bluuebunny/crossref_metadata_2025 huggingface dataset:
Selection of Robotic Machining Parameters with Pneumatic Feed Force Progression
Authors: Burghardt Andrzej , Muszyńska Magdalena , Gierlak Piotr , Kurc Krzysztof , Szybicki Dariusz , Ornat Artur , Uliasz Marek
Published: October 2022

The subject matter of the above paper presents part of the research carried out as part of the robotization of the manufacturing processes of aircraft engine components. The paper concerns robotic deburring of the V2500 diffuser’s sharp edges. The diffuser is a precision casting characterized by a slight variation in the geometry of the workpiece depending on the accuracy of the casting molds and the phenomenon of shrinkage during solidification. Due to the small degree of deburring and thus low cutting forces, robotic machining with the FDB150 tool was used. This tool is characterized by compliance with adjustable force, which enables machining of workpieces with a randomly changing shape. The authors of the paper propose a procedure for carrying out work allowing for the selection of suboptimal process parameters. In the analyzed case, these parameters are the speed of movement of the characteristic point of the tool (TCP) and the tool/workpiece contact force. The proposed procedure for determining the parameters of the force and speed of movement allowed for indicating a set of parameters ensuring the performance of the product in accordance with the requirements defined in the documentation. The proposed solution is of an engineering nature and is not a classic search for the extreme of a function from the point of view of the adopted criteria (quality indicators). Its advantage is simplicity, which is very important from the point of view of an industrial application.
A Lightweight Sensitive Triboelectric Nanogenerator Sensor for Monitoring Loop Drive Technology in Table Tennis Training
Authors: Zhang Jiayun , Xu Qiushuang , Gan Yuyang , Sun Fengxin , Sun Zhe
Published: October 2022

As the Internet of Things becomes more and more mainstream, sensors are widely used in the field of motion monitoring. In this paper, we propose a lightweight and sensitive triboelectric nanogenerator (LS-TENG) consisting of transparent polytetrafluoroethylene (PTFE) and polyamide (PA) films as triboelectric layers, polydimethylsiloxane (PDMS) as support layer, and copper foil as electrode. LS-TENG can be attached to the joints of the human body, and the mechanical energy generated by human motion is converted into electric energy based on the triboelectric effect, thus realizing self-power supply. LS-TENG can monitor the angle changes in elbow and wrist joints when athletes pull the loop and actively generate the output voltage as a sensing signal, which is convenient for coaches to monitor the quality of athletes’ hitting in real time. In addition, LS-TENG can also be used as a power supply for other wireless electronic devices, which facilitates the construction and transmission of large motion data and opens up a new development direction for the field of motion monitoring.
Collective Intelligence in Self-Organized Industrial Cyber-Physical Systems
Authors: Leitão Paulo , Queiroz Jonas , Sakurada Lucas
Published: October 2022

Cyber-physical systems (CPS) play an important role in the implementation of new Industry 4.0 solutions, acting as the backbone infrastructure to host distributed intelligence capabilities and promote the collective intelligence that emerges from the interactions among individuals. This collective intelligence concept provides an alternative way to design complex systems with several benefits, such as modularity, flexibility, robustness, and reconfigurability to condition changes, but it also presents several challenges to be managed (e.g., non-linearity, self-organization, and myopia). With this in mind, this paper discusses the factors that characterize collective intelligence, particularly that associated with industrial CPS, analyzing the enabling concepts, technologies, and application sectors, and providing an illustrative example of its application in an automotive assembly line. The main contribution of the paper focuses on a comprehensive review and analysis of the main aspects, challenges, and research opportunities to be considered for implementing collective intelligence in industrial CPS. The identified challenges are clustered according to five different categories, namely decentralization, emergency, intelligent machines and products, infrastructures and methods, and human integration and ethics. Although the research indicates some potential benefits of using collective intelligence to achieve the desired levels of autonomy and dynamic adaptation of industrial CPS, such approaches are still in the early stages, with perspectives to increase in the coming years. Based on that, they need to be further developed considering some main aspects, for example, related to balancing the distribution of intelligence by the vertical and horizontal dimensions and controlling the nervousness in self-organized systems.
An Information Retrieval-Based Joint System for Complex Chinese Knowledge Graph Question Answering
Authors: Xiao Yuliang , Zhang Lijuan , Huang Jie , Zhang Lei , Wan Jian
Published: October 2022

Knowledge graph-based question answering is an intelligent approach to deducing the answer to a natural language question from structured knowledge graph information. As one of the mainstream knowledge graph-based question answering approaches, information retrieval-based methods infer the correct answer by constructing and ranking candidate paths, which achieve excellent performance in simple questions but struggle to handle complex questions due to rich entity information and diverse relations. In this paper, we construct a joint system with three subsystems based on the information retrieval methods, where candidate paths can be efficiently generated and ranked, and a new text-matching method is introduced to capture the semantic correlation between questions and candidate paths. Results of the experiment conducted on the China Conference on Knowledge Graph and Semantic Computing 2019 Chinese Knowledge Base Question Answering dataset verify the superiority and efficiency of our approach.
Automated Defect Analysis System for Industrial Computerized Tomography Images of Solid Rocket Motor Grains Based on YOLO-V4 Model
Authors: Dai Junjie , Li Tianpeng , Xuan Zhaolong , Feng Zirui
Published: October 2022

As industrial computerized tomography (ICT) is widely used in the non-destructive testing of a solid rocket motor (SRM), the problem of how to automatically discriminate defect types and measure defect sizes with high accuracy in ICT images of SRM grains needs to be urgently solved. To address the problems of low manual recognition efficiency and data utilization in the ICT image analysis of SRM grains, we proposed an automated defect analysis (ADA) system for ICT images of SRM grains based on the YOLO-V4 model. Using the region proposal of the YOLO-V4 model, a region growing algorithm with automatic selection of seed points was proposed to segment the defect areas of the ICT images of grains. Defect sizes were automatically measured based on the automatic determination of defect types by the YOLO-V4 model. In this paper, the image recognition performance of YOLO-V4, YOLO-V3, and Faster R-CNN models were compared. The results show that the average accuracy (mAP) of the YOLO-V4 model is more than 15% higher than that of the YOLO-V3 and Faster R-CNN models, the F1-score is 0.970, and the detection time per image is 0.152 s. The ADA system can measure defect sizes with an error of less than 10%. Tests show that the system proposed in this paper can automatically analyze the defects in ICT images of SRM grains and has certain application value.
Application of a 3D Scanner in Robotic Measurement of Aviation Components
Authors: Kurc Krzysztof , Burghardt Andrzej , Gierlak Piotr , Muszyńska Magdalena , Szybicki Dariusz , Ornat Artur , Uliasz Marek
Published: October 2022

The aviation industry is associated with high precision and accuracy standards of the manufactured components, and thus the need to ensure precise quality control. Measurement processes, depending on the manufactured components, take place before, during and after the processing stage. Optical scanners can be used for these measurements, the measurement results of which can be displayed on the operator panel or used to prepare a report. The innovative approach is to measure, compare the results with a pattern, send the deviations to a neural decision-making system, select the forces and send the results to a robot controller for adaptive machining. The presented proprietary solution includes a data acquisition system, a neural decision-making system and a robot that carries out the machining process via force control. The proposed solution was verified on aviation components. During the process parameter optimization stage for the diffuser and ADT gearbox, the points describing the change in width of the chamfer being performed and the blade thickness in the control sections were approximated.
Complementing JavaScript in High-Performance Node.js and Web Applications with Rust and WebAssembly
Authors: Kyriakou Kyriakos-Ioannis D. , Tselikas Nikolaos D.
Published: October 2022

We examine whether the novel systems programming language named Rust can be utilized alongside JavaScript in Node.js and Web-based applications development. The paper describes how JavaScript can be used as a high-level scripting language in combination with Rust in place of C++ in order to realize efficiency and be free of race conditions as well as memory-related software issues. Furthermore, we conducted stress tests in order to evaluate the performance of the proposed architecture in various scenarios. Rust-based implementations were able to outperform JS by 1.15 by over 115 times across the range of measurements and overpower Node.js’s concurrency model by 14.5 times or more without the need for fine-tuning. In Web browsers, the single-thread WebAssembly implementation outperformed the respective pure JS implementation by about two to four times. WebAssembly executed inside of Chromium compared to the equivalent Node.js implementations was able to deliver 93.5% the performance of the single-threaded implementation and 67.86% the performance of the multi-threaded implementation, which translates to 1.87 to over 24 times greater performance than the equivalent manually optimized pure JS implementation. Our findings provide substantial evidence that Rust is capable of providing the low-level features needed for non-blocking operations and hardware access while maintaining high-level similarities to JavaScript, aiding productivity.
Intelligent Control of Irrigation Systems Using Fuzzy Logic Controller
Authors: Singh Arunesh Kumar , Tariq Tabish , Ahmer Mohammad F. , Sharma Gulshan , Bokoro Pitshou N. , Shongwe Thokozani
Published: October 2022

In this paper, we explain the design and implementation of an intelligent irrigation control system based on fuzzy logic for the automatic control of water pumps used in farms and greenhouses. This system enables its user to save water and electricity and prevent over-watering and under-watering of the crop by taking into account the climatic parameters and soil moisture. The irrigation system works without human intervention. The climate sensors are packaged using electronic circuits, and the whole is interfaced with an Arduino and a Simulink model. These sensors provide information that is used by the Simulink model to control the water pump speed; the speed of the water pump is controlled to increase or decrease the amount of water that needs to be pushed by the pump. The Simulink model contains the fuzzy control logic that manages the data read by the Arduino through sensors and sends the command to change the pump speed to the Arduino by considering all the sensor data. The need for human intervention is eliminated by using this system and a more successful crop is produced by supplying the right amount of water to the crop when it is needed. The water supply is stopped when a sufficient amount of moisture is present in the soil and it is started as soon as the soil moisture levels drops below certain levels, depending upon the environmental factors.
Cascade-Free Modulated Predictive Direct Speed Control of PMSM Drives
Authors: Zheng Changming , Yang Jiafeng , Gong Zheng , Xiao Ziyu , Dong Xuanxuan
Published: October 2022

Conventional predictive control for permanent magnet synchronous motors (PMSMs) contains dual speed and current loops, and has a complex structure and multiple parameters to be tuned. Conventional predictive direct speed control (PDSC) exhibits an unsatisfactory steady-state performance. To tackle these issues, this paper presents a cascade-free modulated PDSC (MPDSC) scheme for PMSM drives. First, a speed predictive model is built, where a second-order sliding mode observer is employed to quickly and robustly estimate the load torque. Then, a dual objective cost function with speed and stator current tracking is designed, which improves the system’s steady-state performance. Furthermore, the analytical solution of the constrained optimal voltage vector is derived and it is synthesized by space vector modulation, resulting in a fixed switching frequency. Experimental results show that the proposed MPDSC has stronger robustness, and lower torque ripples and stator current harmonics compared to conventional PDSC.
Investigation of Performance and Emission Characteristics of CI Engine Using Diesel and Waste Cooking Oil Blends
Authors: Solangi Faheem Ahmed , Memon Liaquat Ali , Samo Saleem Raza , Luhur Muhammad Ramzan , Bhutto Aqeel Ahmed , Ansari Ali Murtaza
Published: October 2022

Reusing waste cooking oil (WCO) as fuel in compression ignition (CI) engine offers a sustainable solution for energy scarcity and environmental protection. WCO and n-pentanol ternary blends deliver are attractive prospects in utilization as bio-components and recycled components to moderately substitute diesel fuel. The current study intends to investigate the performance and emission characteristics of a single cylinder CI engine, having constant load at a uniform speed of 1300 rpm, using diesel-waste cooking oil n-pentanol blends. Blends chosen and analogized with diesel oil as reference fuel and their contents were the following: (1) D95-WCO5 (95%vol. diesel, WCO5%vol. waste cooking oil, (2) D65-WCO20-Pe15 (65%vol. diesel, 20%vol. waste cooking oil, and 15%vol. n-pentanol) and (3) D60-WCO20-Pe20 (60%vol. diesel, 20%vol. waste cooking oil and 20%vol. n-pentanol). The experimental results revealed that with the DF95-WCO5 blend the BSFC improved by 0.32%. However, with the addition of n-pentanol as a ternary blend; DF65-WCO20-Pe15 and DF60-WCO20-Pe20 resulted in improvements of 0.49% and 0.68% respectively. The BTE for DF95-WCO5 increased by 38.7%, while the increase was 39.2% for DF65-WCO20-Pe15 and 39.6% for DF60-WCO20-Pe20, which was less, as compared with diesel fuel. The lowermost level of CO discharge was achieved when the engine was fueled with DF65-WCO20-Pe15 and DF60-WCO20-Pe20, due to the highest level of saturation. CO2, in the cases of DF65-WCO20-Pe15 and DF60-WCO20-Pe20, increased, as compared to diesel fuel under the same engine operating conditions. However, the binary blend DF95-WCO5 resulted in decreased CO2 as analogized to diesel, because of incomplete combustion of the fuel. During experimental work it could be observed that the DF95-WCO5 binary blend produced higher Particulate material (PM-1, PM-2.5, PM-7 and PM-10) emissions, compared to DF100. Moreover, with the addition of n-pentanol as a ternary blend in the ratio of 15 to 20%, emission was further reduced. This indicated that direct exertion of WCO in engines must be promoted, as it is an impressive choice for waste recapture.
Heat Transfer Enhancement Methods Applied in Energy Conversion, Storage and Propulsion Systems
Authors: Xi Wenxiong , Xu Mengyao , Ma Kai , Liu Jian
Published: October 2022

With the development of energy storage and conversion or advanced propulsion systems, heat transfer enhancement methods have become widely applied [...]
An Improved Frequency-Adaptive Virtual Variable Sampling-Based Repetitive Control for an Active Power Filter
Authors: Liu Dong , Li Baojin , Huang Songtao , Liu Linguo , Wang Haozhe , Huang Yukai
Published: October 2022

To eliminate the harmonics caused by nonlinear loads, repetitive controllers are widely applied as current controllers for active power filters (APF). In practice, a variation in grid frequency leads to the appearance of a fractional-order delay filter. As a result, the resonant frequency of the repetitive controller will deviate from the fundamental frequency and the controller cannot compensate for harmonics accurately. To solve this problem, an improved frequency-adaptive repetitive controller based on virtual variable sampling (IMFA-VVS-RC) for APF is proposed in this paper. To enhance the system stability margin, the proposed RC introduces an infinite impulse response (IIR) low-pass filter. The proposed RC has a high stability margin at high frequencies due to the low gain of the IIR low-pass filter in the region above the cutoff frequency. In this way, the influence of model uncertainty and parameter uncertainty on system stability are reduced at high frequencies. At the same time, compared with the conventional repetitive controller (CRC), the proposed RC for APF has a better harmonic suppression ability when the frequency varies. Experiments have verified the effectiveness of the scheme adopted for APF.
Research on the Relationship between Sediment Concentration and Centrifugal Pump Performance Parameters Based on CFD Mixture Model
Authors: Wu Xinhao , Su Peilan , Wu Jianhua , Zhang Yusheng , Wang Baohe
Published: October 2022

To study the relationship between sediment concentration and the performance parameters of centrifugal pumps, Jiamakou water supply pumping station with total installed capacity of 30,880 kW was selected to analyze characteristics of the centrifugal pump in this paper. Based on a CFD mixture model, the effects of different sediment concentrations on the movement of solid–liquid two-phase flow and the performance parameters of the centrifugal pump were obtained. Then, fitting equations were established between performance parameters (head, flow rate, shaft power, and efficiency) of the centrifugal pump and sediment concentration at three working conditions (0.8 Q = 2 m3/s, Q = 2.5 m3/s, 1.2 Q = 3 m3/s) by the polynomial least-square method. Calculated values of fitting equations were compared with the measured values in centrifugal pump operation. The results show that, as the sediment concentration increases from 0.1% to 1%, the maximum volume fraction of sediment at blade outlet increased from 0.14% to 1.14%, and the maximum volume fraction of sediment at blade outlet increased from 0.7% to 2.29%. The turbulent kinetic energy inside the centrifugal pump increased from 8.74 m2/s2 to 10.78 m2/s2. The calculated values of fitting equation are in good agreement with the measured values in centrifugal pump operation, and the maximum errors of head, flow rate, and efficiency are 6.48%, 3.54%, and 2.87%, respectively. Therefore, the reliability of the fitting equations is verified. The research method can provide a reference for the calculation of performance parameters for centrifugal pumps in other water supply pumping stations with sediment-laden flow.
Impact of Changes in Limit Values of Electric and Magnetic Field on Personnel Performing Diagnostics of Transformers
Authors: Nadolny Zbigniew
Published: October 2022

Electric and magnetic fields accompany technical personnel in their working environment (work exposure). That is why many countries have the appropriate regulations. The impact of electric and magnetic fields on humans is still not fully recognized. This is the reason why the limit values of its intensity in many countries differs significantly. The article presents changes in the stress limits of the electric and magnetic fields in Poland at the turn of the last dozen years. The last such change was the result of a Directive of the European Union (2013/35/EU). The effects of changes in limit values on the working conditions of technical personnel performing diagnostics of high voltage transformers or working in the immediate vicinity of such transformers are presented. The article shows that recent changes have improved the working conditions of technical personnel in relation to the electric field and worsened the conditions taking into account the magnetic field.
Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends
Authors: Kim Dongsu , Lee Jongman , Do Sunglok , Mago Pedro J. , Lee Kwang Ho , Cho Heejin
Published: October 2022

Buildings use up to 40% of the global primary energy and 30% of global greenhouse gas emissions, which may significantly impact climate change. Heating, ventilation, and air-conditioning (HVAC) systems are among the most significant contributors to global primary energy consumption and carbon gas emissions. Furthermore, HVAC energy demand is expected to rise in the future. Therefore, advancements in HVAC systems’ performance and design would be critical for mitigating worldwide energy and environmental concerns. To make such advancements, energy modeling and model predictive control (MPC) play an imperative role in designing and operating HVAC systems effectively. Building energy simulations and analysis techniques effectively implement HVAC control schemes in the building system design and operation phases, and thus provide quantitative insights into the behaviors of the HVAC energy flow for architects and engineers. Extensive research and advanced HVAC modeling/control techniques have emerged to provide better solutions in response to the issues. This study reviews building energy modeling techniques and state-of-the-art updates of MPC in HVAC applications based on the most recent research articles (e.g., from MDPI’s and Elsevier’s databases). For the review process, the investigation of relevant keywords and context-based collected data is first carried out to overview their frequency and distribution comprehensively. Then, this review study narrows the topic selection and search scopes to focus on relevant research papers and extract relevant information and outcomes. Finally, a systematic review approach is adopted based on the collected review and research papers to overview the advancements in building system modeling and MPC technologies. This study reveals that advanced building energy modeling is crucial in implementing the MPC-based control and operation design to reduce building energy consumption and cost. This paper presents the details of major modeling techniques, including white-box, grey-box, and black-box modeling approaches. This paper also provides future insights into the advanced HVAC control and operation design for researchers in relevant research and practical fields.
Numerical Simulation of Embedded Discrete-Fracture Seepage in Deep Carbonate Gas Reservoir
Authors: Gong Yufeng , Zhai Shuo , Zha Yuqiang , Xu Tonghao , Liu Shu , Kang Bo , Zhang Bolin
Published: October 2022

Existing fractured gas reservoir development techniques are mainly based on dual medium numerical-simulation models, which can, to a certain extent, effectively simulate natural fractures with high fracture density; however, these models have some limitations, particularly in terms of simulating the fracture morphology and distribution. Considering carbonate gas reservoirs with complex fractures, in this paper, we establish a numerical-simulation model of embedded discrete-fracture seepage in horizontal wells of carbonate gas reservoirs, in order to compare and study the development effect of carbonate gas reservoirs under different horizontal well fracture parameters. The fracture distribution and structure in carbonate gas reservoirs is obtained using an ant-tracking approach based on 3D seismic bodies, and a numerical-simulation model based on the embedded discrete-fractures model is solved using the open-source program MRST. We considered the following parameters: half fracture length, fracture permeability, and horizontal segment length. By changing the fracture parameters of horizontal wells and comparing the gas-production trends, technical optimization in gas reservoir development can be realized. The results show that the embedded discrete-fracture model can effectively solve the difficult problem of characterizing fluid seepage in fractures of different scale in carbonate gas reservoirs. Although gas production increases with increasing fracture length, fracture conductivity, horizontal section length, and natural fracture conductivity, the contributions of these parameters to gas well production capacity are greatly influenced by the natural fractures.
The Effect of Geometry on the Yield of Fresh Water from Single Slope Solar Stills
Authors: Serradj Djamal Eddine Benhadji , Anderson Timothy , Nates Roy
Published: October 2022

When examining the research literature relating to single slope solar stills it is apparent that much of the work has been conducted in an ad-hoc manner. This has led to several recommendations relating to the geometry of stills having developed that do not appear to have a sufficient evidentiary basis. To address this issue, this study used computational fluid dynamics simulations to examine the natural convection in single slope solar stills with cover angles between 0° and 60° and aspect ratios ranging from 1 to 8, with the results validated experimentally. Treating cover angle and aspect ratio as independent design variables showed that there are some features of the natural convection flow that account for the variation in yields reported in the literature (transitions between uni- and multi-cellular flow). More specifically, it was shown that the geometric effects could be correlated in the form of a generalized relationship and that this was able to predict the yield from several independent solar stills reported in the literature. As such, the use of the relationship will allow single slope solar stills designers to predict their yield far more accurately than is currently possible.
Effect of Temporal Variation in Chemical Composition on Methane Yields of Rendering Plant Wastewater
Authors: Conde Erika , Kaparaju Prasad
Published: October 2022

The effect of temporal variation in chemical composition on methane yields of rendering plant wastewater was studied in batch experiments at 37 °C. In total, 14 grab samples were collected from Monday through Friday (Day 1 to 5) from a rendering plant located in Queensland, Australia. Each day, three samples were collected: early morning (S1), midday (S2) and afternoon (S3). Chemical analyses showed that a significant different in total solids (TS), volatile solids (VS), and chemical oxygen demand (COD) was noticed among the samples. TS content ranged from 0.13% to 1.82% w/w, while VS content was between 0.11% and 1.44% w/w. Among the samples, S2 of Day 3 had the highest COD concentration (10.5 g/L) whilst S1 of Day 1 had the lowest COD (3.75 g/L) and total volatile fatty acid (VFA) concentration (149.1 mg/L). In all samples, acetic acid was the dominant VFA and accounted for more than 65–90% of total VFAs. Biochemical methane potential studies showed that the highest methane yield of 270.2 L CH4/kgCODadded was obtained from S3 of Day 3. Whilst the lowest methane yield was noticed for S1 of Day 1 (83.7 L CH4/kgCODadded). Results from kinetic modelling showed the modified Grompetz model was best fit than the first order model and a large variation was noticed between the experimental and the modelled data. Time delay ranged from 2.51 to 3.84 d whilst hydrolysis constant values were close to 0.21 d−1. Thus, the study showed that chemical composition of incoming feed to a biogas plant varies throughout the week and is dependent on the chemical composition of organic materials received and the amount of steam used for rendering process.
Recent Advances in Fuel Additives and Their Spray Characteristics for Diesel-Based Blends
Authors: Haq Muteeb , Jafry Ali , Ahmad Saad , Cheema Taqi , Ansari Munib , Abbas Naseem
Published: October 2022

The spray characteristics play a crucial role in determining the performance and emissions of compression ignition (CI) engines at the pre-combustion stage. With the advent of many types of alternative fuels and their blends with diesel, it is necessary to investigate the effect of fuel properties and various injection conditions to determine the penetration length, spray cone angle and spray area for a viable fuel with similar or better dispersion characteristics to diesel. Hence, this study reviews and summarizes the spray visualization techniques, along with in depth analysis of macroscopic spray properties of various fuel blends with diesel. It was found that higher injection pressures typically led to higher penetration lengths, better atomization with reduced Sauter mean diameter. Liquid properties such as viscosity, surface tension, and evaporation as well as structural properties play a crucial role in spray formation in fuel blends with various types of alcohols, ethers, biodiesel, aliphatic, aromatic, as well as nanoparticle additives. This review compares these fuel additives and their types to present a comparative study with diesel to determine the ideal conditions with minimal changes to the engine for replacing diesel with a sustainable fuel consisting of better combustion efficiency due to its enhanced spray characteristics.
A Multiple Time Scales Rolling Coordinative Dispatching Method for an Island Microgrid with High Proportion Tidal Current Energy Access and Demand Response Resources
Authors: Ouyang Yani , Zhao Wei , Wang Haifeng , Wang Wenyong
Published: October 2022

Currently, the ocean energy strategy is rapidly developing, and a high proportionate tidal current energy grid connection presents significant obstacles to the planning and secure and stable operation of an island microgrid. For an island microgrid with high proportion tidal current energy access and demand response resources, this research suggests a multiple time scales rolling coordinative dispatching method. An MPPT control based on Q-Learning algorithm is first developed for real-time maximum power tracking of tidal current energy generation after the island microgrid’s topology has been examined. Following that, a multiple time scales rolling coordinative dispatching’s fundamental architecture and implementation method are provided, with equal time intervals coordinated in a step-by-step recursive way. In the example analysis of an island microgrid, we consider the rigid demand load that does not participate in the demand side response, and the ship load and controllable load that participate in the demand side response. On sea islands, ship loads on the long timeframe achieve traffic and energy interaction, and dispatchable loads on the short timescale participate in supply and demand balancing. This is due to the multiple time scales properties of demand response resources. In addition, a multiple time scales rolling coordinative dispatching model for an island microgrid is developed. It includes day-ahead, intraday, and real-time components. Finally, example analysis is used to confirm the dispatching method’s usefulness and advancement, and we conclude that the tidal current energy consumption rate of the island microgrid is increased by 17.08%.
Thermodynamic Performance of Geothermal Energy Cascade Utilization for Combined Heating and Power Based on Organic Rankine Cycle and Vapor Compression Cycle
Authors: Li Tailu , Li Xuelong , Gao Haiyang , Gao Xiang , Meng Nan
Published: October 2022

A large population and rapid urbanization dramatically promote the heating supply demand, the combined heating and power (CHP) system for energy cascade utilization came into being. However, the research on the recovery and utilization of condensing heat, the exploration of the coupling law between power generation and heating supply, and the influence of heat source parameters on thermo-economic performance are still insufficient. To this end, two combined heating and power (CHP) systems coupled with an organic Rankine cycle (ORC) and vapor compression cycle (VCC) are proposed, and their thermodynamic and economic performances are optimized and analyzed by the laws of thermodynamics. Results show that the increase of the volume flow will increase the power generation and heating supply quantity of the system, and there is an optimal evaporation temperature range of 130–140 °C to optimize the performance of the system. The increase of heat source temperature will improve the economic performance of the system, but it will reduce the exergetic efficiency. Therefore, two factors should be comprehensively considered in practical engineering. There is mutual exclusivity between the net power output of the system and the heating supply quantity, it should be reasonably allocated according to the actual needs of users in engineering applications. In addition, the exergetic efficiency of the two systems can reach more than 60%, and the energy utilization rate is high, which indicates that the cascade utilization mode is reasonable.
Pyrolysis and Gasification of Biomass and Waste
Authors: Czerski Grzegorz
Published: October 2022

The use of renewable solid fuels, including biomass, is of great importance in today’s society [...]
A Review on the Effects of Pretreatment and Process Parameters on Properties of Pellets
Authors: Li Wentao , Wang Mingfeng , Meng Fanbin , Zhang Yifei , Zhang Bo
Published: October 2022

The development and utilization of biomass can not only address the demand for low-carbon energy and reduce environmental pollution, but can also facilitate the achievement of carbon neutrality. However, there are many factors justifying the case for low utilization of agricultural residues. These factors could be well controlled by producing top-quality pellets. Production of pellets is generally accompanied by the problems of high energy consumption and serious mold wearing. To eliminate these deficiencies, pretreatment has attracted scholars’ attention. In this review, the effects of four pretreatments on the properties of pellets were assessed. Thermal pretreatment can improve the hydrophobicity of pellets, and optimize their properties, while degradation of diverse extractives is noteworthy. Hydrothermal pretreatment improves the physical properties of pellets, through the increase of polar functional groups on the surface of the biomass. Ultrasonic vibration-assisted (UV-A) pelleting produces pellets under low pressure without a heating process; however, it is still not applied to large-scale production. Supercritical fluid extraction can achieve the graded utilization of extracts and bioactive substances in biomass, and the residues can be subsequently utilized as pellet feedstock. Mild hydrothermal treatment is a promising approach to improving the quality of agricultural pellets. Additionally, the effects of process parameters on the physical and chemical properties of pellets should be systematically analyzed.
Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine
Authors: Menga Nicola , Mothakani Akhila , De Giorgi Maria Grazia , Przysowa Radoslaw , Ficarella Antonio
Published: October 2022

Micro turbojets are used for propelling radio-controlled aircraft, aerial targets, and personal air vehicles. When compared to full-scale engines, they are characterized by relatively low efficiency and durability. In this context, the degraded performance of gas path components could lead to an unacceptable reduction in the overall engine performance. In this work, a data-driven model based on a conventional artificial neural network (ANN) and an extreme learning machine (ELM) was used for estimating the performance degradation of the micro turbojet. The training datasets containing the performance data of the engine with degraded components were generated using the validated GSP model and the Monte Carlo approach. In particular, compressor and turbine performance degradation were simulated for three different flight regimes. It was confirmed that component degradation had a similar impact in flight than at sea level. Finally, the datasets were used in the training and testing process of the ELM algorithm with four different input vectors. Two vectors had an extensive number of virtual sensors, and the other two were reduced to just fuel flow and exhaust gas temperature. Even with the small number of sensors, the high prediction accuracy of ELM was maintained for takeoff and cruise but was slightly worse for variable flight conditions.
Advanced Energy Efficiency Systems in Buildings
Authors: Borge-Diez David
Published: October 2022

Energy efficiency is becoming one of the key research topics in the energy field and has, therefore, attracted extensive attention in recent research [...]
A Coal Seam Thickness Prediction Model Based on CPSAC and WOA–LS-SVM: A Case Study on the ZJ Mine in the Huainan Coalfield
Authors: Lin Xiaobo , Zhang Pingsong , Meng Fanbin , Liu Chang
Published: October 2022

The precise prediction of coal seam thickness in operating mines is crucial for the construction of transparent mines. Geological borehole data or a small amount of seismic information is frequently used in traditional coal seam thickness prediction methods; however, these methods have poor precision. In this study, we introduced a model for predicting coal seam thickness based on the comprehensive preference for seismic attribute combination (CPSAC) and the least squares support vector machine (LS-SVM) optimized by the whale optimization algorithm (WOA). We used the CPSAC to modify the mass disturbed data in the seismic attribute data to predict the coal seam thickness. To achieve this the sample size was reduced by optimizing the seismic attribute combinations, and the modified attribute data was entered into the LS-SVM., Furthermore, to create an accurate prediction model for coal thickness, we employed the WOA to determine the optimal penalty coefficient and kernel coefficient of the LS-SVM. An empirical case study was conducted in the northeast mining area of the ZJ mine in the Huainan coalfield. The coal thickness of two mining faces in this research area were estimated and compared, demonstrating the proposed method’s high prediction accuracy. The proposed method has guiding implications for developing an accurate mining geological model and facilitating the accurate use of coal resources.
Solar Thermochemical CO2 Splitting Integrated with Supercritical CO2 Cycle for Efficient Fuel and Power Generation
Authors: Yu Xiangjun , Lian Wenlei , Gao Ke , Jiang Zhixing , Tian Cheng , Sun Nan , Zheng Hangbin , Wang Xinrui , Song Chao , Liu Xianglei
Published: October 2022

Converting CO2 into fuels via solar-driven thermochemical cycles of metal oxides is promising to address global climate change and energy crisis challenges simultaneously. However, it suffers from low energy conversion efficiency (ηen) due to high sensible heat losses when swinging between reduction and oxidation cycles, and a single product of fuels can hardly meet multiple kinds of energy demands. Here, we propose an alternative way to upsurge energy conversion efficiency by integrating solar thermochemical CO2 splitting with a supercritical CO2 thermodynamic cycle. When gas phase heat recovery (εgg) is equal to 0.9, the highest energy conversion efficiency of 20.4% is obtained at the optimal cycle high pressure of 260 bar. In stark contrast, the highest energy conversion efficiency is only 9.8% for conventional solar thermochemical CO2 splitting without including a supercritical CO2 cycle. The superior performance is attributed to efficient harvesting of waste heat and synergy of CO2 splitting cycles with supercritical CO2 cycles. This work provides alternative routes for promoting the development and deployment of solar thermochemical CO2 splitting techniques.
Superconducting Electric Power Systems: R&D Advancements
Authors: Ferreira da Silva Francisco , Fernandes João F. P. , da Costa Branco Paulo José
Published: October 2022

According to the report “Global Energy Perspective” by McKinsey and Co in 2022, the energy transition will continue to gain momentum, with oil demand projected to peak possibly as soon as 2025 [...]
Reducing Volume to Increase Capacity—Measures to Reduce Transport Energy for Recyclable Waste Collection
Authors: Santos Adriano A. , da Silva António Ferreira , Gouveia António , Felgueiras Carlos , Caetano Nídia
Published: October 2022

The production of municipal waste is increasing all over the world. Although a significant part of the waste is collected as commingled waste, much of it is recyclable if disposed of properly. Thus, separate deposition and collection plays an extremely important role today, more than ever, not only in terms of preventing pollution but also from the point of view of recycling as a driver of circular economy and of efficient use of resources. This work is focused on the development of compaction equipment to be applied to containers, which allows a more efficient approach to the process of collecting waste for recycling. As a management option, recycling depends on collective behavior which is based on individual acts. Therefore, individual use of plastic/metal compaction systems can help meet recycling targets, even as a complement to conventional bins. Thus, herein a proposal is presented for a plastic/metal collection station with a built-in compaction element that allows for the compacting of the separated waste, individually, in an easily accessible drawer. Sorting and compacting waste before collection will result in a reduction of the number of collection/transport stops, which will also translate into higher energy efficiency, cost savings, optimization of the transported tons/km ratio, and profitability.
Ultra-Short-Term Load Dynamic Forecasting Method Considering Abnormal Data Reconstruction Based on Model Incremental Training
Authors: Chen Guangyu , Wu Yijie , Yang Li , Xu Ke , Lin Gang , Zhang Yangfei , Zhang Yuzhuo
Published: October 2022

In order to reduce the influence of abnormal data on load forecasting effects and further improve the training efficiency of forecasting models when adding new samples to historical data set, an ultra-short-term load dynamic forecasting method considering abnormal data reconstruction based on model incremental training is proposed in this paper. Firstly, aiming at the abnormal data in ultra-short-term load forecasting, a load abnormal data processing method based on isolation forests and conditional adversarial generative network (IF-CGAN) is proposed. The isolation forest algorithm is used to accurately eliminate the abnormal data points, and a conditional generative adversarial network (CGAN) is constructed to interpolate the abnormal points. The load-influencing factors are taken as the condition constraints of the CGAN, and the weighted loss function is introduced to improve the reconstruction accuracy of abnormal data. Secondly, aiming at the problem of low model training efficiency caused by the new samples in the historical data set, a model incremental training method based on a bidirectional long short-term memory network (Bi-LSTM) is proposed. The historical data are used to train the Bi-LSTM, and the transfer learning is introduced to process the incremental data set to realize the adaptive and rapid adjustment of the model weight and improve the model training efficiency. Finally, the real power grid load data of a region in eastern China are used for simulation analysis. The calculation results show that the proposed method can reconstruct the abnormal data more accurately and improve the accuracy and efficiency of ultra-short-term load forecasting.
Robust Control and Optimization Method for Single-Phase Grid-Connected Inverters Based on All-Pass-Filter Phase-Locked Loop in Weak Grid
Authors: Yang Longyue , Cao Tian , Chen Huapeng , Dong Xinwei , Zhang Shuyuan
Published: October 2022

In a distributed generation system, the all-pass-filter phase-locked loop (APF-PLL) is a commonly used method for grid synchronization. However, the coupling effect between APF-PLL and current control loop increases the risk of oscillation instability for the inverter in the weak grid. At present, there are few effective methods to solve the adverse effect of APF-PLL on the inverter-grid interconnection system in the weak grid. Therefore, a small-signal impedance model of the inverter considering the dual d-q frame brought by APF-PLL is first established. Then the reason for the inverter instability caused by APF-PLL in the weak grid is analyzed. Subsequently, an impedance reshaping method based on a modified first-order filter PLL with a complex coefficient filter (CCF-MFOF-PLL) and its parameter optimization design method are proposed. Finally, the experimental results verify that the proposed method widens the stable range of the inverter and ensures the stable operation of the inverter even with the large grid impedance.
Development and Validation of V2G Technology for Electric Vehicle Chargers Using Combo CCS Type 2 Connector Standards
Authors: Jaman Shahid , Verbrugge Boud , Garcia Oscar Hernandez , Abdel-Monem Mohamed , Oliver Blum , Geury Thomas , Hegazy Omar
Published: October 2022

Vehicle-to-Grid (V2G) technology is viewed as a viable solution to offer auxiliary power system services. Currently, V2G operation is only possible through DC chargers using the CHAdeMO connector with the necessary communication protocol. However, in Europe, for high-power DC charging (>50 kW), the Combined Charging Service (CCS) Type 2 is preferred over CHAdeMO. Therefore, this work presents the development of a V2G testing system with a Combo CCSType 2 charger including communication via the ISO 15118-2 protocol. The BOSCH passenger car with a 400 V battery pack is used to test and validate the technical feasibility of V2G charging via a Combo CCS Type 2 connector standard. The V2G feature is characterized in terms of efficiency, signal delay, response proportionality, magnitude accuracy and noise precision. A data driven V2G charger simulation model based on the real-time data is also developed in MATLAB/Simulink. The performance under various operating settings is presented in the outcomes, emphasizing the need for appropriate hardware calibration, and understanding while delivering standard-compliant grid control services using V2G technology. Finally, the results of the simulation model are compared with the real hardware results in terms of error, noise level and data magnitude accuracy.
Application of Headspace-SIFT-MS to Direct Analysis of Hazardous Volatiles in Drinking Water
Authors: Perkins Mark J. , Langford Vaughan S.
Published: October 2022

Harmful volatile organic compounds (VOCs) are present in drinking water due to anthropogenic activities, such as oil refining (e.g., benzene) and disinfection (e.g., the so-called trihalomethanes, THMs). Gas chromatography (GC)-based techniques are widely applied for analysis of these compounds in the laboratory but have some throughput drawbacks due to sample preparation and the extended analysis time (due to chromatographic separation). Selected ion flow tube mass spectrometry (SIFT-MS) is a direct-injection mass spectrometry (DIMS) technique that has potential to reduce sample preparation and analysis times through direct analysis of aqueous headspace with no preconcentration, drying, or other water management. This study applies headspace-SIFT-MS to the analysis of benzene, related petroleum aromatics, and THMs to evaluate the potential for enhanced sample throughput for drinking water analysis (10 samples per hour). Headspace-SIFT-MS achieved a limit of quantitation (LOQ) of 0.1 to 0.2 μg L−1 for benzene, toluene, ethylbenzene and xylenes (BTEX), and 1 to 2 μg L−1 for the THMs. These LOQs achieve the current European Union and United States regulatory limits but are higher than modern GC methods. Therefore, the potential application of SIFT-MS is envisaged to lie in rapid screening in the laboratory, or field-based real-time monitoring.