features that the computational complexity increases, requiring long inference time. In order to solve the issue, we employ optical flow to aid in-depth estimation. In addition, we propose a new attention structure that makes maximum use of optical flow without complicating the network. Furthermore, we achieve improved performance without modifying the depth estimator by adding a perceptual discriminator in training. The proposed model is evaluated through accuracy, error, and inference time on the KITTI dataset. In the experiments, we have demonstrated the proposed method achieves better performance by up to 34% accuracy, 55% error reduction and 66% faster inference time on Jetson nano compared to previous methods. The proposed method is also evaluated through a collision avoidance in simulated drone flight and achieves the lowest collision rate of all estimation methods. These experimental results show the potential of proposed method to be used in real-world autonomous drone flight applications.
Configurations and Applications of Multi-Agent Hybrid Drone/Unmanned Ground Vehicle for Underground Environments: A Review
Authors: Dinelli Chris , Racette John , Escarcega Mario , Lotero Simon , Gordon Jeffrey , Montoya James , Dunaway Chase , Androulakis Vasileios , Khaniani Hassan , Shao Sihua , Roghanchi Pedram , Hassanalian Mostafa
Published: February 2023

Subterranean openings, including mines, present a unique and challenging environment for robots and autonomous exploration systems. Autonomous robots that are created today will be deployed in harsh and unexplored landscapes that humanity is increasingly encountering in its scientific and technological endeavors. Terrestrial and extraterrestrial environments pose significant challenges for both humans and robots: they are inhospitable and inaccessible to humans due to a lack of space or oxygen, poor or no illumination, unpredictable terrain, a GPS-denied environment, and a lack of satellite imagery or mapping information of any type. Underground mines provide a good physical simulation for these types of environments, and thus, can be useful for testing and developing highly sought-after autonomous navigation frameworks for autonomous agents. This review presents a collective study of robotic systems, both of individual and hybrid types, intended for deployment in such environments. The prevalent configurations, practices for their construction and the hardware equipment of existing multi-agent hybrid robotic systems will be discussed. It aims to provide a supplementary tool for defining the state of the art of coupled Unmanned Ground Vehicle (UGV)–Unmanned Aerial Vehicle (UAV) systems implemented for underground exploration and navigation purposes, as well as to provide some suggestions for multi-agent robotic system solutions, and ultimately, to support the development of a semi-autonomous hybrid UGV–UAV system to assist with mine emergency responses.
A Distributed Collaborative Allocation Method of Reconnaissance and Strike Tasks for Heterogeneous UAVs
Authors: Deng Hanqiang , Huang Jian , Liu Quan , Zhao Tuo , Zhou Cong , Gao Jialong
Published: February 2023

Unmanned aerial vehicles (UAVs) are becoming more and more widely used in battlefield reconnaissance and target strikes because of their high cost-effectiveness, but task planning for large-scale UAV swarms is a problem that needs to be solved. To solve the high-risk problem caused by incomplete information for the combat area and the potential coordination between targets when a heterogeneous UAV swarm performs reconnaissance and strike missions, this paper proposes a distributed task-allocation algorithm. The method prioritizes tasks by evaluating the swarm’s capability superiority to tasks to reduce the search space, uses the time coordination mechanism and deterrent maneuver strategy to reduce the risk of reconnaissance missions, and uses the distributed negotiation mechanism to allocate reconnaissance tasks and coordinated strike tasks. The simulation results under the distributed framework verify the effectiveness of the distributed negotiation mechanism, and the comparative experiments under different strategies show that the time coordination mechanism and the deterrent maneuver strategy can effectively reduce the mission risk when the target is unknown. The comparison with the centralized global optimization algorithm verifies the efficiency and effectiveness of the proposed method when applied to large-scale UAV swarms. Since the distributed negotiation task-allocation architecture avoids dependence on the highly reliable network and the central node, it can further improve the reliability and scalability of the swarm, and make it applicable to more complex combat environments.
An Efficient Virtual Machine Consolidation Algorithm for Cloud Computing
Authors: Yuan Ling , Wang Zhenjiang , Sun Ping , Wei Yinzhen
Published: February 2023

With the rapid development of integration in blockchain and IoT, virtual machine consolidation (VMC) has become a heated topic because it can effectively improve the energy efficiency and service quality of cloud computing in the blockchain. The current VMC algorithm is not effective enough because it does not regard the load of the virtual machine (VM) as an analyzed time series. Therefore, we proposed a VMC algorithm based on load forecast to improve efficiency. First, we proposed a migration VM selection strategy based on load increment prediction called LIP. Combined with the current load and load increment, this strategy can effectively improve the accuracy of selecting VM from the overloaded physical machines (PMs). Then, we proposed a VM migration point selection strategy based on the load sequence prediction called SIR. We merged VMs with complementary load series into the same PM, effectively improving the stability of the PM load, thereby reducing the service level agreement violation (SLAV) and the number of VM migrations due to the resource competition of the PM. Finally, we proposed a better virtual machine consolidation (VMC) algorithm based on the load prediction of LIP and SIR. The experimental results show that our VMC algorithm can effectively improve energy efficiency.
Quantum Machine Learning for Distributed Quantum Protocols with Local Operations and Noisy Classical Communications
Authors: Chittoor Hari Hara Suthan , Simeone Osvaldo
Published: February 2023

Distributed quantum information processing protocols such as quantum entanglement distillation and quantum state discrimination rely on local operations and classical communications (LOCC). Existing LOCC-based protocols typically assume the availability of ideal, noiseless, communication channels. In this paper, we study the case in which classical communication takes place over noisy channels, and we propose to address the design of LOCC protocols in this setting via the use of quantum machine learning tools. We specifically focus on the important tasks of quantum entanglement distillation and quantum state discrimination, and implement local processing through parameterized quantum circuits (PQCs) that are optimized to maximize the average fidelity and average success probability in the respective tasks, while accounting for communication errors. The introduced approach, Noise Aware-LOCCNet (NA-LOCCNet), is shown to have significant advantages over existing protocols designed for noiseless communications.
Coupling Quantum Random Walks with Long- and Short-Term Memory for High Pixel Image Encryption Schemes
Authors: Liang Junqing , Song Zhaoyang , Sun Zhongwei , Lv Mou , Ma Hongyang
Published: February 2023

This paper proposes an encryption scheme for high pixel density images. Based on the application of the quantum random walk algorithm, the long short-term memory (LSTM) can effectively solve the problem of low efficiency of the quantum random walk algorithm in generating large-scale pseudorandom matrices, and further improve the statistical properties of the pseudorandom matrices required for encryption. The LSTM is then divided into columns and fed into the LSTM in order for training. Due to the randomness of the input matrix, the LSTM cannot be trained effectively, so the output matrix is predicted to be highly random. The LSTM prediction matrix of the same size as the key matrix is generated based on the pixel density of the image to be encrypted, which can effectively complete the encryption of the image. In the statistical performance test, the proposed encryption scheme achieves an average information entropy of 7.9992, an average number of pixels changed rate (NPCR) of 99.6231%, an average uniform average change intensity (UACI) of 33.6029%, and an average correlation of 0.0032. Finally, various noise simulation tests are also conducted to verify its robustness in real-world applications where common noise and attack interference are encountered.
Ultrasoft Classical Systems at Zero Temperature
Authors: de Mello Matheus , Díaz-Méndez Rogelio , Mendoza-Coto Alejandro
Published: February 2023

At low temperatures, classical ultrasoft particle systems develop interesting phases via the self-assembly of particle clusters. In this study, we reach analytical expressions for the energy and the density interval of the coexistence regions for general ultrasoft pairwise potentials at zero temperatures. We use an expansion in the inverse of the number of particles per cluster for an accurate determination of the different quantities of interest. Differently from previous works, we study the ground state of such models, in two and three dimensions, considering an integer cluster occupancy number. The resulting expressions were successfully tested in the small and large density regimes for the Generalized Exponential Model α, varying the value of the exponent.
Reliable Optimization of Arbitrary Functions over Quantum Measurements
Authors: Luo Jing , Shang Jiangwei
Published: February 2023

As the connection between classical and quantum worlds, quantum measurements play a unique role in the era of quantum information processing. Given an arbitrary function of quantum measurements, how to obtain its optimal value is often considered as a basic yet important problem in various applications. Typical examples include but are not limited to optimizing the likelihood functions in quantum measurement tomography, searching the Bell parameters in Bell-test experiments, and calculating the capacities of quantum channels. In this work, we propose reliable algorithms for optimizing arbitrary functions over the space of quantum measurements by combining the so-called Gilbert’s algorithm for convex optimization with certain gradient algorithms. With extensive applications, we demonstrate the efficacy of our algorithms with both convex and nonconvex functions.
Intellectual Capital and Technology as Factors of Career Success: Role of Income Inequality
Authors: Lasisi Taiwo T. , Lazareva Elena I. , Abramyan Gor A. , Gavrilova Julia V. , Murzin Anton D.
Published: February 2023

The United Nations Sustainable Development Goals (UNSDGs) elaborately promote “sustained, inclusive, and sustainable economic growth, full and productive employment, and decent work for all” (Goal 8: SDGs). Considering that there has not been any cross-country comparison of the role of intellectual capital in career success, this study examines the nexus between intellectual capital and career success through the channels of income inequality, information, and communication technology from 1997 to 2018 for six European Economic Area (EEA) countries with high human development index (HDI). Using the Pooled Mean Group Autoregressive distributive lag model, results show that there is a positive and linear relationship between intellectual capital, income inequality, information and communication technology, and career success in the long run. Findings from the causality test reveal there is one-way causality running from information and communication technology and career success as well as intellectual capital to career success. These findings suggest that intellectual capital is important for career success; therefore, policymakers need to invest in developing and improving intellectual capital to ensure objective career success among the nationals.
Impact of Money Supply in Different States of Inflation and Economic Growth in South Africa
Authors: Buthelezi Eugene Msizi
Published: February 2023

This paper investigates the impact of the money supply in different states of inflation and economic growth in South Africa from 1990 to 2021. The term “states” defines periods of low and high rates of economic variables of interest. Markov-switching dynamic regression (MSDRM) and time-varying parameter structural vector autoregression (TVP-VAR) are used in this paper. The contribution of this paper is not only based on the long run but also on the examination of the impact of the money supply in different states of inflation and economic growth. Moreover, the use of shock accounts for time-varying elasticity. It is found that there is a 0.70% decrease in the gross domestic product for a 1% increase in money supply in state 1, while in state 2, the money supply was insignificant. The money supply had a negative and a positive impact on inflation in states 1 and 2, with rates of 0.05% and 0.35% in the respective states. The money supply had a high multiplier effect on gross domestic product and inflation. More than 5 years were spent in each state for both gross domestic product and inflation, while the transition probability of moving and returning to each state is significant. The trade-off of using the money supply for economic growth and inflation is evident in South Africa. It is recommended that the state of the economy be considered when using the money supply in an effort to stimulate economic growth or stabilise inflation.
Trombe Wall System’s Thermal Energy Output Analysis at a Factory Building
Authors: Prozuments Aleksejs , Borodinecs Anatolijs , Bajare Diana
Published: February 2023

Solar energy utilization for covering and offsetting the heating loads of buildings, is a sustainable way to reduce energy consumption (electricity, gas, etc.) for space heating. As such, a Trombe wall technology is a classic passive solar heating system used in buildings, that can be modified and applied to cold climate regions. This work presents a case study on a Trombe wall’s application in relation to its thermal energy output for space heating purposes at a factory building in central Latvia. The solar radiation and temperature measurements were carried out throughout the months of June to October. The results show that the examined wall has a monthly energy yield of 120 to 290 kWh, suggesting that Trombe wall systems are applicable as a secondary space heating source in cold climate regions such as northern Europe, however, a number of design and structural aspects have to be thoroughly considered.
Effect of Centrifugal Force on Power Output of a Spin-Coated Poly(Vinylidene Fluoride-Trifluoroethylene)-Based Piezoelectric Nanogenerator
Authors: Jeong Dong Geun , Singh Huidrom Hemojit , Kim Mi Suk , Jung Jong Hoon
Published: February 2023

While poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)) film is an excellent piezoelectric material for mechanical energy harvesting, the piezoelectric output varies considerably with the spin coating conditions. Herein, we reported a systematic evaluation of the structural, electrical, mechanical, and microstructural properties of spin-coated P(VDF-TrFE) films obtained at various distances from the center, as well as under different rotational speeds. With increasing distance, the remnant polarization, dielectric constant, and crystallinity of the films increased, which resulted in enhanced piezoelectric power at the largest distance. With increasing rotational speed, the remnant polarization, dielectric constant, and crystallinity of the films initially increased and then decreased, while the Young’s modulus continuously increased. This resulted in an enhanced piezoelectric power at a given rotational speed. The piezoelectric power is proportional to the remnant polarization and inversely proportional to the Young’s modulus. The highest (2.1 mW) and lowest (0.5 mW) instantaneous powers were obtained at the largest (1.09 μC/cm2·GPa−1) and smallest (0.60 μC/cm2·GPa−1) value of remnant polarization over Young’s modulus, respectively. We explain these behaviors in terms of the centrifugal force-induced shear stress and grain alignment, as well as the thickness-dependent β-phase crystallization and confinement. This work implies that the spin coating conditions of distance and rotational speed should be optimized for the enhanced power output of spin-coated P(VDF-TrFE)-based piezoelectric nanogenerators.
Market Manipulation in Stock and Power Markets: A Study of Indicator-Based Monitoring and Regulatory Challenges
Authors: Hao Yuna , Vand Behrang , Delgado Benjamin Manrique , Baldi Simone
Published: February 2023

In recent years, algorithmic-based market manipulation in stock and power markets has considerably increased, and it is difficult to identify all such manipulation cases. This causes serious challenges for market regulators. This work highlights and lists various aspects of the monitoring of stock and power markets, using as test cases the regulatory agencies and regulatory policies in diverse regions, including Hong Kong, the United Kingdom, the United States and the European Union. Reported cases of market manipulations in the regions are examined. In order to help establish a relevant digital regulatory system, this work reviews and categorizes the indicators used to monitor the stock and power markets, and provides an in-depth analysis of the relationship between the indicators and market manipulation. This study specifically compiles a set of 10 indicators for detecting manipulation in the stock market, utilizing the perspectives of return rate, liquidity, volatility, market sentiment, closing price and firm governance. Additionally, 15 indicators are identified for detecting manipulation in the power market, utilizing the perspectives of market power (also known as pricing power or market structure), market conduct and market performance. Finally, the study elaborates on the current challenges in the regulation of stock and power markets in terms of parameter performance, data availability and technical requirements.
Investigation on Laminar Flow and Heat Transfer of Helium–Xenon Gas Mixtures with Variable Properties
Authors: Zhou Biao , Sun Jun , Sun Yuliang
Published: February 2023

The space Brayton nuclear reactor system usually adopts the helium–xenon gas mixture (He–Xe) as the working fluid. The flow of He–Xe in the micro channel regenerator of the system is generally laminar. Since the properties of He–Xe are significantly different from those of common pure gases, the impact of this difference on the laminar flow and heat transfer needs to be evaluated. In present study, the numerical simulations of laminar convective heat transfer for helium, nitrogen and He–Xe are conducted by Ansys Fluent. Compared with simulation results, the applicability of existing laminar friction factor (f) and Nusselt number correlations is evaluated. By establishing the functions of property ratios with the temperature ratio and the mixing ratio, a new laminar f correlation for property-variable He–Xe is proposed. Results show that the calculation error of existing f correlations for He–Xe is obviously large, exceeding 13%. With the new f correlation, the predictions of laminar f for He–Xe are in good agreement with the simulation results in the fully developed region, and the calculation error is reduced to 3%.
AWEbox: An Optimal Control Framework for Single- and Multi-Aircraft Airborne Wind Energy Systems
Authors: De Schutter Jochem , Leuthold Rachel , Bronnenmeyer Thilo , Malz Elena , Gros Sebastien , Diehl Moritz
Published: February 2023

In this paper, we present AWEbox, a Python toolbox for modeling and optimal control of multi-aircraft systems for airborne wind energy (AWE). AWEbox provides an implementation of optimization-friendly multi-aircraft AWE dynamics for a wide range of system architectures and modeling options. It automatically formulates typical AWE optimal control problems based on these models, and finds a numerical solution in a reliable and efficient fashion. To obtain a high level of reliability and efficiency, the toolbox implements different homotopy methods for initial guess refinement. The first type of method produces a feasible initial guess from an analytic initial guess based on user-provided parameters. The second type implements a warm-start procedure for parametric sweeps. We investigate the software performance in two different case studies. In the first case study, we solve a single-aircraft reference problem for a large number of different initial guesses. The homotopy methods reduce the expected computation time by a factor of 1.7 and the peak computation time by a factor of eight, compared to when no homotopy is applied. Overall, the CPU timings are competitive with the timings reported in the literature. When the user initialization draws on expert a priori knowledge, homotopies do not increase expected performance, but the peak CPU time is still reduced by a factor of 5.5. In the second case study, a power curve for a dual-aircraft lift-mode AWE system is computed using the two different homotopy types for initial guess refinement. On average, the second homotopy type, which is tailored for parametric sweeps, outperforms the first type in terms of CPU time by a factor of three. In conclusion, AWEbox provides an open-source implementation of efficient and reliable optimal control methods that both control experts and non-expert AWE developers can benefit from.
Adaptive Neural Network Q-Learning-Based Full Recurrent Adaptive NeuroFuzzy Nonlinear Control Paradigms for Bidirectional-Interlinking Converter in a Grid-Connected Hybrid AC-DC Microgrid
Authors: Awais Muhammad , Khan Laiq , Khan Said Ghani , Awais Qasim , Jamil Mohsin
Published: February 2023

The stability of a hybrid AC-DC microgrid depends mainly upon the bidirectional interlinking converter (BIC), which is responsible for power transfer, power balance, voltage solidity, frequency and transients sanity. The varying generation from renewable resources, fluctuating loads, and bidirectional power flow from the utility grid, charging station, super-capacitor, and batteries produce various stability issues on hybrid microgrids, like net active-reactive power flow on the AC-bus, frequency oscillations, total harmonic distortion (THD), and voltage variations. Therefore, the control of BIC between AC and DC buses in grid-connected hybrid microgrid power systems is of great importance for the quality/smooth operation of power flow, power sharing and stability of the whole power system. In literature, various control schemes are suggested, like conventional droop control, communication-based control, model predictive control, etc., each addressing different stability issues of hybrid AC-DC microgrids. However, model dependence, single-point-failure (SPF), communication vulnerability, complex computations, and complicated multilayer structures motivated the authors to develop online adaptive neural network (NN) Q-learning-based full recurrent adaptive neurofuzzy nonlinear control paradigms for BIC in a grid-connected hybrid AC-DC microgrid. The proposed strategies successfully ensure the following: (i) frequency stabilization, (ii) THD reduction, (iii) voltage normalization and (iv) negligible net active-reactive power flow on the AC-bus. Three novel adaptive NN Q-learning-based full recurrent adaptive neurofuzzy nonlinear control paradigms are proposed for PQ-control of BIC in a grid-connected hybrid AC-DC microgrid. The control schemes are based on NN Q-learning and full recurrent adaptive neurofuzzy identifiers. Hybrid adaptive full recurrent Legendre wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control, Hybrid adaptive full recurrent Mexican hat wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control, and Hybrid adaptive full recurrent Morlet wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control are modeled and tested for the control of BIC. The controllers differ from each other, based on variants used in the antecedent part (Gaussian membership function and B-Spline membership function), and consequent part (Legendre wavelet, Mexican hat wavelet, and Morlet wavelet) of the full recurrent adaptive neurofuzzy identifiers. The performance of the proposed control schemes was validated for various quality and stability parameters, using a simulation testbench in MATLAB/Simulink. The simulation results were bench-marked against an aPID controller, and each proposed control scheme, for a simulation time of a complete solar day.
On the Exact Analytical Formulas of Leakage Current-Based Supercapacitor Model Operating in Industrial Applications
Authors: Ali Ziad M. , Calasan Martin , Aleem Shady H. E. Abdel , Hasanien Hany M.
Published: February 2023

The resistance–capacitance (RC) model is one of the most applicable circuits for modeling the charging and discharging processes of supercapacitors (SCs). Although this circuit is usually used in the electric and thermal investigation of the performance of SCs, it does not include leakage currents. This paper presents exact analytical formulas of leakage-current-based supercapacitor models that can be used in industrial applications, i.e., constant-power-based applications. In the proposed model, current and voltage are represented as a solution of nonlinear equations that are solved using the standard Newton method. The proposed expressions’ accuracy is compared with the results obtained using traditional numerical integration methods with leakage current formulation and other methods, found in the literature, with no leakage current formulation. The results confirm that including leakage current represents a more accurate and realistic manner of modeling SCs. The results show that the derived expressions are precise, allowing the generation of results that closely match those obtained using traditional numerical-based methods. The derived expressions can be used to investigate SCs further and achieve more accurate and efficient regulation and control of SCs in different applications.
Numerical Simulation of the Heat Transfer and Flow Characteristics of Pulse Tube Refrigerators
Authors: Meng Yuan , Cui Zheng , Shao Wei , Ji Wanxiang
Published: February 2023

Because of the unequal diameter between the pulse tube and the heat exchangers at the two sides, the fluid entering the pulse tube from the heat exchanger easily forms a complex disturbing flow in the pulse tube, which causes energy loss and affects the performance of a pulse tube refrigerator. This study proposes a numerical model for predicting the flow and heat transfer characteristics of pulse tube refrigerators. Three cases of adding conical tube transitions between the pulse tube and the heat exchanger are studied, and the results indicate that the conical tube transition can reduce the fluid flow velocity at the inlet and outlet of the pulse tube and reduce the size of the vortex at the boundary of the pulse tube. In comparison with the tapered transition of 45° on only one side of the pulse tube, both sides can maintain the temperature gradient, effectively decrease the effect of the disturbing flow, and significantly improve the cooling performance of the pulse tube.
Research and Innovation Needs for the Waste-To-Energy Sector towards a Net-Zero Circular Economy
Authors: Dal Pozzo Alessandro , Lucquiaud Mathieu , De Greef Johan
Published: February 2023

This perspective article aims to identify key research priorities to make the waste-to-energy sector compatible with the societal goals of circularity and carbon neutrality. These priorities range from fundamental research to process engineering innovations and socio-economic challenges. Three focus areas are highlighted: (i) the optimization of flue gas cleaning processes to minimize gaseous emissions and cross-media, (ii) the expansion of process control intelligence to meet targets for both material recovery and energy recovery, and (iii) climate neutrality, with the potential for negative emissions via the removal of atmospheric carbon dioxide across the full cycle of the waste resource. For each area, recent research trends and key aspects that are yet to be addressed are discussed.
A Comprehensive Assessment of Clean Coal Fuels for Residential Use to Replace Bituminous Raw Coal
Authors: Wang Yi , Liang Bin , Li Dong , Zheng Hua’an , Lei Yuan , Teng Haipeng , Altaf Adnan Raza
Published: February 2023

Residential coal combustion is a major source of air pollution in developing countries, including China. Indeed, precisely measuring the real-time emission of major air pollutants is often challenging and can hardly be repeated at a lab-scale. In this study, for the first time, two clean coals initiated from raw bituminous coal were burned for real-time estimation of air pollution characteristics and their thermal efficiencies in different stoves. Moreover, thermodynamic equilibrium simulations were investigated for slagging parameters using Factsage 7.1 at reaction temperature 800~1600 °C. Results revealed that the firepower of clean coals (Briquetted coal and Semi-coke) was much higher (2.2 kW and 2.1 kW) than raw coal (1.8 kW) in a traditional stove. However, the thermal efficiencies were remarkably increased (13.3% and 13.5%) in an improved stove for briquetted coal and semi-coke, respectively. The emission of major air pollutants including carbon monoxide (CO), sulfur dioxide (SO2), particulate matter (PM), polycyclic aromatic hydrocarbons (PAHs), non-methane hydrocarbons (NMHCs) from both coal and semi-coke was significantly reduced. Thermodynamic equilibrium calculations indicate that briquetted coal is not susceptible to slagging under the reaction conditions in the household stove. The current study provides guidance for the selection of alternative and efficient clean coal fuels in rural areas for household purposes coupled with public health and safety.
Perspectives for the Use of Biogas and Biomethane in the Context of the Green Energy Transformation on the Example of an EU Country
Authors: Rogala Zbigniew , Stanclik Michał , Łuszkiewicz Dariusz , Malecha Ziemowit
Published: February 2023

Natural gas is the primary source of energy, accounting for around 20% of the world’s energy production. It is also a key element in reducing CO2 emissions due to its key role in stabilizing renewable energy sources. At the same time, natural gas as a fossil fuel that emits CO2 should be limited. A natural candidate that can ensure the stabilization of RES and at the same time reduce the demand for fossil fuels is biogas. Successful use of biogas requires a thorough understanding of the potential, structure, and specifics of its sources and production technology. The presented study provides a perspective and a brief overview of the existing potential of biogas production. Poland was chosen as the case study and it was shown that almost 90% of the Polish biogas potential comes from sources with a capacity below 100 Nm3/h, which is the current minimum commercially available capacity of the biogas purification and liquefaction technologies. Consequently, full utilization of these sources would require a downsizing of these technologies.
Experimental Research to Increase the Combustion Efficiency in the Top-Lit Updraft Principle Based Gasifier
Authors: Pavel Ioan , Rădoi Radu Iulian , Matache Gabriela , Popescu Ana-Maria Carla , Pavel Kati
Published: February 2023

The recovery of vegetal waste for energy purposes is one of the ways to increase the amount of energy obtained from renewable sources. The Top-Lit Updraft (TLUD) gasification and combustion process is recognized as the least polluting of all other combustion processes, resulting in a sterile charcoal called biochar, which can be used as an amendment in agricultural soils. The purpose of this research was to determine the influence of excess air in the combustion area compared to the (theoretical) calculated requirement for a TLUD energy module. Most scientific publications on this topic recommend primary/secondary air flow rate ratios of 1/3 or 1/4. In this study, the two recommended ratios were tested, and it was found that better energy results correspond to the ratio of 1/3. For this 1/3 ratio, the investigations continued in order to optimize the combustion process. The results achieved demonstrate that the excess combustion air flow of 30% improves the performance of the energy module due to the increase in oxygen supply and the increase in air speed in the combustion area of the syngas resulting from gasification. Increasing the excess combustion air flow rate by +50% had the effect of lowering the temperature in the flame due to the cooling of the combustion gases caused by a too high rate of excess cold air flow.
Technoeconomic Feasibility of Bioenergy Production from Wood Sawdust
Authors: Alizadeh Peyman , Tabil Lope G. , Mupondwa Edmund , Li Xue , Cree Duncan
Published: February 2023

In this study, the technoeconomic feasibility of bioenergy production from sawdust under four different case scenarios is simulated and compared. These scenarios include: (1) heat and electricity generation from raw sawdust; (2) pellet production from sawdust; (3) and (4) integrated biorefinery approach for the simultaneous manufacturing of multiple products (steam-exploded and torrefied pellets) and co-products (furfural, hydroxy methyl furfural (HMF), acetic acid), along with heat and electricity generation. Economic assessments such as cost analysis, payback time (PBT), net present value (NPV) and internal rate of return (IRR) were determined for these scenarios. The results showed that the approach of producing torrefied pellets, furfural, and acetic acid, along with co-generated heat and electricity, in terms of multiproducts and profitability (NPV (at 7%): USD 38.29 M) was preferable over other alternatives. In terms of simplified technology and other economic indices (PBT: 2.49 year, IRR: 51.33%, and return on investment (ROI): 40.1%), the scenario for producing pellets from wood sawdust was more promising than others. If plant capacity was not a limiting factor, the optimal size for the combined heat and power (CHP) plant was between 250–300 kt for the main product. Additionally, untreated and treated pellet plants equipped with CHP had an optimal size of 150–200 kt of wood pellets per year.
Power Quality Transient Detection and Characterization Using Deep Learning Techniques
Authors: Rodrigues Nuno M. , Janeiro Fernando M. , Ramos Pedro M.
Published: February 2023

Power quality issues can affect the performance of devices powered by the grid and can, in severe cases, permanently damage connected devices. Events that affect power quality include sags, swells, waveform distortions and transients. Transients are one of the most common power quality disturbances and are caused by lightning strikes or switching activities among power-grid-connected systems and devices. Transients can reach very high magnitudes, and their duration spans from nanoseconds to milliseconds. This study proposed a deep-learning-based technique that was supported by convolutional neural networks and a bidirectional long short-term memory approach in order to detect and characterize power-quality transients. The method was validated (i.e., benchmarked) using an alternative algorithm that had been previously validated according to a digital high-pass filter and a morphological closing operation. The training and performance assessments were carried out using actual power-grid-measured data and events.
Water Content in Transformer Insulation System: A Review on the Detection and Quantification Methods
Authors: Arsad Siti Rosilah , Ker Pin Jern , Jamaludin Md. Zaini , Choong Pooi Ying , Lee Hui Jing , Thiviyanathan Vimal Angela , Yang Ghazali Young Zaidey
Published: February 2023

Water can be an irritant to a power transformer, as it is recognized as a major hazard to the operation of transformers. The water content of a transformer insulation system comprises the water in the transformer insulation oil and in the cellulose paper. The increase in the water content in the insulation system leads to reduced breakdown voltage, accelerated aging of the oil–paper insulation system, and the possibility of producing bubbles at high temperatures. Therefore, various techniques have been applied to measure the water content in both oil and paper insulation. This article comprehensively reviews and analyzes the methods (technically or nontechnically) that have been used to monitor the water content in transformer insulation systems. Apart from discussing the advantages and major drawbacks of these methods, the accuracy, measurement time, and cost of each technique are also elucidated in this review. This review can be extremely useful to the utility in monitoring and maintaining the good condition of transformers. Based on the reviewed methods and their challenges, a few future research directions and prospects for determining the water content in transformer insulation systems are outlined, such as utilizing artificial intelligence and enhancing current techniques.
Advanced Method of Variable Refrigerant Flow (VRF) Systems Designing to Forecast Onsite Operation—Part 2: Phenomenological Simulation to Recoup Refrigeration Energy
Authors: Radchenko Mykola , Radchenko Andrii , Trushliakov Eugeniy , Koshlak Hanna , Radchenko Roman
Published: February 2023

This paper focuses on the application of speed-regulated compressors (SRCs) to cover changeable heat loads with high efficiency in conventional air conditioning systems (ACS) as well as in the more advanced variable refrigerant flow (VRF)-type outdoor and indoor ACS. In reality, an SRC is an oversized compressor, although it can operate efficiently at part loads. The higher the level of regulated loads (LRL) of the SRC, the more the compressor is oversized. It is preferable to reduce the size of the SRC by covering the peak loads and recouping the excessive refrigeration energy reserved at decreased actual loads within the range of regulated loads. Therefore, the range of changeable loads is chosen as the object to be narrowed by using the reserved refrigeration capacity. Thus, the general fundamental approach of dividing the overall heat load range of the ACS into the ranges with changeable and unchangeable loads, as previously developed by the authors, is applied for the range of primary changeable loads. Due to this innovative step, the principle of two-stage outdoor air conditioning according to changeable and unchangeable loads, also proposed by the authors, has been extended over the range of primary changeable loads to reduce the level of refrigeration capacity regulation and SRC size. To realize this, part of the changeable load range is offset by the reserved refrigeration capacity, leading to a reduction in the changeable load range and the SRC size by approximately 20% for temperate climatic conditions.
Experimental Determination of Thermal Couplings in Packages Containing Multiple LEDs
Authors: Janicki Marcin , Ptak Przemysław , Torzewicz Tomasz , Górecki Krzysztof
Published: February 2023

Light Emitting Diodes (LEDs) are the most commonly used light sources. Temperature strongly affects their operation. Considering that multiple devices are often placed in a single housing, thermal couplings between devices become important. This problem is illustrated here based on the example of a light source containing four LEDs in a single package. Thermal analyses are carried out based on measured transient temperature responses. The measurement results are processed employing the Network Identification by Deconvolution method. The obtained results demonstrate clearly that depending on the device mounting manner and applied cooling condition the temperature rise value induced in neighboring devices can exceed 70% of the rise in the heating diode. Consequently, thermal models of such LEDs should consider not only self-heating effects, but also thermal interactions with the other diodes.
Two-Stage Short-Term Power Load Forecasting Based on RFECV Feature Selection Algorithm and a TCN–ECA–LSTM Neural Network
Authors: Liang Hui , Wu Jiahui , Zhang Hua , Yang Jian
Published: February 2023

To solve the problem of feature selection and error correction after mode decomposition and improve the ability of power load forecasting models to capture complex time series information, a two-stage short-term power load forecasting method based on recursive feature elimination with a cross validation (RFECV) algorithm and time convolution network–efficient channel attention mechanism–long short-term memory network (TCN–ECA–LSTM) is presented. First, the load sequence is decomposed into a relatively stable set of modal components using variational mode decomposition. Then, the RFECV-based method filters the feature set of each modal component to construct the best feature set. Finally, a two-stage prediction model based on TCN–ECA–LSTM is established. The first stage predicts each modal component and the second stage reconstructs the load forecast based on the predicted value of the previous stage. This paper takes actual data from New South Wales, Australia, as an example, and the results show that the method proposed in this paper can build the feature set reliably and efficiently and has a higher accuracy than the conventional prediction model.
Retrofitting of the District Heating System Based on the Application of Heat Pumps Operating with Natural Refrigerants
Authors: Požgaj Damir , Pavković Branimir , Delač Boris , Glažar Vladimir
Published: February 2023

The implementation of renewable energy sources and heat pumps with natural refrigerants in the existing 3rd-generation district heating (DH) systems is a promising technology for the conversion to a 4th-generation DH system. This paper aims to investigate this transition through a case study for the existing DH system in Croatia. The district of Rijeka, which is considered in the case study, has an existing 3rd-generation DH system with a capacity of 9.2 MW, which was originally designed for a temperature regime of 130/70 °C and produces thermal energy from natural gas. In order to use heat pumps efficiently in such a system, the temperature of the distribution system and the energy consumption should be reduced. Trnsys software was used to perform complete dynamic simulations of the DH system. Used baseline models were validated according to the actual energy consumption reported by the heat supplier. The application of heat pumps with natural refrigerants in combination with on-site electricity production (PV system) can almost eliminate system CO2 emissions, reduce primary energy consumption and lower operation costs. Simulations were performed for 18 different scenarios that included 2 different building envelope variants. The results were compared, then discussed and conclusions were drawn.
Modelling Energy Data in a Generalized Additive Model—A Case Study of Colombia
Authors: Berbesi Lina , Pritchard Geoffrey
Published: February 2023

Energy demand modelling is essential for reliable informing and framing energy policy decisions. More accurate modelling betters ensuring availability of energy and energy quality. Energy availability is related to energy access across the country and defines important economic measures such as energy poverty, which plays a critical role in developing countries. Energy quality is related to the reliability of the supply for correctly estimating energy needs. To incorporate spatial and temporal components of energy in a way that availability and quality are accurately assessed, this article discussed a number of suitable task methods for this (Second-generation GAMs with one-dimensional smoothers: Cyclic/Non-Cyclic Cubic Splines and two-dimensional smoothers: Markov Random Fields/Tensor Splines Interactions). The results showed that the complete consideration of both temporal and spatial aspects leads to a better fitted model which explains more of the data variation.
Increasing Energy Efficiency Based on the Kaizen Approach
Authors: Androniceanu Armenia , Enache Ioana-Catalina , Valter Elena-Narcisa , Raduica Florin-Felix
Published: February 2023

The energy crisis, an increasing concern of the industrial sector, is characterized by increased energy costs and low energy availability. As part of an effort to increase energy efficiency, the present study assessed the impact of applying lean tools on the energy consumption of a manufacturing organization. Using the Kaizen approach, the organization’s processes were analyzed to identify the main vulnerabilities and make the required changes to increase consumption performance. Several technical improvements were performed to reach increased energy efficiency. Results showed an energy efficiency increase; consumption was reduced by up to 7.5% in the production line, 3.5% in the extruder stage, and up to 20% for the injection stage of the manufacturing process. The results suggest that the organization’s energy reduction targets have been achieved. Standardization of procedures was useful in the development of Kaizen projects which could be applied with certain limitations. The implications of these results for understanding energy consumption reduction are discussed.
Generalized Method of Mathematical Prototyping of Energy Processes for Digital Twins Development
Authors: Khalyutin Sergey , Starostin Igor , Agafonkina Irina
Published: February 2023

The use of digital twins in smart power systems at the stages of the life cycle is promising. The dynamics of such systems (smart energy renewable sources, smart energy hydrogen systems, etc.), are determined mainly by the physical and chemical processes occurring inside the systems. The basis for developing digital twins is reliable mathematical models of the systems. In the present paper, the authors present a method of energy processes mathematical prototyping—an overall approach to modeling processes of various physical and chemical natures based on modern non-equilibrium thermodynamics, mechanics, and electrodynamics. Controlled parameters are connected with measured ones by developing a theoretically correct system of process dynamics equations with accuracy up to the experimentally studied properties of substances and processes. Subsequent transformation into particular mathematical models of a specific class of systems makes this approach widely applicable. The properties of substances and processes are given in the form of functional dependencies on the state of the system up to experimentally determined constant coefficients. The authors consider algorithms for identifying the constant coefficients of the functions of substances and processes properties, which complement the proposed unified approach of designing models of various physical and chemical nature systems.