bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative Studies on Methyl Ester Production from Pretreated Sludge Palm Oil Using Homogeneous and Heterogeneous Base Catalysts
Authors: Oo Ye Min , Juera-Ong Panupong , Pongraktham Kritsakon , Somnuk Krit
Published: September 2024

A heterogeneous base catalyst transesterification process with a calcium oxide (CaO) catalyst was performed to produce high-purity methyl ester (ME) from pretreated sludge palm oil (PSPO) derived from sludge palm oil (SPO). Additionally, a comparative analysis was conducted with potassium hydroxide (KOH) as a homogeneous base catalyst to assess the distinctions between heterogeneous and homogeneous base catalysts. The response surface methodology (RSM) was utilized to determine the optimal and recommended conditions for both transesterification processes. For heterogeneous transesterification, a varying CaO catalyst loading (10–60 wt.%), methanol (25–65 wt.%), and reaction time (60–180 min) were essential parameters. Meanwhile, homogeneous transesterification involved investigating the KOH catalyst loading (1–3 wt.%), methanol (1.8–5.5 wt.%), and reaction time (20–60 min). For the heterogeneous-base-catalyzed reaction, the recommended conditions were as follows: a molar ratio of methanol to oil of 5.83:1 (41.61 wt.%), 31.3 wt.% CaO, and a reaction time of 119.0 min, which resulted in a ME purity of 96.51 wt.%. The optimal conditions for homogeneous transesterification were a molar ratio of methanol to oil of 0.49:1 (3.45 wt.%), a 40 min reaction time, and a 1.39 wt.% KOH concentration, which achieved 96.59 wt.% ME and met the standard.
Levels of Amyloid Beta (Aβ) Expression in the Caenorhabditis elegans Neurons Influence the Onset and Severity of Neuronally Mediated Phenotypes
Authors: Sirwani Neha , Hedtke Shannon M. , Grant Kirsten , McColl Gawain , Grant Warwick N.
Published: September 2024

A characteristic feature of Alzheimer’s disease (AD) is the formation of neuronal extracellular senile plaques composed of aggregates of fibrillar amyloid β (Aβ) peptides, with the Aβ1-42 peptide being the most abundant species. These Aβ peptides have been proposed to contribute to the pathophysiology of the disease; however, there are few tools available to test this hypothesis directly. In particular, there are no data that establish a dose–response relationship between Aβ peptide expression level and disease. We have generated a panel of transgenic Caenorhabditis elegans strains expressing the human Aβ1-42 peptide under the control of promoter regions of two pan-neuronal expressed genes, snb-1 and rgef-1. Phenotypic data show strong age-related defects in motility, subtle changes in chemotaxis, reduced median and maximum lifespan, changes in health span indicators, and impaired learning. The Aβ1-42 expression level of these strains differed as a function of promoter identity and transgene copy number, and the timing and severity of phenotypes mediated by Aβ1-42 were strongly positively correlated with expression level. The pan-neuronal expression of varying levels of human Aβ1-42 in a nematode model provides a new tool to investigate the in vivo toxicity of neuronal Aβ expression and the molecular and cellular mechanisms underlying AD progression in the absence of endogenous Aβ peptides. More importantly, it allows direct quantitative testing of the dose–response relationship between neuronal Aβ peptide expression and disease for the first time. These strains may also be used to develop screens for novel therapeutics to treat Alzheimer’s disease.
Toilet Training Readiness Scale for 0–5-Year-Old Children: A New Measurement Tool Based on a Child-Centred Approach
Authors: Barutçu Adnan , Mete Burak , Demirhindi Hakan , Barutçu Saliha , Kıdı Aliye , Evliyaoğlu Nurdan
Published: September 2024

Background and Objectives: There is no standardised approach to toilet training in children. This study aimed to determine the factors affecting the duration of toilet training in children aged 0–5 years and to develop a tool to assess the child’s readiness to start toilet training. Materials and Methods: This cross-sectional study was conducted on 409 children aged 0–5 years. Social, economic, behavioural, and developmental characteristics that are effective in toilet training in healthy children were evaluated. A scale assessing children’s readiness for toilet training (Toilet Training Readiness Scale-TTRS) was developed and content validated. Results: The mean age of the 409 children included in this study was 44.69 ± 13.07 months (min = 4; max = 60 months). The mean age of initiation of toilet training was 26.8 months. Most frequently, urine and faeces trainings were started together (52.1%). In the logistic regression analysis performed to evaluate the factors affecting the duration of toilet training, it was found that the TTRS score, mother’s employment status, family type, child’s first reaction, toilet type, and continuity of training were important predictors. The duration of toilet training showed a weak negative correlation with the scores obtained from the TTRS and the number of children in the family but a weak positive correlation with the age at the beginning of toilet training. The TTRS scores were inversely proportional to the duration of toilet training. Conclusions: Family characteristics, socioeconomic conditions, and readiness of the child for and no interruption in toilet training are important in completing toilet training in a short time and successfully. If a child-focused approach is adopted, evaluating the child from this point of view and initiating the training at the appropriate time may help to complete a more successful and shorter toilet training. We recommend that the scale we have developed be studied in other studies and different groups.
Diatom Diversity from Watercourses of North-Eastern Kamchatka with Description of One New Species
Authors: Kezlya Elena M. , Glushchenko Anton M. , Kulikovskiy Maxim S.
Published: September 2024

A study of diatoms in eight watercourses (four spawning rivers and four streams) in the area of the Ozernovsky Mining and Metallurgical Complex (MMC) on the Kamchatka Peninsula was carried out for the first time. A total of 174 taxa were identified, and a comparative analysis of periphyton species diversity at the sampling stations was carried out. A new species for science was proposed: Gomphonema anissimovae Glushchenko, Kezlya & Kulikovskiy sp. nov. The composition and quantitative characteristics of plankton were analysed only in samples collected from rivers. It was shown that all rivers are oligotrophic in terms of phytoplankton biomass. The work includes lists of taxa with indication of their abundance, as well as illustrative material of found diatom taxa, which will provide an opportunity to monitor changes in planktonic and periphyton microalgae communities in the studied watercourses in the context of potentially high anthropogenic load from industry.
Controversies in Venous Thromboembolism Risk Assessment in Inflammatory Bowel Disease: A Narrative Review
Authors: Sharma Nikhil , Tewatia Pavit , Harvey Philip R. , Kumar Aditi
Published: September 2024

Inflammatory bowel disease (IBD) is a chronic inflammatory condition affecting the gastrointestinal tract with increasing rates of incidence and prevalence across the world. Complex inflammatory and prothrombotic pathophysiology in IBD makes venous thromboembolism (VTE) a common complication with significant morbidity and mortality. This risk is increased in pregnancy. As we continue to understand the pathogenesis of IBD, this article highlights the continued risk of VTE following discharge, for which there is currently no clear guidance, yet the risk of VTE remains high. Furthermore, we discuss this increased VTE risk in the context of pregnant IBD patients and the relevant current guidelines. Alongside this, medications that are used to manage IBD carry their own thrombotic risk, which clinicians should be aware of. Assessing VTE risks in IBD populations using newer medications should be a focus of future research.
Enhanced Multi-UAV Formation Control and Obstacle Avoidance Using IAAPF-SMC
Authors: Zhang Pengfei , Wang Zhongliu , Zhu Ziwen , Liang Qinyang , Luo Jiangyu
Published: September 2024

In response to safety concerns pertaining to multi-UAV formation flights, a novel obstacle avoidance method based on an Improved Adaptive Artificial Potential field (IAAPF) is presented. This approach enhances UAV obstacle avoidance capabilities by utilizing segmented attraction potential fields refined with adaptive factors and augmented with virtual forces for inter-UAV collision avoidance. To further enhance the control and stability of multi-UAV formations, a Sliding Mode Control (SMC) method is integrated into the IAAPF-based obstacle avoidance framework. Renowned for its robustness and ability to handle system uncertainties and disturbances, the SMC method is combined with a feedback control system that utilizes inner and outer loops. The outer loop generates the desired path based on the leader’s state and control commands, while the inner loop tracks these trajectories and adjusts the follower UAVs’ motions. This design ensures that obstacle feedback is accounted for before the desired state information is received, enabling effective obstacle avoidance while maintaining formation integrity. Integrating leader-follower formation control techniques with SMC-based multi-UAV obstacle avoidance strategies ensures the effective convergence of the formation velocity and spacing to predetermined values, meeting the cooperative obstacle avoidance requirements of multi-UAV formations. Simulation results validate the efficacy of the proposed method in reaching otherwise unreachable destinations within obstacle-rich environments, while ensuring robust collision avoidance among UAVs.
A Survey on Vision-Based Anti Unmanned Aerial Vehicles Methods
Authors: Wang Bingshu , Li Qiang , Mao Qianchen , Wang Jinbao , Chen C. L. Philip , Shangguan Aihong , Zhang Haosu
Published: September 2024

The rapid development and widespread application of Unmanned Aerial Vehicles (UAV) have raised significant concerns about safety and privacy, thus requiring powerful anti-UAV systems. This survey provides an overview of anti-UAV detection and tracking methods in recent years. Firstly, we emphasize the key challenges of existing anti-UAV and delve into various detection and tracking methods. It is noteworthy that our study emphasizes the shift toward deep learning to enhance detection accuracy and tracking performance. Secondly, the survey organizes some public datasets, provides effective links, and discusses the characteristics and limitations of each dataset. Next, by analyzing current research trends, we have identified key areas of innovation, including the progress of deep learning techniques in real-time detection and tracking, multi-sensor fusion systems, and the automatic switching mechanisms that adapt to different conditions. Finally, this survey discusses the limitations and future research directions. This paper aims to deepen the understanding of innovations in anti-UAV detection and tracking methods. Hopefully our work can offer a valuable resource for researchers and practitioners involved in anti-UAV research.
Autocatalytic Sets and Assembly Theory: A Toy Model Perspective
Authors: Raubitzek Sebastian , Schatten Alexander , König Philip , Marica Edina , Eresheim Sebastian , Mallinger Kevin
Published: September 2024

Assembly Theory provides a promising framework to explain the complexity of systems such as molecular structures and the origins of life, with broad applicability across various disciplines. In this study, we explore and consolidate different aspects of Assembly Theory by introducing a simplified Toy Model to simulate the autocatalytic formation of complex structures. This model abstracts the molecular formation process, focusing on the probabilistic control of catalysis rather than the intricate interactions found in organic chemistry. We establish a connection between probabilistic catalysis events and key principles of Assembly Theory, particularly the probability of a possible construction path in the formation of a complex object, and examine how the assembly of complex objects is impacted by the presence of autocatalysis. Our findings suggest that this presence of autocatalysis tends to favor longer consecutive construction sequences in environments with a low probability of catalysis, while this bias diminishes in environments with higher catalysis probabilities, highlighting the significant influence of environmental factors on the assembly of complex structures.
A Fresh Revisit of the Issues and Improvements in Impulse Invariance Filter Design for Infinite Impulse Response Filters
Authors: Kwan Chiman , Ferguson Hal
Published: September 2024

The objective of this paper is to first present some issues with impulse invariance filter (IIF) design during the design of digital infinite impulse response (IIR) filters. Engineers are often confused about some inconsistent observations. For instance, if the impulse response of a digital filter is designed using the impulse invariance procedure, then the analog and digital filters’ frequency and step responses are very different. Two simple remedies are presented in this paper. One is a post-processing approach that scales the frequency and step responses of the digital filter by the sampling interval T. Another one is a pre-processing approach that scales the impulse response of the analog filter by T. However, even after these remedies, there is still a steady state bias in the step response of the digital filter for certain cases where there is discontinuity in the analog impulse response. A recommendation is to include a correction term in the digital filter. After that, the steady state bias in the digital filter is then suppressed. Moreover, the MATLAB R2021a command “impinvar” needs to also include a correction term so that the frequency and step responses can be more accurate in the digital filter. Two comparative studies were carried out to compare the improved IIF filter with three competing digital IIR filter design methods. Although the above issues and improvements have been proposed by researchers in the past, many researchers, engineers, and students are still not aware of them. This paper provides a fresh revisit of these issues and improvements by using figures, equations, and examples. Proper credits are also given to those researchers who first pointed out those issues and improvements. It is hoped that through an open access journal, future rediscovery of issues and improvements in IIF can be prevented.
SCS-YOLO: A Defect Detection Model for Cigarette Appearance
Authors: Ding Yingchao , Zhou Hao , Wu Hao , Ma Chenrui , Yuan Guowu
Published: September 2024

Appearance defects significantly impact cigarette quality. However, in the current high-speed production lines, manual inspection and traditional methods are unable to satisfy the actual demands of inspection. Therefore, a real-time and high-precision defect detection model for cigarette appearance, SCS-YOLO, is presented. The model integrates space-to-depth convolution (SPD-Conv), a convolutional block attention module (CBAM), and a self-calibrated convolutional module (SCConv). SPD-Conv replaces the pooling structure to enhance the granularity of feature information. CBAM improves the ability to pay attention to defect locations. Improved self-calibrated convolution broadens the network’s receptive field and feature fusion capability. Additionally, Complete IoU loss (CIoU) is replaced with Efficient IoU Loss (EIoU) to enhance model localization and mitigate sample imbalance. The experimental results show that the accuracy of SCS-YOLO is 95.5% and the mAP (mean average precision) value is 95.2%. Compared with the original model, the accuracy and mAP value of the SCS-YOLO model are improved by 4.0%. Furthermore, the model achieves a detection speed of 216 FPS, meeting cigarette production lines’ accuracy and speed demands. Our research will positively impact the real-time detection of appearance defects in cigarette production lines.
Multi-Trajectory Planning Control Strategy for Hydropower Plant Bridge Crane Based on Evaluation Algorithm
Authors: Chen Tiehua , Xu Ming , Wu Guangxin , Dong Shihao , Liu Xinze
Published: September 2024

Currently, the research on crane trajectory planning mostly aims to, first, plan the trajectories of the crane and the trolley, and then to use a trial-and-error method or optimization algorithm to iteratively calculate the optimal trajectory parameters under the control of the optimal trajectory parameters to achieve the suppression of the swing angle. However, research on the fusion application of multi-trajectory planning algorithms is very rare. In addition, the existing methods are not suitable for the special operation control of hydropower plant bridge cranes. Based on the application scenario of hydropower plant bridge cranes, this paper proposes a comprehensive multi-trajectory control strategy based on the entropy weight technique for order preference, similarly to the ideal solution (TOPSIS) evaluation method. Specifically, the kinematic analysis of the crane is carried out and the trajectory evaluation index system is established. Secondly, under the walking constraint condition, four different trajectory planning algorithms are used to obtain the crane trajectory curve. In order to ensure the accuracy and comprehensiveness of the evaluation, the evaluation data are obtained through the Adams motion simulation platform. Finally, based on the entropy weight TOPSIS evaluation method, the optimal walking trajectory for each displacement is selected. The simulation and experimental results show that the evaluation method can select the optimal trajectory based on the motion characteristics of the trajectory algorithm in different displacement conditions, effectively reducing the load swing during the walking process of the crane and improving the positioning accuracy.
Vehicle Trajectory Prediction Based on Adaptive Edge Generation
Authors: Ren He , Zhang Yanyan
Published: September 2024

With the rapid evolution of intelligent driving technology, vehicle trajectory prediction has become a pivotal technique for enhancing road safety and traffic efficiency. In this domain, high-definition vector maps and graph neural networks (GNNs) play a vital role, supporting precise vehicle positioning and optimizing path planning, thereby improving the performance of intelligent driving systems. However, high-definition vector maps and traditional GNNs still encounter several challenges in trajectory prediction, such as high computational resource demands, long training times, and limited modeling capabilities for dynamic traffic environments and complex interactions. To address these challenges, this paper proposes an adaptive edge generator method, this method dynamically constructs and optimizes the connections between nodes in the GNN architecture, effectively enhancing the accuracy and efficiency of trajectory prediction. Specifically, we classify nodes into dynamic and static nodes based on their attributes, and devise differentiated edge construction strategies accordingly. For dynamic nodes, we introduce a relative angle factor, enabling the attention model to comprehensively consider the distance and intersection status between nodes, resulting in more accurate computation of edge weights. For static nodes, we utilize a length threshold to assess the feasibility of establishing connections between vehicles and lane lines, determining whether a connection should be established. Through this approach, we successfully reduce the algorithmic complexity, increase computational speed, and maintain high trajectory prediction accuracy. Tests on the Argoverse motion prediction dataset demonstrate that trajectory prediction utilizing the adaptive edge generator achieves an average displacement error (ADE) of 0.6681, a final displacement error (FDE) of 0.9864, and a miss rate (MR) of 0.0952. Furthermore, the model parameters are significantly reduced, validating the effectiveness of the proposed vehicle trajectory prediction method based on the adaptive edge generator.
The Influence of Drying Sewage Sludge with the Addition of Walnut Shells on Changes in the Parameters and Chemical Composition of the Mixture
Authors: Kujawiak Sebastian , Makowska Małgorzata , Waliszewska Bogusława , Janczak Damian , Brózdowski Jakub , Czekała Wojciech , Zyffert Artur
Published: September 2024

One method of preparing sludge for management and use is solar drying. To intensify the drying process, natural lignocellulosic additives can be used to alter the structure of the sludge and accelerate water evaporation. Light, hard materials with low absorption capacity are best suited for this purpose, e.g., walnut shells, which are unused waste. The aim of the study was to determine the impact of the evaluation of walnut shells on the sludge drying process and to assess the impact of the drying process on the chemical, physical, and fuel properties of the additive. The moisture content, crushing strength, chemical composition, and physical and fuel properties of mixtures were determined. A small addition of walnut shells (25%) was found to accelerate the drying process even in winter and spring (up to 30 days) compared to sludge without additives. Walnut shells retain their chemical composition and strength despite unfavourable conditions and a chemically aggressive environment, indicating they may be reused. The mixture containing sewage sludge and walnut shells has a calorific value of 15.6 MJ/kg, which is similar to wood; it is also fully biodegradable and suitable as a fertiliser to improve soil structure, as it contains approx. 80–90% DM (including approx. 40% carbon, 3% nitrogen, and other elements, such as phosphorus and potassium.)
Optimal Power Dispatch for Maximum Energy Community Welfare by Considering Closed Distribution Systems and Renewable Sources
Authors: De Oliveira-De Jesus Paulo M. , Yusta Jose M.
Published: September 2024

Regulatory boards are promoting closed distribution systems (CDSs), which are different from traditional public-access networks, that can be owned and managed by energy communities (ECs). The inclusion of local renewable energy potential and an adequate schedule of storage devices in a CDS allow cooperation among the EC’s members in order to reduce operational expenditure (OPEX), providing internally competitive electricity prices with respect to those provided by publicly regulated networks and electricity markets. The CDS operators can assume a new role as the centralized energy dispatchers of generation and storage assets in order to maximize the profits of the members of the EC. This paper proposes an innovative optimal active and reactive power dispatch model for maximum community welfare conditions. A key difference between this proposal and existing social-welfare-based dispatches on public-access networks is the exclusion of the profit of the external wholesale electricity market. The focus of the proposed method is to maximize the welfare of all community members. A remuneration framework based on a collective EC with a single frontier is adopted, considering agreements between members based on locational marginal pricing (CDS-LMP). Results from an illustrative case study show a reduction of 50% in the EC’s OPEX with a payback time of 6 years for investments in CDSs, renewable sources, and storage.
Co-Design of a Wind–Hydrogen System: The Effect of Varying Wind Turbine Types on Techno-Economic Parameters
Authors: Reichartz Thorsten , Jacobs Georg , Blickwedel Lucas , Frings Dustin , Schelenz Ralf
Published: September 2024

Green hydrogen is crucial for achieving climate neutrality and replacing fossil fuels in processes that are hard to electrify. Wind farms producing electricity and hydrogen can help mitigate stress on electricity grids and enable new markets for operators. While optimizing wind farms for electricity production is well-established, optimizing combined wind–hydrogen systems is a relatively new research field. This study examines the potential profit of wind–hydrogen systems by conducting a case study of an onshore wind farm near the North Sea. Varying turbine types from high wind-speed turbines (with high annual energy production) to low wind-speed turbines (with high full-load hours) are examined. Findings indicate that in a combined hydrogen system, the low wind-speed turbines, which are sub-optimal for mere electricity production, yield lower levelized costs of hydrogen at a higher hydrogen production. Although high wind-speed turbines generate higher profits under current market conditions, at high hydrogen prices and low electricity prices, low wind-speed turbines can yield higher total profit at this site. Therefore, an integrated optimization approach of wind–hydrogen systems can, in certain cases, lead to better results compared to an isolated, sequential optimization of each individual system.
Model Predictive Control of a Stand-Alone Hybrid Battery-Hydrogen Energy System: A Case Study of the PHOEBUS Energy System
Authors: Holtwerth Alexander , Xhonneux André , Müller Dirk
Published: September 2024

Model predictive control is a promising approach to robustly control complex energy systems, such as hybrid battery-hydrogen energy storage systems that enable seasonal storage of renewable energies. However, deriving a mathematical model of the energy system suitable for model predictive control is difficult due to the unique characteristics of each energy system component. This work introduces mixed integer linear programming models to describe the nonlinear multidimensional operational behavior of components using piecewise linear functions. Furthermore, this paper develops a new approach for deriving a strategy for seasonal storage of renewable energies using cost factors in the objective function of the optimization problem while considering degradation effects. An experimentally validated simulation model of the PHOEBUS Energy System is utilized to compare the performance of two model predictive controllers with a hysteresis band controller such as utilized for the real-world system. Furthermore, the sensitivity of the model predictive controller to the prediction horizon length and the temporal resolution is investigated. The prediction horizon was found to have the highest impact on the performance of the model predictive controller. The best-performing model predictive controller with a 14-day prediction horizon and perfect foresight increased the total energy stored at the end of the year by 18.9% while decreasing the degradation of the electrolyzer and the fuel cell.
Invasion Characteristics of Marginal Water under the Control of High-Permeability Zones and Its Influence on the Development of Vertical Heterogeneous Gas Reservoirs
Authors: Guo Ping , Zheng Jian , Dong Chao , Wang Zhouhua , Liao Hengjie , Fan Haijun
Published: September 2024

In-depth understanding of the gas–water seepage law caused by different degrees of gas layer perforation and varying gas production rates is key to determining a reasonable development technology policy for vertical heterogeneous edge-water gas reservoirs. Based on core physical data from the entire section of the X2 well, a large-scale high-pressure positive-rhythm profile model that takes into account the influence of “discontinuous interlayer” was innovatively established. The water intrusion process of the gas layer profile under different gas production rates and degrees of gas layer perforation was simulated using an electrical resistivity scanning device. The experimental model has an area of 3000 cm2, with a maximum pressure of 70 MPa and a maximum temperature resistance of 150 °C. It includes 456 evenly distributed fluid saturation test points to accurately monitor the gas–water distribution, addressing the issues of small bearing pressure and insufficient saturation monitoring points found in other large-scale models. The experimental results show that, in heterogeneous reservoirs, the high-permeability zone controls the invasion path of edge water, which is the main reason for the uneven invasion of edge water. For the positive-rhythm profile of the F layer, a higher gas production rate (1000 mL/min) shortens the water-free gas recovery period of the gas well and reduces the recovery rate. Perforating the upper two-thirds of the layer can inhibit edge-water breakthrough, prolong the water-free gas recovery period of the gas well, enable the gas–water interface to advance more uniformly, and enhance the recovery degree. The results of this study greatly enhance our understanding of the water invasion characteristics of positive-rhythm reservoirs under the influence of different gas production rates and varying degrees of gas layer perforation.
Experimental Investigations into a Hybrid Energy Storage System Using Directly Connected Lead-Acid and Li-Ion Batteries
Authors: Dascalu Andrei , Cruden Andrew J. , Sharkh Suleiman M.
Published: September 2024

This paper presents experimental investigations into a hybrid energy storage system comprising directly parallel connected lead-acid and lithium batteries. This is achieved by the charge and discharge cycling of five hybrid battery configurations at rates of 0.2–1C, with a 10–50% depth of discharge (DoD) at 24 V and one at 48 V. The resulting data include the overall round-trip efficiency, transient currents, energy transfers between the strings, and the amount of energy discharged by each string across all systems. The general observation is that the round-trip efficiency drops from a maximum of around 94–95% in the first stages of the charge/discharge process, when only the Li-ion strings are active, to around 82–90% when the lead-acid strings reach a DoD of up to 50%. The most important parameters in the round-trip efficiency function are the ratio between the Li-ion and lead-acid energy available and the charge/discharge current. The energy transfer between the strings, caused by the transient currents, is negligible in the first stages of the discharge and then grows, with the DoD peaking at around 60% DoD. Finally, during the first stage of discharge, when only the Li-ion strings are active, the amount of energy discharged varies with the discharge C rate, decreasing to almost half at between 0.2 and 1C.
Hydrogen, E-Fuels, Biofuels: What Is the Most Viable Alternative to Diesel for Heavy-Duty Internal Combustion Engine Vehicles?
Authors: Baldinelli Arianna , Francesconi Marco , Antonelli Marco
Published: September 2024

Hydrogen mobility embodies a promising solution to address the challenges posed by traditional fossil fuel-based vehicles. The use of hydrogen in small heavy-duty road vehicles based on internal combustion engines (ICEs) may be appealing for two fundamental reasons: Direct electrification seems less promising in heavy-duty transport systems, and fuel cell-based hydrogen vehicle implementation may not proceed at the expected pace due to higher investment costs compared to ICEs. On the other hand, hydrogen combustion is gaining attractiveness and relies on robust and cheap technologies, but it is not the only renewable solution. In this framework, this work presents a methodology to assess the Well-to-Wheel primary energy consumption and CO2 emissions of small heavy-duty vehicles. The methodology is applied in a real case study, namely a passenger coach traveling on a 100 km mission in non-optimized conditions. Therefore, the suitability of hydrogen is compared with standard diesel and other alternative diesel-type fuels (biodiesel and synthetic diesel types). Hydrogen shows competitivity with standard diesel from the point of view of CO2 emission reduction (−29%) while it hides a higher primary energy consumption (+40%) based on the current power-to-hydrogen efficiency declared by electrolyzer manufacturers. Nonetheless, HVO brings the highest benefits both from the point of view of primary energy consumption and emission reduction, namely −35% and 464–634 kgCO2/100km avoided compared to hydrogen. Moreover, the availability of HVO—like other biofuels—does not depend on carbon from CO2 capture and sequestration systems.
Experimental Research on Pressure Pulsation and Flow Structures of the Low Specific Speed Centrifugal Pump
Authors: Lv Weiling , Zhang Yang , Zhang Wenbin , Ni Ping , Li Changjiang , Chen Jiaqing , Gao Bo
Published: September 2024

The low specific speed centrifugal pump plays a crucial role in industrial applications, and ensuring its efficient and stable operation is extremely important for the safety of the whole system. The pump must operate with an extremely high head, an extremely low flow rate, and a very fast speed. The internal flow structure is complex and there is a strong interaction between dynamic and static components; consequently, the hydraulic excitation force produced becomes a significant factor that triggers abnormal vibrations in the pump. Therefore, this study focuses on a low specific speed centrifugal pump and uses a single-stage model pump to conduct PIV and pressure pulsation tests. The findings reveal that the PIV tests successfully captured the typical jet-wake structure at the outlet of the impeller, as well as the flow separation structure at the leading edge of the guide vanes and the suction surface. On the left side of the discharge pipe, large-scale flow separation and reverse flow happen as a result of the flow-through effect, producing a strong vortex zone. The flow field on the left side of the pressure chamber is relatively uniform, and the low-speed region on the suction surface of the guide vanes is reduced due to the reverse flow. The results of the pressure pulsation test showed that the energy of pressure pulsation in the flow passage of the guide vane occurs at the fBPF and its harmonics, and the interaction between the rotor and stator is significant. Under the same operating condition, the RMS value distribution and amplitude at fBPF of each measurement point are asymmetric in the circumferential direction. The amplitude of fBPF near the discharge pipe is lower, while the RMS value is higher. A complex flow structure is shown by the larger amplitude and RMS value of the fBPF on the left side of the pressure chamber. With the flow rate increasing, the energy at fBPF of each measurement point increases first and then decreases, while the RMS value decreases, indicating a more uniform flow field inside the pump.
A Lightweight Electric Meter Recognition Model for Power Inspection Robots
Authors: Song Shuangshuang , Tian Hongsai , Zhao Feng
Published: September 2024

Power inspection robots are essential for ensuring safe and optimal operation of power systems. However, during the operation of the power inspection robot, constraints imposed by computational and storage resources slow down the detection speed of the power system, failing to meet real-time monitoring requirements. To address these issues, this study proposes a lightweight electric meter recognition model for power inspection robots based on YOLOv5. The aim is to ensure efficient operation of the model on embedded devices, achieve real-time meter recognition, and enhance the practicality of the inspection robot. In the proposed model, GhostNet, a lightweight network, is employed as the YOLOv5 backbone feature extraction module, thus improving the model’s computational efficiency. In addition, the Wise-IoU (WIoU) loss function is used to improve the localization accuracy of the electric meter recognition model. Moreover, the GSConv module was introduced in the neck network for further model lightweighting. The experimental results demonstrated that the proposed model achieves a recognition accuracy of 98.8%, a recall rate of 98.8%, and a frame rate of 416.67 frames per second, while reducing computational volume by 25% compared to the YOLOv5 model. Furthermore, through case studies and comparisons, we illustrated the effectiveness and superiority of the proposed approach.
Development of a Simulation Model for a New Rotary Engine to Optimize Port Location and Operating Conditions Using GT-POWER
Authors: Kim Young-Jic , Park Young-Joon , Park Tae-Joon , Lee Chang-Eon
Published: September 2024

The objective of this study is to develop a 1D CFD simulation model to identify the optimal design parameters, using GT-POWER prior to the optimization of a new rotary engine derived from a three-lobe gerotor pump (GP3 RTE) based on 3D CFD simulation. The models were compared based on their respective development stages (steps 1–4) to ascertain the impact of each parameter on performance. The step 4 model, which exhibited a similar trend to that observed in the 3D CFD results, was selected for further analysis and validation. The developed model accurately predicted GP3 RTE performance in terms of fuel consumption, indicated power, efficiency, and exhaust gas reticulation (EGR) behavior, approaching the accuracy of the CONVERGE model. Furthermore, the optimal intake/exhaust port locations and operating conditions of the GP3 RTE were derived using the developed step 4 model. The model provided a convenient and powerful tool for obtaining basic information regarding the unique behavior of the GP3 RTE, thereby enabling the optimization of the design parameters without the necessity for time-consuming three-dimensional design modifications.
Quantifying Agricultural Residues Biomass Resources and the Energy Potentials with Characterization of Their Nature and Ethiopian Case Consumption Inference
Authors: Tesfay Angesom Gebrezgabiher , Tesfay Asfafaw Haileselassie , Adaramola Muyiwa Samuel
Published: September 2024

As the Ethiopian energy demand urges for fuel options, it is essential to identify biomass fuels and estimate their energy potential. This study quantified the agricultural residues’ biomass resources and their energy potential. Further analyzed and characterized the potential nature through quantitative and qualitative methodologies with descriptive, comparative, explanatory, and exploratory studies. Five-year crop yield data of 27 crops were collected from the Central Statistical Agency of Ethiopia. Conversion factors into energy were surveyed from the literature. Subsequently, the residues available and their energy potentials were estimated. Mathematical and statistical analysis methods were considered in an Excel sheet. A new measure of natural potential capacity for energy was defined in two views (resource and application). Accordingly, their potential capacities were rated and prioritized comparatively. The gross energy potential of all the residues was estimated to be 494.7 PJ. With 30% collecting efficiency, it corresponds to the imported petroleum fuel in 2018. Five major crops contributed to 80% of this gross potential. Maize and sorghum presented the highest potential due to their superior yields and good natural potential capacities. They are also well distributed in all the regions. Cotton and maize’s natural potential capacities are the best in both views. Generally, commercial crops presented better capacities than the major cereal crops. However, major crops’ energy potentials dominated due to their yields. These resources need mobilization into modern and commercially accessible fuel forms that await intervention. Densified and carbonized forms of consumption in nearby industries and households are most viable for the Ethiopian case.
A Comprehensive Review of Energy-Efficient Techniques for UAV-Assisted Industrial Wireless Networks
Authors: Zhang Yijia , Zhao Ruotong , Mishra Deepak , Ng Derrick Wing Kwan
Published: September 2024

The rapid expansion of the Industrial Internet-of-Things (IIoT) has spurred significant research interest due to the growth of security-aware, vehicular, and time-sensitive applications. Unmanned aerial vehicles (UAVs) are widely deployed within wireless communication systems to establish rapid and reliable links between users and devices, attributed to their high flexibility and maneuverability. Leveraging UAVs provides a promising solution to enhance communication system performance and effectiveness while overcoming the unprecedented challenges of stringent spectrum limitations and demanding data traffic. However, due to the dramatic increase in the number of vehicles and devices in the industrial wireless networks and limitations on UAVs’ battery storage and computing resources, the adoption of energy-efficient techniques is essential to ensure sustainable system implementation and to prolong the lifetime of the network. This paper provides a comprehensive review of various disruptive methodologies for addressing energy-efficient issues in UAV-assisted industrial wireless networks. We begin by introducing the background of recent research areas from different aspects, including security-enhanced industrial networks, industrial vehicular networks, machine learning for industrial communications, and time-sensitive networks. Our review identifies key challenges from an energy efficiency perspective and evaluates relevant techniques, including resource allocation, UAV trajectory design and wireless power transfer (WPT), across various applications and scenarios. This paper thoroughly discusses the features, strengths, weaknesses, and potential of existing works. Finally, we highlight open research issues and gaps and present promising potential directions for future investigation.
A Transient Damping Improvement Strategy for Enhancing Grid-Connected Active Power Response Performance of VSG
Authors: Xia Yan , Chen Yang , Chen Renzhao , Li Ke , Li Huizhu , Shi Jinhui , Yang Yiqiang
Published: September 2024

The given power and grid frequency disturbances can cause transient oscillations and steady-state deviations in the output power of a virtual synchronous generator (VSG), which can be effectively addressed by adding transient damping. However, this approach may result in significant power overshoot. This article proposes an improved VSG control strategy based on transient electromagnetic power feedback compensation and a small-signal model reduction scheme. Firstly, the grid-connected active closed-loop small-signal models of typical VSG control and transient damping VSG control are established, respectively. The transient oscillation suppression mechanism of active power is revealed through root locus and frequency response analyses, and the power overshoot characteristics of the two control strategies are analysed by combining them with the system of zero points. Secondly, the active transient feedback compensation method and the small-signal model reduction design method are introduced in detail. Finally, comparative analysis experiments are conducted using the Matlab/Simulink and hardware-in-the-loop experimental platform. It is verified that the proposed control strategy can suppress transient oscillations in active power, prevent steady-state deviations, and effectively mitigate the power overshoot problem of the system.
Effect of Ferrite Core Modification on Electromagnetic Force Considering Spatial Harmonics in an Induction Cooktop
Authors: Lee Sangjin , Yun Gyeonghwan , Lukman Grace Firsta , Kim Jang-Mok , Kim Tae-Hoon , Lee Cheewoo
Published: September 2024

This study investigates the influence of ferrite shape modifications on the performance and noise characteristics of an induction cooktop. The goal is to optimize the air gap dimensions between ferrites and cookware, enhancing efficiency while managing noise levels. Using finite element method (FEM) simulations, we analyze the spatial distribution of magnetic forces and their harmonics. Eight ferrite shape models were examined, focusing on both outer and inner air gaps. Model #8 (reduced outer air gap) and Model #9 (reduced inner air gap) were experimentally validated. Noise measurements indicated that Model #8 reduced 120 Hz harmonic noise components, while Model #9 increased them due to enhanced excitation forces. Current measurements confirmed that Model #9 achieved higher efficiency, with RMS current reduced to 94.54% of the base model. The study reveals a trade-off between performance and noise: inner air gap reduction significantly boosts efficiency but raises noise levels, whereas outer air gap reduction offers balanced improvements. These findings provide insights for optimizing induction cooktop designs, aiming for quieter operation without compromising efficiency.
Environmental and Social Life Cycle Assessment of Data Centre Heat Recovery Technologies Combined with Fuel Cells for Energy Generation
Authors: Puentes Bejarano Camila Andrea , Pérez Rodríguez Javier , de Andrés Almeida Juan Manuel , Hidalgo-Carvajal David , Gustaffson Jonas , Summers Jon , Abánades Alberto
Published: September 2024

The energy sector is essential in the transition to a more sustainable future, and renewable energies will play a key role in achieving this. It is also a sector in which the circular economy presents an opportunity for the utilisation of other resources and residual energy flows. This study examines the environmental and social performance of innovative energy technologies (which contribute to the circularity of resources) implemented in a demonstrator site in Luleå (Sweden). The demo-site collected excess heat from a data centre to cogenerate energy, combining the waste heat with fuel cells that use biogas derived from waste, meeting part of its electrical demand and supplying thermal energy to an existing district heating network. Following a cradle-to-gate approach, an environmental and a social life cycle assessment were developed to compare two scenarios: a baseline scenario reflecting current energy supply methods and the WEDISTRICT scenario, which considers the application of different renewable and circular technologies. The findings indicate that transitioning to renewable energy sources significantly reduces environmental impacts in seven of the eight assessed impact categories. Specifically, the study showed a 48% reduction in climate change impact per kWh generated. Additionally, the WEDISTRICT scenario, accounting for avoided burdens, prevented 0.21 kg CO2 eq per kWh auto-consumed. From the social perspective, the WEDISTRICT scenario demonstrated improvement in employment conditions within the worker and local community categories, product satisfaction within the society category, and fair competition within the value chain category. Projects like WEDISTRICT demonstrate the circularity options of the energy sector, the utilisation of resources and residual energy flows, and that these lead to environmental and social improvements throughout the entire life cycle, not just during the operation phase.