bluuebunny/crossref_metadata_2025 huggingface dataset:
Kinetic and Thermodynamic Study of Ag+, Cu2+, and Zn2+ Ion Adsorption on LTA for High-Performance Antibacterial Coating
Authors: Yang Jiayuan , Cui Jixing , Zhang Haiping , Zhang Hui , Zhu Jesse
Published: December 2024

Antibacterial powder coatings have attracted increasing attention with the awakening of people’s health awareness. Silver antibacterial agent has been widely used in coating system due to its superior stability and durability. However, silver ions have the problems of excessive release rate and the tendency to cause yellowing of the coating film. The addition of Cu2+ and Zn2+ can effectively alleviate these two phenomena. In this paper, the ternary exchange kinetics of Ag+, Cu2+, and Zn2+ were studied to provide a theoretical basis for the synthesis of LTA-Ag-Cu-Zn. The reaction kinetics study shows that the selectivity and the adsorption capacity of LTA to Ag+ is higher than that of Cu2+ and Zn2+. The thermodynamic analysis discovers that LTA has the highest selectivity for Ag+, and the exchange between the two is spontaneous. In contrast, the selectivity of LTA to Cu2+ and Zn2+ is concentration-dependent. By establishing the three-ion competitive adsorption curve, it is found that the selectivity of Ag+ is the highest, and the selectivity of copper and zinc is similar. These trends result from Ag+ ions’ low hydration energy, small hydration radius, and strong electronegativity. This research lays the groundwork for developing high-performance LTA-Ag-Cu-Zn tri-ion exchange antibacterial agents.
Evaluation of Bonding Strength of Pipeline Coating Based on Circumferential Guided Waves
Authors: Ma Yunxiu , Ding Xiaoran , Wang Aocheng , Liu Gang , Chen Lei
Published: December 2024

The anti-corrosion layer of the pipe provides corrosion resistance and extends the lifespan of the whole pipeline. Heat-shrinkable tape is primarily used as the pipeline joint coating material bonded to the pipeline weld connection position after heating. Delineating the bonding strength and assessing the quality of the bonded structure is crucial for pipeline safety. A detection technology based on nonlinear ultrasound is presented to quantitatively evaluate the bonding strength of a steel-EVA-polyethylene three-layer annulus bonding structure. Using the Floquet boundary condition, the dispersion curves of phase velocity and group velocity for a three-layer annulus bonding structure are obtained. Additionally, wave structure analysis is employed in theoretical study to choose guided wave modes that are appropriate for detection. In this paper, guided wave amplitude, frequency attenuation, and nonlinear harmonics are used to evaluate the structural bonding strength. The results reveal that the detection method based on amplitude and frequency attenuation can be used to preliminarily screen the poor bonding, while the acoustic nonlinear coefficient is sensitive to bonding strength changes. This study introduces a comprehensive and precise pipeline joint bonding strength detection system leveraging ultrasonic-guided wave technology for pipeline coating applications. The detection system determines the bonding strength of bonded structures with greater precision than conventional ultrasonic inspection methods.
Tailoring the Ductility of Ti-6Al-4V Titanium Alloy Fabricated by Laser Power Bed Fusion at Liquid Nitrogen Temperature
Authors: Xie Bichen , Zeng Wei , Xia Tian , Wang Lianbo , Chen Kun
Published: December 2024

By tailoring different microstructural features, this study verifies that the laser powder bed fusion (LPBF)-fabricated Ti-6Al-4V titanium alloy with a fully α/β lamellar structure exhibits excellent ductility at liquid nitrogen temperature. HT-800 was obtained by holding at 800 °C for two hours and then furnace-cooled, resulting in a microstructure consisting of residual martensitic α’ phase, lamellar α phase, and particulate β phase. The HT-900 was obtained by holding at 900 °C for two hours and then furnace-cooled, completely eliminating the multi-level martensitic α’ phase generated during the LPBF process and resulting in an α/β lamellar structure. HT-900 achieved an elongation of 11% at liquid nitrogen temperature, a 47% improvement over the HT-800. After low-temperature strain fracture, the proportions of 61.38°<11–20> twin boundaries in the HT-800 and HT-900 were 21.4% and 26.4%, respectively, indicating that a substantial amount of deformation twinning is activated at liquid nitrogen temperature. Twinning induces the activation of slip systems by altering the orientation of surrounding grains. The coordinated plastic deformation of twinning and slip enhances the ductility of the HT-900 at 77 K. The results show that the LPBF-TC4 titanium alloy with a fully α/β lamellar structure exhibits superior, coordinated plastic deformation capabilities at 77 K, maintaining high strength while achieving greater ductility and fracture toughness.
The Impact of Microenvironment and Dysplasia Types on the Prognosis of Myelodysplastic Syndrome
Authors: Guvendir Bakkaloglu Irem , Zemheri Itir Ebru , Kaya Ali Hakan , Kilicaslan Emrah
Published: December 2024

Background: A detailed examination of bone marrow (BM) aspiration and biopsy can provide clues regarding the course of the disease as well as the diagnostic features of myelodysplastic syndrome (MDS). Methods: Our aim is to reveal the histomorphological features of MDS, investigate the impact of dysplasia types on prognosis, and highlight the importance of the microenvironment. Results: In 130 (93.5%) of 139 cases, Wright–Giemsa-stained BM aspiration slides were evaluated, and the dysplasia diversity was examined in detail. A regression analysis of dysplasia features significant for overall survival revealed that the presence of hyperlobulation in the megakaryocytic series (p = 0.014, odds ratio = 3.485) and the presence of an abnormal localization of immature progenitors (ALIP) (p = 0.010, odds ratio = 2.206) were significantly associated with poor prognosis. Additionally, an increase in the microvessel density (MVD) was found to be associated with a poor prognosis (p < 0.001). A multiple regression analysis identified that MVD is the most significant parameter (p = 0.014). Conclusions: The diversity of dysplasia in BM aspiration and biopsy does not predict MDS subtypes; however, certain cytomorphological dysplasia types can provide insights regarding survival. The microenvironment’s impact on MDS pathogenesis is undeniable, with ALIP and MVD presence and frequency being significant factors. Thus, BM histomorphological examination, beyond its diagnostic role, also offers prognostic insights
The Applicability and Limitations of the Spectrofluorometric Method for Determination of ALDH1 Activity in Serum and Plasma
Authors: Michorowska Sylwia , Wiśniewska Agnieszka , Wolinowska Renata , Wroczyński Piotr , Giebułtowicz Joanna
Published: December 2024

Background: Aldehyde dehydrogenase class 1 (ALDH1) is an enzyme that is ubiquitously distributed in adult tissues and may serve as a prognostic marker in various cancer types. In blood, 99% of ALDH1 is found in erythrocytes; although, it was also demonstrated that leukocytes and platelets exhibit ALDH activity. No ALDH activity was detected in plasma, even when employing the highly sensitive fluorometric method with 7-methoxy-1-naphthaldehyde as a substrate. However, some reports have been released describing stable and measurable ALDH1 activity in the serum of healthy subjects using 6-methoxy-2-naphthaldehyde as a substrate and a Shimadzu RF—5301 spectrofluorometer. Methods: Our study aimed to verify whether ALDH1 activity can be measured in plasma or serum (n = 80) using 6-methoxy-2-naphthaldehyde as a substrate and a highly sensitive Hitachi F7000 spectrofluorometer, which offers a higher signal-to-noise ratio compared to the Shimadzu RF-5301. Additionally, HPLC with fluorometric detection was used to validate the results (n = 25) and analyze the influence of hemolysis (n = 5) and liver cell damage (n = 15) on ALDH1 activity in serum. Results: Measurable ALDH activity in serum/plasma was very rarely detected using a spectrofluorometer (2 cases out of 80). However, background drift in assays without coenzyme addition was observed, and it may be easily mistaken for ALDH or oxidase activity. Therefore, the spectrofluorometer drift observed in blank assays and modified by a matrix, e.g., enhanced in protein-rich samples, should be considered in ALDH1 activity assays. Conclusions: The spectrofluorometric method has limited applicability for determining ALDH activity in plasma and serum. HPLC can measure ALDH1 activity in plasma or serum; however, factors like hemolysis and elevated liver enzymes significantly affect activity and must be considered in diagnostic interpretations. To enhance research quality on ALDH1 as a biomarker for diseases, including cancers, we recommend using control samples, reference materials, and purifying commercially available aldehyde substrates to improve method sensitivity.
Advances and Perspectives in Multilevel Converters: A Comprehensive Review
Authors: Benevieri Alessandro , Cosso Simone , Formentini Andrea , Marchesoni Mario , Passalacqua Massimiliano , Vaccaro Luis
Published: December 2024

In contemporary power electronics, multilevel converters stand at the forefront of high-power, high-voltage applications, offering superior performance in terms of efficiency, reduced harmonics, and improved voltage waveform quality compared to traditional two-level converters. Their capability to synthesize waveforms with multiple voltage levels has garnered significant attention across various industrial sectors, including renewable energy systems, electric vehicles, and high-voltage power transmissions. This paper provides a comprehensive overview of multilevel converter technology, encompassing their classification, main topologies, recent advancements, and emerging trends. By exploring the evolution of multilevel converter technology and identifying future research directions, researchers and engineers can gain valuable insights into the design, optimization, and application of these advanced power electronic systems.
Dual-Network Layered Network: A Method to Improve Reliability, Security, and Network Efficiency in Distributed Heterogeneous Network Transmission
Authors: Qi Shengyuan , Yang Lin , Ma Linru , Jiang Shanqing , Cheng Guang
Published: December 2024

This article delves into the routing architecture and reliable transmission service framework of dual-network layered networks, with a focus on analyzing their core design ideas and implementation strategies. In the context of increasing network complexity today, traditional single-network architectures are unable to meet diverse application needs. Therefore, dual-network layered networks, as an innovative solution, are gradually receiving attention from both academia and industry. This article first analyzes the key technical elements in the dual-network layered network architecture, including the optimization of routing algorithms, distributed consensus, reliability assurance mechanisms for packet transmission, and dynamic allocation strategies for network resources. Through in-depth research on these technologies, this article elaborates on the important role of dual-network layered networks in building efficient and stable transmission environments, providing important theoretical foundations and technical support for the construction and optimization of future network systems.
Distributed Semi-Supervised Partial Multi-Label Learning over Networks
Authors: Xu Zhen , Chen Weibin
Published: December 2024

Inthis paper, a distributed semi-supervised partial multi-label learning (dS2PML) algorithm is proposed, which can be used to address the problem of distributed classification of partially multi-labeled data and unlabeled data. In this algorithm, we utilize the multi-kernel function together with the label correlation term to construct the discriminant function. In addition, to obtain a decentralized implementation, we design a reconstructed error on the labeling confidence based on globally common basic data that are selected by a distributed strategy. By exploiting the similarity structure among feature and label spaces under the sparsity constraint, the labeling confidences of partially multi-labeled and unlabeled data are estimated in a decentralized manner. Meanwhile, by using the sparse random feature map to approximate the kernel feature map, the multi-label classifier can be trained under the supervision of the estimated labeling confidence. Experiments on multiple real datasets are conducted to evaluate the learning performance of the proposed approach. According to the experimental results, the average ranks of all the comparison algorithms evaluated on five evaluation metrics are computed. The ranking results show that the average ranks of our algorithm in terms of hamming loss, one error, average precision, ranking loss, and coverage are 3.16, 2.27, 2.15, 2.38, and 2.18, respectively. The average ranks of the dS2PML are second only to the corresponding centralized S2PML (cS2PML) algorithms and higher than other existing comparison algorithms in five evaluation metrics. The average rank differences in terms of Hamming loss, one error, average precision, ranking loss, and coverage between our proposed algorithm and the closest comparison algorithm are 0.28, 1.67, 1.80, 1.15, and 1.62, respectively. Additionally, owing to the distributed storage and decentralized processing of training data, our proposed dS2PML algorithm reduces CPU time by more than 65% and memory consumption by more than 6% compared to the centralized comparison algorithms. The experimental results indicate that our proposed algorithm outperforms the other state-of-the-art algorithms in classification accuracy, CPU time, and memory consumption.
Decision Making for Communication Anti-Jamming Tasks with Knowledge-Graph-Based Q-Learning
Authors: Feng Xijin , Niu Yingtao , Liu Qi , Zhou Quan
Published: December 2024

Due to the severe threats posed by smart jammers, anti-jamming decision making has become an essential technology for wireless communications. Most of the existing anti-jamming decision-making approaches have adopted Q-Learning to improve accuracy. However, the performances of these approaches drop dramatically in fast-varying jamming environments. Thus, an advanced Q-Learning approach utilizing domain knowledge graph as prior knowledge is proposed to select the optimal strategies with high flexibility and accuracy in different jamming environments. Specifically, by taking a knowledge graph that contains anti-jamming knowledge to initialize the Q-table, Q-Learning can avoid becoming stuck at local suboptimal solutions and obtain accurate strategies with fewer iterations. The iterations of the proposed approach are one third of those of other approaches based on Q-Learning and the average rewards of the proposed approach have improved by 2 percent. Numerical results demonstrate the optimality and excellent performance of the proposed approach over various existing benchmarks.
A Review of Research on Dual-Side Control Methods for Magnetic Coupling Wireless Power Transfer Systems Based on Dual-Active Bridge Converters
Authors: Jiang Yanwei , Li Yingao , Yang Jingjing , Shu Xujian
Published: December 2024

Magnetic Coupling Wireless Power Transfer (MCWPT) technology has broad application prospects due to its high degree of freedom in energy exchange and unparalleled security. In recent years, various controllers have been added to WPT systems to improve energy transfer performance. Among them, the Dual-Side Control (DSC) approach, which employs Dual-Active Bridge (DAB) converters with simultaneous control of the transmitter and receiver, has made great progress. The DAB structure has a compromise between cost and complexity, and the DSC can bring a high enough degree of control freedom to the MCWPT system to easily achieve efficiency optimization and desired output characteristics. However, various DSC methods have different characteristics. Therefore, this paper reviews and summarizes the current research status of DSC methods based on the DAB converters in MCWPT systems. Wireless communication is one of the decisive factors in the design of a control strategy, which is categorized in this paper according to the main approaches used in all the papers. Various DSC strategies are described in detail and compared comprehensively according to the control variables and control objectives. This review will be highly beneficial to research entities and industry professionals as a ready reference for the control design in MCWPT.
Implementation of an FPGA-Based System to Process Images and Match Keypoints on High-Resolution Pictures
Authors: Bundschuh Sina , Kunze Jan , Kuhnert Klaus-Dieter
Published: December 2024

Processing scenery and finding points of interest is crucial for applications in robotics and aerospace missions. Those areas require efficient and reliable visual input processing. Here, field programmable gate arrays (FPGAs) offer essential advantages, like low power consumption compared to CPUs, performing a large number of calculations simultaneously, and having compact hardware. This paper presents an FPGA system that processes incoming camera data, finds points of interest, and matches them across different images on high-resolution images (2048 × 1088). It is a novel approach to implement the complete image processing pipeline on high-resolution images within the FPGA fabric without additional hardware. For keypoint detection and matching, our work uses a modified SIFT algorithm optimized for FPGA implementation processing and a nearest neighbor-based matching method. It was implemented on a Xilinx Kintex-7 FPGA and partially on a NanoXplore NG-Ultra to evaluate a radiation-hardened FPGA for space applications. On the Kintex-7, the keypoint detection achieves a speed of 33 ms per image, and its features are matched on up to 5 images per second. Judging by the resource utilization of one image processing module on the NG-Ultra, porting the entire system on a radiation-hardened FPGA appears feasible.
Hydro–Wind–PV–Integrated Operation Optimization and Ultra-Short-Term HESS Configuration
Authors: Zhang Jinhua , Wang Haizheng , Fan Chuanxi , Hu Jiahao , Zhang Xinyue
Published: December 2024

In order to address the challenges associated with optimizing multi-timescale operations and allocating ultra-short-term energy storage for HWP integration, this study takes into account both the economic and reliability aspects of the HWP integration base. It proposes a model for optimizing operations and allocating energy storage capacity, achieving optimization across long-term, short-term, and ultra-short-term operations for an MECB. Initially, operation optimization is implemented for an entire group of terraced hydropower plants by regulating them with annual regulating capabilities on a long-term timescale. The objectives are to maximize the daily average minimum output and annual power generation. Subsequently, short-term operation optimization focuses on maximizing HWP power feed-in, minimizing new energy power curtailment, and reducing residual load standard deviation while ensuring the guaranteed output optimization results for the long term. Finally, to mitigate ultra-short-term fluctuations in new energy, a HESS with specified capacity and power is configured with the goal of minimizing comprehensive costs. Additionally, to address the challenge of smoothing negative fluctuations, which is hindered by charging and discharging efficiency limitations, a variable baseline is introduced, deviating from the conventional 0 MW baseline. A simulation study based on data from the hydro–wind–PV hybrid project in the Beipanjiang River Basin, China, demonstrates the following: (1) after long-term system optimization, the total power generation capacity of the system increases by 9.68%, while the peak-to-valley difference in output is significantly reduced; (2) short-term system optimization significantly reduces both the average variance in residual loads and the amount of power curtailed over five representative days; (3) the system incorporates 398.62 MWh of lithium-ion battery storage with a power of 412.47 MW and 51.09 MWh of supercapacitor storage with a power of 223.32 MW, which, together, completely smooth out the ultra-short-term fluctuations in new energy output.
An Inversion Method Based on Prior Knowledge for Deep Cascading Pipeline Defects
Authors: Chen Pengchao , Xuan Wenbo , Li Rui , Wang Fuxiang , Fu Kuan
Published: December 2024

With the robust growth of the national economy, the demand for oil and natural gas continues to rise, heightening the significance of pipeline transportation in the energy sector. However, long-term pipeline operations are often subjected to factors such as corrosion, aging, and damage, which can result in leaks and safety incidents, posing significant threats to life, property, and environmental integrity. Consequently, timely and precise detection of pipeline defects and estimation of their sizes hold paramount practical importance. This research endeavors to employ advanced information technology and artificial intelligence to explore methods for pipeline defect detection and size estimation grounded in prior knowledge. The aim is to enhance the accuracy and reliability of pipeline defect diagnosis and ensure the safe operation of pipelines. The primary innovative work includes the development of a preprocessing method based on prior knowledge, the design of an adaptive algorithm for estimation of defect size, and the creation of an algorithm for estimation of deep cascade pipeline defect size. These methods effectively combine traditional mechanisms and data-driven approaches, leveraging the strengths of both to improve performance, accuracy, and robustness. The proposed methodology demonstrates superior accuracy and stability in defect inversion, providing strong technical support for the quantitative assessment of pipeline defects, which is significant for fault diagnosis and the precise maintenance of long-distance pipelines.
Generalized Distribution Network Data-Gathering Procedure for ADMS Deployment
Authors: Bekut Duško , Švenda Goran , Kanjuh Sonja , Koturević Verica
Published: December 2024

The implementation of advanced distribution management systems (ADMS) in today’s distribution networks (DNs) is critical for efficient operation. However, ADMS deployment poses significant challenges, particularly in gathering the extensive and diverse data required to model DNs. This paper presents a generalized, systematic, and algorithm-driven procedure for optimizing the missing data-gathering process during ADMS deployment. The procedure identifies the required DN model data by layers, considers distribution power utility (DPU) data sources, identifies missing data, and evaluates methods and the missing data-gathering ways, considering cost, duration, and specific constraints for data gathering. The developed approach provides DPUs with a clear, structured, and proactive approach to data gathering, significantly reducing complexity and enhancing efficiency. The practical application of this procedure is demonstrated using a real-world unbalanced DN example from a North American DPU, showcasing its potential to streamline ADMS deployment and deliver tangible operational benefits.
Co-Optimization of Speed Planning and Energy Management for Plug-In Hybrid Electric Trucks Passing Through Traffic Light Intersections
Authors: Liu Xin , Shi Guojing , Yang Changbo , Xu Enyong , Meng Yanmei
Published: December 2024

To tackle the energy-saving optimization issue of plug-in hybrid electric trucks traversing multiple traffic light intersections continuously, this paper presents a double-layer energy management strategy that utilizes the dynamic programming–twin delayed deep deterministic policy gradient (DP-TD3) algorithm to synergistically optimize the speed planning and energy management of plug-in hybrid electric trucks, thereby enhancing the vehicle’s passability through traffic light intersections and fuel economy. In the upper layer, the dynamic programming (DP) algorithm is employed to create a speed-planning model. This model effectively converts the nonlinear constraints related to the position, phase, and timing information of each traffic signal on the road into time-varying constraints, thereby improving computational efficiency. In the lower layer, an energy management model is constructed using the twin delayed deep deterministic policy gradient (TD3) algorithm to achieve optimal allocation of demanded power through the interaction of the TD3 agent with the truck environment. The model’s validity is confirmed through testing on a hardware-in-the-loop test machine, followed by simulation experiments. The results demonstrate that the DP-TD3 method proposed in this paper effectively enhances fuel economy, achieving an average fuel saving of 14.61% compared to the dynamic programming–charge depletion/charge sustenance (DP-CD/CS) method.
Impact of Energy System Optimization Based on Different Ground Source Heat Pump Models
Authors: Lai Yingjun , Gao Yan , Gao Yaping
Published: December 2024

With rapidly developing new energy technologies, rational energy planning has become an important area of research. Ground source heat pumps (GSHPs) have shown themselves to be highly efficient. effective in reducing building or district energy consumption and operating costs. However, when optimizing integrated energy systems, most studies simplify the GSHP model by using the rated coefficient of performance (COP) of the GSHP unit, neglecting factors such as soil, buried piping, and actual operating conditions. This simplification leads to a deviation from the actual operation of GSHPs, creating a gap between the derived operational guidelines and real-world performance. Therefore, this paper examines a hotel equipped with photovoltaic panels, a GSHP, and a hybrid energy storage unit. By constructing models of the underground pipes, GSHP units, and pumps, this paper considers the thermal exchanger between the underground pipes and the soil, the thermal pump, and the operating status of the unit. The purpose is to optimize the running expenses using an enhanced mote swarm optimization (PSO) algorithm to calculate the optimal operating strategy of system equipment. Compared to the simplified energy system optimization model, the detailed GSHP unit model shows a 21.36% increase in energy consumption, a 13.64% decrease in the mean COP of the GSHP unit, and a 44.4% rise in system running expenses. The differences in the GSHP model affect the energy consumption results of the unit by changing the relationship between the power consumption of the PV system and the GSHP at different times, which in turn affects the operation of the energy storage unit. The final discussion highlights significant differences in the calculated system operating results derived from the two models, suggesting that these may profoundly affect the architectural and enhancement processes of more complex GSHP configurations.
Experimental Study on Enhanced Oil Recovery of Shallow Super-Heavy Oil in the Late Stage of the Multi-Cycle Huff and Puff Process
Authors: Hu Chunyu , Tao Jianqiang , Feng Meng , Wang Qian , Cao Hui , Su Hongmei , Sun Junke , Wang Wenfeng
Published: December 2024

The shallow, thin super-heavy oil reservoir demonstrates certain characteristics, such as shallow reservoir depths, low-formation temperature, and high crude oil viscosity at reservoir temperatures. In the current production process, the central area of P601 is undergoing high-frequency huff and puff operations, facing certain problems such as decreasing production, low recovery rates, and rapid depletion of formation pressure. Through physical simulation experiments, the various elements of HDNS-enhanced oil recovery technology were analyzed. Nitrogen plus an oil-soluble viscosity reducer can improve the thermal recovery and development effect of super-heavy oil. With the addition of the viscosity-reducing slug, the recovery rate of steam flooding was 58.61%, which was 23.32% higher than that of pure steam flooding; after adding the 0.8 PV nitrogen slug, the recovery rate increased to 76.48%. With the increased nitrogen injection dosage, the water breakthrough time was extended, the water cut decreased, and the recovery rate increased. Nitrogen also plays a role in profile control and plugging within the reservoir; this function can effectively increase the heating range, increase steam sweep efficiency, and reduce water cut. So, the synergistic effects of steam, nitrogen, and viscosity-reducing agents are good. This technology enhances the development of shallow-layer heavy oil reservoirs, and subsequent development technologies are being compared and studied to ensure the sustainable development of super-heavy oil reservoirs.
Investigating the Influence of Heat-Insulated Wheelhouses on Truck Tyre Temperature and Rolling Resistance
Authors: Hyttinen Jukka , Ussner Matthias , Österlöf Rickard , Jerrelind Jenny , Drugge Lars
Published: December 2024

Due to legislations introduced to prevent global warming, vehicle manufacturers must find new ways to reduce CO2 emissions. This paper explores a way to reduce rolling resistance by heat-insulating and covering a truck’s wheelhouse. Truck tyre rolling resistance was measured at +5 °C ambient temperature for consecutive speed steps in a climate wind chamber with and without heat insulation. The study showed that by encapsulating and insulating the wheelhouse, already generated strain-induced heat could be kept in the tyre, consequently leading to reduced rolling resistance. The tyre shoulder temperature was monitored during the experiments along with the tyre pressure. When the wheelhouses were encapsulated, a significant reduction in rolling resistance and an increase in tyre pressure and temperature were measured at all evaluated speed levels.
Operating Costs in the Polish Energy Sector: Challenges for Capital Groups
Authors: Borowiec Leszek , Wyrzykowska Barbara , Kacprzak Marzena , Król Agnieszka , Wolińska Emilia
Published: December 2024

Electricity is one of the most widely used energy sources. The climate crisis, public pressure to invest in renewable and low-carbon energy sources, and the reduction in industrial electricity consumption caused by the COVID-19 pandemic have a significant impact on the energy sector. In addition, military action in Europe is affecting energy generation capacity and availability, which raises the question of economic calculus, particularly regarding the cost of generation and supply. These factors affect the cost structure of those responsible for supplying energy and, in extreme cases, can lead to energy exclusion. The article aimed to identify differences in the presentation and interpretation of operating cost data from the individual and consolidated financial statements of Polish energy groups, which is of key importance for investors, analysts and decision-makers in the energy sector. The analysis uses data for 2018–2022 from the income statement. The research hypothesis is that the complexity of Polish energy groups in the Polish energy sector leads to ambiguity in the interpretation of cost data included in stand-alone and consolidated financial statements.
Performance Assessment of a Municipal Solid Waste Gasification and Power Generation System Integrated with Absorption Heat Pump Drying
Authors: Chen Handing , Song Xudong , Jian Yingfu
Published: December 2024

Gasification combined with syngas power generation technologies, such as fuel cells, gas turbines, and the organic Rankine cycle, present significant potential for the efficient disposal of municipal solid waste. This paper proposes a hybrid system that integrates municipal solid waste gasification with syngas power generation. An absorption heat pump was employed for drying wet municipal solid waste. The thermomechanical analysis and variable condition analysis of the proposed integrated system were conducted. The influences of municipal solid waste drying degree on the system performance were researched emphatically. The results indicated that the system effectively implemented cascade energy utilization, with the power generation from solid oxide fuel cells contributing the most to total power generation. The total power generation increased from 34,469.50 to 42,967.03 kW as the moisture content decreased from 40.0% to zero. Both total power generation efficiency and overall exergy efficiency of the proposed integrated system increased as the moisture content decreased. The municipal solid waste drying process utilizing an AHP is beneficial to the system.
Analytical Computation of the Maximum Power Point of Solar Cells Using Perturbation Theory
Authors: Tirado-Serrato José G. , Garcia Alfredo Sanchez , Maximov Serguei
Published: December 2024

To compute the maximum power point (MPP) from physical parameters of the single-diode model (SDM), it is necessary to solve a transcendental equation using numerical methods. This is computationally expensive and can lead to divergence problems. An alternative is to develop analytical approximations which can be accurate enough for engineering problems and simpler to use. Therefore, this paper presents approximations for computing the MPP of single-junction solar cells. Two special cases are considered: (i) SDM with only series resistance, and (ii) SDM with only shunt resistance. Power series closed-form expressions for the MPP are obtained using perturbation theory and the Lagrange inversion theorem. Validation of the formulas is performed using experimental data from six different technologies obtained from the NREL database and comparing the results with the numerical solution of the SDM and three approximations from the literature. The results show an absolute percentage error (APE) of less than 0.035% with respect to the real MPP measurements. In cases with limited computational resources, this value could be further improved by using a higher- or lower-order power-series approximation.
Power Loss and Electrothermal Characterization of Hybrid Power Integrated Modules for Industrial Servo Motor Drives
Authors: Cheng Hsien-Chie , Liu Yan-Cheng , Jhu Wen-You , Chiu Po-Kai , Chang Tao-Chih , Chiang Kuo-Ning
Published: December 2024

This study aims to facilitate the assessment of the electromagnetic-electrical-thermal coupled response of a developed 30 kHz/12 kW silicon carbide (SiC)/silicon (Si) hybrid power-integrated module (hPIM) during load operation. To achieve this goal, an efficient electromagnetic-circuit-thermal coupling (ECTC) analysis methodology is introduced. This ECTC methodology incorporates a fully integrated electromagnetic-circuit coupling (EMCC) analysis model for parasitic extraction in order to addressing their effects on power losses, and a simplified electrothermal coupling (SETC) analysis model for temperature evaluation in order to consider the coupling influence of the instantaneous junction temperature on instantaneous power losses. The SETC model couples a simple lookup table that maps the power loss (P) in terms of the temperature (T) constructed using the developed EMCC model, and an equivalent Foster thermal network model established through three-dimensional (3D) computational fluid dynamics (CFD) thermal flow analysis. This PT lookup table, replacing the tedious and time-consuming EMCC simulation, is responsible for fast estimation of temperature-dependent power losses. The proposed analysis models, namely the CFD, EMCC, and SETC analysis models, are validated through thermal experiments and detailed modeling. Finally, the influence of various operation conditions on the power losses of the hPIM during the power conversion operation is explored through parametric analysis.
Generalized Predictive Control with Added Zeros and Poles in Its Augmented Model for Power Electronics Applications
Authors: Cordero Raymundo , Caramalac Matheus , Ali Wisam
Published: December 2024

Generalized predictive control (GPC) became one of the most popular and useful control strategies for academic and industry applications. An augmented model is applied to predict the future plant responses. This augmented model can be designed to embed the model of the plant reference, allowing its tracking by the controller according to the internal model principle (IMP). On the other hand, the performance of many controllers can be improved by adding zeros and poles in their structures (e.g., lead and lag compensators). However, according to the authors’ research, adding arbitrary poles or zeros to the GPC augmented model has not been explored yet. This paper presents a simple methodology to add arbitrary zeros and poles in the GPC augmented model. A new augmented model state variable is defined. The control law of the proposed approach embeds zeros and poles when zero-pole cancellation is avoided. Simulation results (considering a LCL filter controlled by a single-phase inverter of 500 W and a polynomial reference tracking controller) and experimental tests (using a third-order linear plant controlled by a resonant controller) prove that the proposed approach improves the transient response of different kinds of predictive tracking controllers applied to control different plants (including power electronics applications), without affecting the steady-state tracking capabilities of the control systems.
Coordinated Optimization Method for Distributed Energy Storage and Dynamic Reconfiguration to Enhance the Economy and Reliability of Distribution Network
Authors: Zhao Caihong , Duan Qing , Lu Junda , Wang Haoqing , Sha Guanglin , Jia Jiaoxin , Zhou Qi
Published: December 2024

To fully leverage the application potential of distributed energy storage systems (DESS) and network reconfiguration, a coordinated optimization method is proposed to enhance the economic efficiency of distribution networks under normal conditions and the reliability of a power supply during fault conditions. First, a scenario-generation method is developed based on Latin hypercube sampling and Kantorovich distance synchronous back-substitution reduction is used to obtain the typical scenario of wind and solar output. Next, a planning operation coordinated optimization framework and model are established, considering both normal and fault states of the distribution network. In the planning layer, the objective is to minimize the annual comprehensive capital expenditures for the distribution network to improve the economic efficiency of the distribution network. The operation layer includes both normal operation and fault operation states, with the optimization goal of minimizing the sum of normal operation costs and the fault costs associated with load shedding. Subsequently, a hybrid optimization algorithm combining an improved Aquila Optimizer-Second-Order Cone Programming (IAO-SOCP) is proposed to solve the coordinated optimization model. Finally, the proposed coordinated optimization method is validated using an enhanced IEEE 33-bus distribution network case study. The results demonstrate that the method effectively reduces network losses and minimizes load shedding costs during fault conditions, thereby ensuring a balance between the economic efficiency and reliability of the distribution network.
Impact of Penalty Structures on Virtual Power Plants in a Day-Ahead Electricity Market
Authors: Song Youngkook , Chae Myeongju , Chu Yeonouk , Yoon Yongtae , Jin Younggyu
Published: December 2024

The rapid increase in distributed energy resources has augmented the significance of virtual power plants (VPPs), which are essential for the aggregation and management of variable renewable energy resources (RERs). The inherent variability and uncertainty of RERs necessitate the implementation of deviation penalties to address the discrepancies between the awarded bids and actual generation, which is crucial in maintaining market stability and ensuring reliable grid operations. Therefore, this study proposes a framework for deviation penalty structures, categorizing penalties based on three factors: the penalty scope, penalty rate, and penalty coefficient. The simulation results show that the penalty scope significantly influences the revenue of VPPs, with over-generation penalty structures typically yielding higher profitability. Conversely, dual-sided penalty structures result in lower total revenues compared to one-sided penalty structures. For instance, when the penalty price coefficient is set to 0.1, the total revenue of a dual-sided penalty structure is approximately 62.26% lower than that of a one-sided penalty structure during the morning period. The results also demonstrate that deviation penalty structures have a direct impact on power deviations and curtailment behavior. Finally, we offer recommendations for the design of an effective penalty structure aimed at assisting policymakers and distributed system operators (DSOs) in structuring market mechanisms, which not only facilitate the integration of RERs but also enhance their economic viability within electricity markets.
Optimized Scheduling Model Considering the Demand Response and Sequential Requirements of Polysilicon Production
Authors: Wang Xi , Chen Baorui , Xiao Yuduo , Liao Siyang , Ye Xi , Bai Jiayu
Published: December 2024

The polysilicon production process has significant potential to be made adjustable, and actively changing its production schedule to participate in grid dispatch can effectively alleviate the pressure on the power supply and balance demand while promoting renewable energy consumption. Considering the complex inter-coupling relationship between the sequential requirements and adjustable potential of polysilicon production, this paper analyzes the electricity consumption characteristics of various stages of a polysilicon production process that uses the improved Siemens method as its primary approach to production. The interaction between the polysilicon production process, equipment, and materials is modeled through the state–task network method, and the production timing requirements are transformed into constraint expressions. An optimized scheduling model that includes the production’s sequential requirements within a time-of-use electricity pricing context is established. Our analysis shows that the proposed model can formulate a feasible production plan with the lowest power purchase cost for polysilicon plants while meeting the production’s sequential requirements and product order demands.
Analysis for the Implementation of Surplus Hydropower for Green Hydrogen Production in Ecuador
Authors: Pinchao Paul , Torres Alejandra , Yánez Marco , Reina Salvatore , Cando Edgar
Published: December 2024

This study investigates the feasibility of utilizing surplus hydropower from Ecuador’s major hydroelectric plants to produce green hydrogen, a clean energy source that can be used to meet a large percentage of energy needs. Given Ecuador’s significant hydropower infrastructure, this approach leverages untapped energy resources for hydrogen production, with potential impacts on decarbonization strategies. A Pareto analysis identified five key hydroelectric plants that contribute the most to the national surplus. Using historical data from 2019 to 2023, a stochastic model was applied to estimate future surplus availability through 2030. The findings indicate that although Ecuador’s surplus hydropower peaked in 2021, the general trend shows a decline, suggesting an urgent need to capitalize on these resources efficiently. The results indicate a projected annual surplus of hydroelectric energy in Ecuador, ranging from 7475 to 3445 GWh over the next five years, which could be utilized for green hydrogen production. Ecuador thus has promising potential to become a green hydrogen producer, enhancing both regional energy security and carbon reduction goals. The reduction in energy availability for hydrogen production is attributed to the increasing energy demand and variable climatic conditions.
Research on SOC Prediction of Lithium-Ion Batteries Based on OLHS-DBO-BP Neural Network
Authors: Wang Genbao , Xue Yejian , Qiao Yafei , Song Chunyang , Ming Qing , Tian Shuang , Xia Yonggao
Published: December 2024

Accurately estimating the state of charge (SOC) of lithium-ion batteries is of great significance for extending battery lifespan and enhancing the efficiency of energy management. Regarding the issue of the relatively low estimation accuracy of SOC by the backpropagation neural network (BPNN), an enhanced dung beetle optimizer (DBO) algorithm is proposed to optimize the initial weights and thresholds of the BPNN. This overcomes the drawback of a single BP neural network being prone to local optimum and accelerates the convergence rate. Simulation analyses on the experimental data of NCM and A123 lithium batteries were conducted in Matlab R2022a. The results indicate that the proposed algorithm in this paper has an average SOC estimation error of less than 1.6% and a maximum error within 2.9%, demonstrating relatively high estimation accuracy and robustness, and it holds certain theoretical research significance.
A Model-Driven Approach for Estimating the Energy Performance of an Electric Vehicle Used as a Taxi in an Intermediate Andean City
Authors: Castillo-Calderón Jairo , Cordero-Moreno Daniel , Larrodé Pellicer Emilio
Published: December 2024

Regarding the decision to opt for vehicles with electric propulsion systems to achieve a sustainable future, much research has focused on the electrification of passenger cars, since this class of vehicles is the largest contributor of greenhouse gas emissions in the transportation sector. The purpose of this paper is to assess the energy performance of an electric vehicle used as a taxi in Loja, Ecuador, an intermediate Andean city, using a model-driven approach. Data acquisition was performed through the OBDII port of the KIA SOUL EV for 24 days and the variable mass of the vehicle was recorded as a function of the number of passengers; the effects of road gradient were also considered. The energy performance of the vehicle was simulated by developing an analytical model in MATLAB/Simulink. An average measured battery performance of 8.49 ± 1.4 km/kWh per day was obtained, where the actual energy regenerated was 31.2 ± 1.5%. To validate the proposed model, the results of the daily energy performance estimated with the simulation were compared with those measured in real driving conditions. The results demonstrated a Pearson correlation coefficient of 0.93, indicating a strong positive linear dependence between the variables. In addition, a coefficient of determination of 0.86 and a mean absolute percentage error of 3.35% were obtained, suggesting that the model has a satisfactory predictive capacity for energy performance.