bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Adaptive PID Controller Design for a PEM Fuel Cell Using Stochastic Gradient Descent with Momentum Enhanced by Whale Optimizer
Authors: Silaa Mohammed Yousri , Barambones Oscar , Bencherif Aissa
Published: August 2022

This paper presents an adaptive PID using stochastic gradient descent with momentum (SGDM) for a proton exchange membrane fuel cell (PEMFC) power system. PEMFC is a nonlinear system that encounters external disturbances such as inlet gas pressures and temperature variations, for which an adaptive control law should be designed. The SGDM algorithm is employed to minimize the cost function and adapt the PID parameters according to the perturbation changes. The whale optimization algorithm (WOA) was chosen to enhance the adaptive rates in the offline mode. The proposed controller is compared with PID stochastic gradient descent (PIDSGD) and PID Ziegler Nichols tuning (PID-ZN). The control strategies’ robustnesses are tested under a variety of temperatures and loads. Unlike the PIDSGD and PID-ZN controllers, the PIDSGDM controller can attain the required control performance, such as fast convergence and high robustness. Simulation results using Matlab/Simulink have been studied and illustrate the effectiveness of the proposed controller.
Performance Analysis of Custom Dual-Finger 250 nm InP HBT Devices for Implementation of 255 GHz Amplifiers
Authors: Koh Yoon Kyeong , Kim Yang Woo , Kim Moonil
Published: August 2022

The performances of WR-3.4 monolithic amplifiers fabricated using dual-finger 6 µm InP HBT devices are investigated. While one amplifier uses the dual-finger devices formed by simply connecting two existing standard single-finger HBTs, the second amplifier uses newly formed devices that share a common collector metal on a single merged device isolation area. The amplifiers using two types of devices based on the identical matching networks are fabricated for on-wafer probing tests. The custom merged-device amplifier shows clear performance advantages over the separate-device amplifier, showing a peak gain of 10.5 dB and the maximum output power of 5.2 dBm at 255 GHz.
LSW-Net: A Learning Scattering Wavelet Network for Brain Tumor and Retinal Image Segmentation
Authors: Liu Ruihua , Nan Haoyu , Zou Yangyang , Xie Ting , Ye Zhiyong
Published: August 2022

Convolutional network models have been widely used in image segmentation. However, there are many types of boundary contour features in medical images which seriously affect the stability and accuracy of image segmentation models, such as the ambiguity of tumors, the variability of lesions, and the weak boundaries of fine blood vessels. In this paper, in order to solve these problems we first introduce the dual-tree complex wavelet scattering transform module, and then innovatively propose a learning scattering wavelet network model. In addition, a new improved active contour loss function is further constructed to deal with complex segmentation. Finally, the equilibrium coefficient of our model is discussed. Experiments on the BraTS2020 dataset show that the LSW-Net model has improved the Dice coefficient, accuracy, and sensitivity of the classic FCN, SegNet, and At-Unet models by at least 3.51%, 2.11%, and 0.46%, respectively. In addition, the LSW-Net model still has an advantage in the average measure of Dice coefficients compared with some advanced segmentation models. Experiments on the DRIVE dataset prove that our model outperforms the other 14 algorithms in both Dice coefficient and specificity measures. In particular, the sensitivity of our model provides a 3.39% improvement when compared with the Unet model, and the model’s effect is obvious.
A Granulation Tissue Detection Model to Track Chronic Wound Healing in DM Foot Ulcers
Authors: Lien Angela Shin-Yu , Lai Chen-Yao , Wei Jyh-Da , Yang Hui-Mei , Yeh Jiun-Ting , Tai Hao-Chih
Published: August 2022

Diabetes mellitus (DM) foot ulcer is a chronic wound and is highly related to the mortality and morbidity of infection, and might induce sepsis and foot amputation, especially during the isolation stage of the COVID-19 pandemic. Visual observation when changing dressings is the most common and traditional method of detecting wound healing. The formation of granulation tissues plays an important role in wound healing. In the complex pathophysiology of excess and unhealthy granulation induced by infection, oxygen supply may explain the wound healing process in DM patients with multiple complicated wounds. Thus, advanced and useful tools to observe the condition of wound healing are very important for DM patients with extremities ulcers. For this purpose, we developed an artificial intelligence (AI) detection model to identify the growth of granulation tissue of the wound bed. We recruited 100 patients to provide 219 images of wounds at different healing stages from 2 hospitals. This was performed to understand the wound images of inconsistent size, and to allow self-inspection on mobile devices, having limited computing resources. We segmented those images into 32 × 32 blocks and used a reduced ResNet-18 model to test them individually. Furthermore, we conducted a learning method of active learning to improve the efficiency of model training. Experimental results reveal that our model can identify the region of granulation tissue with an Intersection-over-Union (IOU) rate higher than 0.5 compared to the ground truth. Multiple cross-repetitive validations also confirm that the detection results of our model may serve as an auxiliary indicator for assessing the progress of wound healing. The preliminary findings may help to identify the granulation tissue of patients with DM foot ulcer, which may lead to better long-term home care during the COVID-19 pandemic. The current limit of our model is an IOU of about 0.6. If more actual data are available, the IOU is expected to improve. We can continue to use the currently established active learning process for subsequent training.
Intelligent Sensors and Environment Driven Biological Comfort Control Based Smart Energy Consumption System
Authors: Nawaz Muhammad Asim , Khan Bilal , Ali Sahibzada Muhammad , Awais Muhammad , Qureshi Muhammad Bilal , Jawad Muhammad , Mehmood Chaudhry Arshad , Ullah Zahid , Aslam Sheraz
Published: August 2022

The smart energy consumption of any household, maintaining the thermal comfort level of the occupant, is of great interest. Sensors and Internet-of-Things (IoT)-based intelligent hardware setups control the home appliances intelligently and ensure smart energy consumption, considering environment parameters. However, the effects of environment-driven consumer body dynamics on energy consumption, considering consumer comfort level, need to be addressed. Therefore, an Energy Management System (EMS) is modeled, designed, and analyzed with hybrid inputs, namely environmental perturbations, and consumer body biological shifts, such as blood flows in skin, fat, muscle, and core layers (affecting consumer comfort through blood-driven-sensations). In this regard, our work incorporates 69 Multi-Node (MN) Stolwijik’s consumer body interfaced with an indoor (room) electrical system capable of mutual interactions exchange from room environmental parameters and consumer body dynamics. The mutual energy transactions are controlled with classical PID and Adaptive Neuro-Fuzzy-Type II (NF-II) systems inside the room dimensions. Further, consumer comfort, room environment, and energy consumption relations with bidirectional control are demonstrated, analyzed, and tested in MATLAB/Simulink to reduce energy consumption and energy cost. Finally, six different cases are considered in simulation settings and for performance validation, one case is validated as real-time hardware experimentation.
Mean Phase Voltages and Duty Cycles Estimation of a Three-Phase Inverter in a Drive System Using Machine Learning Algorithms
Authors: Anđelić Nikola , Lorencin Ivan , Glučina Matko , Car Zlatan
Published: August 2022

To achieve an accurate, efficient, and high dynamic control performance of electric motor drives, precise phase voltage information is required. However, measuring the phase voltages of electrical motor drives online is expensive and potentially contains measurement errors, so they are estimated by inverter models. In this paper, the idea is to investigate if various machine learning (ML) algorithms could be used to estimate the mean phase voltages and duty cycles of the black-box inverter model and black-box inverter compensation scheme with high accuracy using a publicly available dataset. Initially, nine ML algorithms were trained and tested using default parameters. Then, the randomized hyper-parameter search was developed and implemented alongside a 5-fold cross-validation procedure on each ML algorithm to find the hyper-parameters that will achieve high estimation accuracy on both the training and testing part of a dataset. Based on obtained estimation accuracies, the eight ML algorithms from all nine were chosen and used to build the stacking ensemble. The best mean estimation accuracy values achieved with stacking ensemble in the black-box inverter model are R¯2=0.9998, MAE¯=1.03, and RMSE¯=1.54, and in the case of the black-box inverter compensation scheme R¯2=0.9991, MAE¯=0.0042, and RMSE¯=0.0063, respectively.
Tailored Blockchain Applications for the Natural Gas Industry: The Case Study of SOCAR
Authors: Zehir Cemal , Zehir Melike , Borodin Alex , Mamedov Zahid Farrukh , Qurbanov Sadiq
Published: August 2022

Blockchain technology has emerging areas of deployment in diverse sectors and use cases. In this study, several potential application areas of blockchain with promising benefits have been identified in the natural gas industry. There is no single solution that can address different challenges and meet disparate requirements. Therefore, it is important to understand the needs of the natural gas industry and propose appropriate blockchain solutions. Moreover, in the literature, there is a lack of detailed case studies involving industrial experts from the natural gas sector. Expert opinion can be useful for prioritizing the most needed or expected blockchain application areas among several options. By considering privacy, authentication, speed, security, energy consumption, and costs, suitable blockchain types and consensus mechanisms can be determined. This study presents one of the first detailed case studies for tailored applications of blockchain in the natural gas industry. Through a two-staged semi-structured interview with executives from SOCAR Azerbaijan, the most important blockchain application areas and operational requirements were identified. Furthermore, the most suitable blockchain solutions that can address application-specific conditions and needs were determined. This study both, develops a replicable and reliable methodology to conduct detailed blockchain implementation case studies in the natural gas industry and various other sectors, and provides detailed insights into the primary application areas, operational expectations–requirements, and implementation challenges specific to each application.
Heat to Hydrogen by Reverse Electrodialysis—Using a Non-Equilibrium Thermodynamics Model to Evaluate Hydrogen Production Concepts Utilising Waste Heat
Authors: Solberg Simon B. B. , Zimmermann Pauline , Wilhelmsen Øivind , Lamb Jacob J. , Bock Robert , Burheim Odne S.
Published: August 2022

The reverse electrodialysis heat engine (REDHE) is a promising salinity gradient energy technology, capable of producing hydrogen with an input of waste heat at temperatures below 100 °C. A salinity gradient drives water electrolysis in the reverse electrodialysis (RED) cell, and spent solutions are regenerated using waste heat in a precipitation or evaporation unit. This work presents a non-equilibrium thermodynamics model for the RED cell, and the hydrogen production is investigated for KCl/water solutions. The results show that the evaporation concept requires 40 times less waste heat and produces three times more hydrogen than the precipitation concept. With commercial evaporation technology, a system efficiency of 2% is obtained, with a hydrogen production rate of 0.38 gH2 m−2h−1 and a waste heat requirement of 1.7 kWh gH2−1. The water transference coefficient and the salt diffusion coefficient are identified as membrane properties with a large negative impact on hydrogen production and system efficiency. Each unit of the water transference coefficient in the range tw=[0–10] causes a −7 mV decrease in unit cell electric potential, and a −0.3% decrease in system efficiency. Increasing the membrane salt diffusion coefficient from 10−12 to 10−11 leads to the system efficiency decreasing from 2% to 0.6%.
Assessment of Turbulence Models over a Curved Hill Flow with Passive Scalar Transport
Authors: Paeres David , Lagares Christian , Araya Guillermo
Published: August 2022

An incoming canonical spatially developing turbulent boundary layer (SDTBL) over a 2-D curved hill is numerically investigated via the Reynolds-averaged Navier–Stokes (RANS) equations plus two eddy-viscosity models: the K−ω SST (henceforth SST) and the Spalart–Allmaras (henceforth SA) turbulence models. A spatially evolving thermal boundary layer has also been included, assuming temperature as a passive scalar (Pr = 0.71) and a turbulent Prandtl number, Prt, of 0.90 for wall-normal turbulent heat flux modeling. The complex flow with a combined strong adverse/favorable streamline curvature-driven pressure gradient caused by concave/convex surface curvatures has been replicated from wind-tunnel experiments from the literature, and the measured velocity and pressure fields have been used for validation purposes (the thermal field was not experimentally measured). Furthermore, direct numerical simulation (DNS) databases from the literature were also employed for the incoming turbulent flow assessment. Concerning first-order statistics, the SA model demonstrated a better agreement with experiments where the turbulent boundary layer remained attached, for instance, in Cp, Cf, and Us predictions. Conversely, the SST model has shown a slightly better match with experiments over the flow separation zone (in terms of Cp and Cf) and in Us profiles just upstream of the bubble. The Reynolds analogy, based on the St/(Cf/2) ratio, holds in zero-pressure gradient (ZPG) zones; however, it is significantly deteriorated by the presence of streamline curvature-driven pressure gradient, particularly due to concave wall curvature or adverse-pressure gradient (APG). In terms of second-order statistics, the SST model has better captured the positively correlated characteristics of u′ and v′ or positive Reynolds shear stresses ( > 0) inside the recirculating zone. Very strong APG induced outer secondary peaks in  and turbulence production as well as an evident negative slope on the constant shear layer.
Overview of Solar–Wind Hybrid Products: Prominent Challenges and Possible Solutions
Authors: Babaremu Kunle , Olumba Nmesoma , Chris-Okoro Ikenna , Chuckwuma Konyegwachie , Jen Tien-Chien , Oladijo Oluseyi , Akinlabi Esther
Published: August 2022

Solar and wind power systems have been prime solutions to the challenges centered on reliable power supply, sustainability, and energy costs for several years. However, there are still various challenges in these renewable industries, especially regarding limited peak periods. Solar–wind hybrid technology introduced to mitigate these setbacks has significant drawbacks and suffers from low adoption rates in many geographies. Hence, it is essential to investigate the challenges faced with these technologies and analyze the viable solutions proposed. This work examined solar–wind hybrid plants’ economic and technical opportunities and challenges. In the present work, the pressing challenges solar–wind hybrids face were detailed through extensive case studies, the case study of enabling policies in India, and overproduction in Germany. Presently, the principal challenges of solar–wind hybrids are overproduction, enabling policies, and electricity storage. This review highlights specific, viable, proposed solutions to these problems. As already recorded in the literature, it was discovered that academic research in this space focuses majorly on the techno-economic and seemingly theoretical aspects of these hybrid systems. In contrast, reports and publications from original equipment manufacturers (OEMs) and engineering, procurement, and construction engineers (EPCs) are more rounded, featuring real-life application and implementation.
Strategies for the Adoption of Hydrogen-Based Energy Storage Systems: An Exploratory Study in Australia
Authors: Wells Cameron , Minunno Roberto , Chong Heap-Yih , Morrison Gregory M.
Published: August 2022

A significant contribution to the reduction of carbon emissions will be enabled through the transition from a centralised fossil fuel system to a decentralised, renewable electricity system. However, due to the intermittent nature of renewable energy, storage is required to provide a suitable response to dynamic loads and manage the excess generated electricity with utilisation during periods of low generation. This paper investigates the use of stationary hydrogen-based energy storage systems for microgrids and distributed energy resource systems. An exploratory study was conducted in Australia based on a mixed methodology. Ten Australian industry experts were interviewed to determine use cases for hydrogen-based energy storage systems’ requirements, barriers, methods, and recommendations. This study suggests that the current cost of the electrolyser, fuel cell, and storage medium, and the current low round-trip efficiency, are the main elements inhibiting hydrogen-based energy storage systems. Limited industry and practical experience are barriers to the implementation of hydrogen storage systems. Government support could help scale hydrogen-based energy storage systems among early adopters and enablers. Furthermore, collaboration and knowledge sharing could reduce risks, allowing the involvement of more stakeholders. Competition and innovation could ultimately reduce the costs, increasing the uptake of hydrogen storage systems.
Influence of Negative Sequence Injection Strategies on Faulted Phase Selector Performance
Authors: Villén María Teresa , Comech Maria Paz , Martinez Carrasco Eduardo , Prada Hurtado Aníbal Antonio
Published: August 2022

Renewable power is expected to increase drastically in the coming years due to the energy transition. A large part of the newly installed generators will be connected to the power system through inverters and electronic converters, whose behaviour differs from the generators connected synchronously to the network. One of the main differences is the current contribution during symmetrical and asymmetrical faults which can affect protection systems. New grid codes establish requirements for fast current injection, but the converter’s maximum current limitations during faults make it difficult to establish control strategies for such current contribution. This paper studies the performance of faulted phase selector algorithm of a commercial relay under the current contribution from renewables before unbalanced faults. Two positive and negative sequence current injection strategies in compliance with new Spanish grid code requirements are proposed and tested under fault conditions in HiL (Hardware in the Loop). Test results show that the selected injection strategy affects the fault phase identification algorithm. Furthermore, the negative sequence injection requirements established in the new grid code improve the relay performance when line-to-line faults are applied, but they are not enough to identify all fault types.
Can China Meet Its 2030 Total Energy Consumption Target? Based on an RF-SSA-SVR-KDE Model
Authors: Cui Xiwen , Guan Xinyu , Wang Dongyu , Niu Dongxiao , Xu Xiaomin
Published: August 2022

In order to accurately predict China’s future total energy consumption, this article constructs a random forest (RF)–sparrow search algorithm (SSA)–support vector regression machine (SVR)–kernel density estimation (KDE) model to forecast China’s future energy consumption in 2022–2030. It is explored whether China can reach the relevant target in 2030. This article begins by using a random forest model to screen for influences to be used as the input set for the model. Then, the sparrow search algorithm is applied to optimize the SVR to overcome the drawback of difficult parameter setting of SVR. Finally, the model SSA-SVR is applied to forecast the future total energy consumption in China. Then, interval forecasting was performed using kernel density estimation, which enhanced the predictive significance of the model. By comparing the prediction results and error values with those of RF-PSO-SVR, RF-SVR and RF-BP, it is demonstrated that the combined model proposed in the paper is more accurate. This will have even better accuracy for future predictions.
Model Predictive Control for Energy Optimization in Generators/Motors as Well as Converters and Inverters for Futuristic Integrated Power Networks
Authors: Mercorelli Paolo
Published: August 2022

Predicting, controlling and distributing energy in an efficient way represents and will represent one of the most critical points for the future of our industrial societies [...]
Effects of Confining Pressure and Hydrostatic Pressure on the Fracturing of Rock under Cyclic Electrohydraulic Shock Waves
Authors: Yu Qing , Zhang Hui , Yang Ruizhi , Cai Zhixiang , Liu Kerou
Published: August 2022

For an array of applications of the high voltage pulse discharge technology in reservoir stimulations and to gain a deeper understanding of the fractures mechanism of deep well rock under cyclic electrohydraulic shock waves (EHSWs), the effect of confining pressure and hydrostatic pressure on the fracturing of rock under EHSWs are investigated in this paper. Firstly, a two-dimensional (2D) water-explosive numerical model is built to match the computed peak pressure of the EHSW with that obtained by the empirical formula by tuning the relevant parameters, based on the equivalent method of EHSWs. Then, a rock model is established to obtain the stress distribution under static loads. Subsequently, the water-explosive model is coupled with the rock model to obtain the stress distribution under static and dynamic loads. In addition, based on this coupling model, the influences of confining pressure and hydrostatic pressure on circumferential stress, radial stress in the rock and the fracturing of rock around the wellbore are discussed. Finally, two improvement measures (increasing discharge energy and changing loading mode) are proposed to acquire greater fracture density based on intensive numerical simulations. The results show that the increase in hydrostatic pressure is beneficial to the crack formation and development, whereas confining pressure is harmful. Moreover, the inhibitory effect of confining pressure on crack formation is greater than the promotion effect of hydrostatic pressure on crack formation. Increasing the discharge energy can effectively promote the development of the number and length of main cracks. Under four repetitive loading modes with the same total discharge energy (1.36 × 15 kJ), the greatest fracture density can be obtained by using repetitive loading mode with a gradually decreasing mode of discharge energy (first level: 2 times (1.36 × 5 kJ); second level: 5 times (1.36 × 1 kJ)).
A Novel Overlap-Time Effect Suppression for Current Source Converter
Authors: Ding Hao , Li Quanjie , Yuan Jing , Wang Wei , Li Mingming , Guerrero Josep M.
Published: August 2022

In order to ensure the continuity of the DC-side inductor current, current source converter (CSC) needs to add overlap time between the drive signals, but the overlap time will introduce low order (mainly fifth and seventh) harmonics to the grid current, which seriously degrade the harmonic performance of grid current. At present, some research has been conducted to theoretically analyze and mitigate the overlap-time effect in CSC, including the use of positive-slope sawtooth wave or negative-slope sawtooth wave as the carrier wave, turning on the switch early or delaying turning it off, and eliminating the deviation effect by compensation algorithms, etc. However, existing overlap-time suppression schemes takes the nearest three vector synthesis reference vector scheme as the object of study, in other words, the effect of overlap time on the non-nearest three-vector synthesis reference vector scheme has not been considered. To address these issues, this paper takes the non-nearest three-vector synthesis reference vector scheme as the object of study to analyze the effect of overlap time on the driving signal and establishes the quantitative relationship between the current harmonics introduced in the grid current and overlap time through Fourier decomposition. Then, the design process of the proposed improved space vector modulation by constructing freewheeling channels to replace the overlap time is presented in detail. Finally, simulation and experimental results verify that the overlap-time suppression effect of the proposed scheme is about 100%.
Stylization of a Seismic Image Profile Based on a Convolutional Neural Network
Authors: Hu Huiting , Lian Wenxin , Su Rui , Ren Chongyu , Zhang Juan
Published: August 2022

Seismic data are widely used in oil, gas, and other kinds of mineral exploration and development. However, due to low artificial interpretation accuracy and small sample sizes, seismic data may not meet the needs of convolutional neural network training. There are major differences between optical image and seismic data, making it difficult for a model to learn seismic data characteristics. Therefore, a style transfer network is necessary to make the styles of optical image and seismic data more similar. Since the stylization effect of a seismic section is similar to that of most art styles, based on an in-depth study of image style transfer, this paper compared the effects of various style transfer models, and selected a Laplacian pyramid network to carry out a study of seismic section stylization. It transmits low-resolution global style patterns through a drafting network, revises high-resolution local details through correction networks, and aggregates all pyramid layers to output final stylized images of seismic profiles. Experiments show that this method can effectively convey the whole style pattern without losing the original image content. This style transfer method, based on the Laplacian pyramid network, provides theoretical guidance for the fast and objective application of the model to seismic data features.
The Theoretical, Practical, and Technological Foundations of the 15-Minute City Model: Proximity and Its Environmental, Social and Economic Benefits for Sustainability
Authors: Allam Zaheer , Bibri Simon Elias , Chabaud Didier , Moreno Carlos
Published: August 2022

Conventional and emerging paradigms of urbanism require new responses under the current circumstances, especially in relation to the integration of sustainability dimensions and technology advances. The escalating rate of urbanization, coupled with the climate emergency, fundamentally indeed disrupt the challenges that urbanism research and practice deal with, calling for adopting more innovative approaches to urban planning and design. With cities contributing around 65% of Greenhouse Gas (GHG) emissions and experiencing an unprecedented growth of population, contemporary urban policy needs to be redefined and re-assessed accordingly. While numerous urban models, such as the Compact City, the Eco-City, the Sustainable City, and the Smart City, have emerged in response to the challenges of sustainability and urbanization, the 15-Minute City has recently gained a steep popularity. This paper explores the theoretical, practical, and technological foundations of the 15-Minute City, with a particular focus on the proximity dimension of mixed land-use and its environmental, social, and economic benefits of sustainability as supported by smart technologies. We argue that this evolving model of urbanism has the potential to gain more expansion and success in regard to building more sustainable, efficient, resilient, equitable, and inclusive cities in line with the global agendas of Sustainable Development Goal (SDG) 11, as it adds a strategic value to the amalgam of the prevailing and emerging paradigms of urbanism and their synergies with respect to increasing the benefits of sustainability while emphasizing its environmental dimension.
Recent Developments of Heat Transfer Enhancement and Thermal Management Technology
Authors: Xi Wenxiong , Xu Mengyao , Liu Chaoyang , Liu Jian
Published: August 2022

New thermal management technology is widely developed in new energy industries, such as electric vehicles, IoT sensors, photothermal energy conversion, ground thermal energy utilization and propulsion systems [...]
Socio-Economic, Technical and Environmental Indicators for Sustainable Sewage Sludge Management and LEAP Analysis of Emissions Reduction
Authors: Đurđević Dinko , Žiković Saša , Čop Tomislav
Published: August 2022

The waste management sector is transitioning from a dirty and undesirable industry towards a green and sustainable future where energy and materials are recycled. Recycling has potential in sewage sludge treatment, where energy and highly valuable nutrients can be recovered through innovative and sustainable sludge management. Although there are many technologies and techniques already used for sewage sludge, the indicators for their sustainability are not developed. In terms of sustainable and innovative sewage sludge treatment, usually, only techno-economic aspects of existing and current technologies are considered. We explore the existing indicators and propose new indicators for sustainable and innovative sewage sludge treatment technologies. The indicators are differentiated into four main categories: technical, social, environmental and economic, where specific indicators are explained, followed by a description of their impact on sustainability. We also consider a case study using the LEAP tool, which considered GHG emissions when utilizing sewage sludge as an energy feedstock to replace existing fossil fuels in the energy mix in several scenarios. The results showed a significant emissions reduction when sludge is used—37.6% and 90.9% in 2030 and 2050, respectively.
Refined 1D–3D Coupling for High-Frequency Forced Vibration Analysis in Hydraulic Systems
Authors: Zhou Xijun , Ye Yongjin , Zhang Xianyu , Yang Xiuwei , Wang Haijun
Published: August 2022

High-Frequency Pressure Fluctuation (HFPF) is an extensively observed hydraulic phenomenon in pumped-storage power stations and water conveyance projects. The investigation of the propagation characteristics of the pressure perturbation is of great significance for the safe operation of hydraulic facilities. In this study, a one-dimensional (1D)–three-dimensional (3D) coupling model is established based on the combination of the Method of Characteristics (MOC) and Computational Fluid Dynamics (CFD) and implanted in the open source software OpenFOAM. The established model in this study implants the dynamic mesh module into the original OpenFOAM solver sonicLiquidFoam and presents the complete solution procedure of the CFD model with the dynamic mesh considered. The vibration of the pipe walls modeled by the mesh motion is employed to numerically generate the HFPF in the hydraulic system, which could not be implemented in the traditional MOC model. The independence of the pressure perturbation in the pipeline system is validated by the time-domain pressure variation. The graphical method is applied to describe the multiple reflection and superposition characteristics of the traveling wave in a simplified hydraulic system. Based on this, the mechanism of the superimposed characteristic of the traveling and standing pressure waves in the hydraulic system are analyzed, and the theoretical superimposed time-domain processes and the variations of the pressure oscillation magnitude are analyzed and presented. The 1D–3D coupling method and the theoretical analysis method could be referenced by other complex hydraulic systems.
The Effect of Bedding Plane Angle on Hydraulic Fracture Propagation in Mineral Heterogeneity Model
Authors: Han Weige , Cui Zhendong , Zhu Zhengguo , Han Xianmin
Published: August 2022

The bedding planes of unconventional oil and gas reservoirs are relatively well developed. Bedding planes directly interfere with hydraulic fracture expansion. Determining how bedding planes influence hydraulic fractures is key for understanding the formation and evolution of hydraulic fracturing networks. After conducting X-ray diffraction analysis of shale, we used Python programming to establish a numerical model of mineral heterogeneity with a 0-thickness cohesive element and a bedding plane that was globally embedded. The influence of the bedding-plane angle on hydraulic fracture propagation was studied. Acoustic emission (AE) data were simulated using MATLAB programming to study fracture propagation in detail. The numerical simulation and AE data showed that the propagation paths of hydraulic fractures were determined by the maximum principal stress and bedding plane. Clearer bedding effects were observed with smaller angles between the bedding surface and the maximum principal stress. However, the bedding effect led to continuous bedding slip fractures, which is not conducive to forming a complex fracture network. At moderate bedding plane angles, cross-layer and bedding fractures alternately appeared, characteristic of intermittent dislocation fracture and a complex fracture network. During hydraulic fracturing, tensile fractures represented the dominant fracture type and manifested in cross-layer fractures. We observed large fracture widths, which are conducive to proppant migration and filling. However, the shear fractures mostly manifested as bedding slip fractures with small fracture widths. Combining the fracture-network, AE, and fractal dimension data showed that a complex fracture network was most readily generated when the angle between the bedding plane and the maximum principal stress was 30°. The numerical simulation results provide important technical information for fracturing construction, which should support the efficient extraction of unconventional tight oil and gas.
An Equivalent Model of Wind Farm Based on Multivariate Multi-Scale Entropy and Multi-View Clustering
Authors: Han Ji , Li Li , Song Huihui , Liu Meng , Song Zongxun , Qu Yanbin
Published: August 2022

Wind farm (WF) equivalence is an effective method to achieve accurate and efficient simulation of large-scale WF. Existing equivalent models are generally suitable for one certain or very few scenarios, and have difficulty reflecting the multiple aspects of dynamic processes of WF. Aiming at these problems, this paper proposes an equivalent model of WF based on multivariate multi-scale entropy (MMSE) and multi-view clustering. Firstly, the influence of the factors on the dynamic process of the wind turbine (WT) is discussed, including control mode, wind speed and its wake effect, resistance of crowbar resistor and so on. The relationship between these factors and the dynamic equivalence of WF is analyzed. Secondly, an overview of MMSE is given, and the applicability of MMSE on WF equivalence is analyzed. On this basis, this paper proposes the extraction process of a WT clustering indicator using MMSE. Then, the multi-view fuzzy C means (MV-FCM) algorithm is used for the clustering of WTs, and the equivalent model of WF is obtained after calculating the equivalent parameters. Finally, the IEEE14 power system including WF is simulated. The results show that the equivalent model could be applied to dynamic process simulation in various fault scenarios of power systems, and the error is small when the cluster number is 4. Compared with the detailed model, the simulation time of the WF equivalent model proposed in this paper is shortened by 86%, and the simulation accuracy is improved by about 44% compared with the comparative model.
Thermal Degradation and Organic Chlorine Removal from Mixed Plastic Wastes
Authors: Xu Zhuo , Ierulli Victor , Bar-Ziv Ezra , McDonald Armando
Published: August 2022

Plastic waste accumulation has been growing due to the increase in plastic generation and the lack of infrastructure for recycling. One of the approaches is to treat the mixed plastic waste (MPW) through thermal processes to produce feedstocks for other applications. However, the presence of polyvinyl chloride (PVC) in MPW would produce HCl during processing and has negative impacts (emission, catalyst poisoning, etc.). In addition, due to the high heterogeneity of MPW, it is difficult to generate consistent experimental data. In this study, MPW was homogenized through double compounding–extrusion and then formed into a sheet to be treated at 400 °C. The solid products at various mass losses were characterized by heat and chlorine content, Fourier-transform infrared (FTIR) spectroscopy, and elemental composition analysis. It was found that the thermal degradation of MPW started at ~260 °C. The chlorine removal efficiency increased with mass loss and reached an asymptotic value of ~84% at ~28% mass loss, and the remaining chlorine can be attributed to inorganic sources. A PVC de-chlorination model was developed for MPW using TGA data for PVC and MPW to determine organic chlorine removal efficiency. These results show that PVC de-chlorination was not affected by other plastics at this temperature. As the mass loss increases, the heat content first increases and then decreases. It was found that mass loss is a universal parameter for organic chlorine removal efficiency and heat content. The elemental composition analysis and FTIR spectroscopy also shed more light into the chemical changes during MPW thermal degradation.
Rotating Flow and Heat Transfer of Single-Wall Carbon Nanotube and Multi-Wall Carbon Nanotube Hybrid Nanofluid with Base Fluid Water over a Stretching Sheet
Authors: Haider Syed Muhammad Ali , Ali Bagh , Wang Qiuwang , Zhao Cunlu
Published: August 2022

In this article, numerical simulations of the rotational flow of water-based magnetohydrodynamic (MHD) nanofluid containing single-wall carbon nanotube (SWCNT) and hybrid nanofluid containing single- and multiple-wall carbon nanotube (SWCNT-MWCNT) over a stretching sheet are performed. The primary goal is to improve thermal transport efficiency due to CNTs extraordinary thermal conductivity. The 3D governing equations for microorganism concentration, energy, momentum, concentration, and mass conservation are transformed into 1D ordinary differentiation via similarity transformations. In a MATLAB environment, the resultant system of equations (ODEs) are then solved using Runge–Kutta fourth order with the shooting process. Tables and graphs were used to show the results of physical parameters. According to our findings, enhancing the rotational parameter λ and the magnetic field M reduce the base fluid velocity along the x-axis, and on the other hand, the opposite tendency is shown along the y-axis. Furthermore, the velocities, temperature, and microorganism concentration profiles of hybrid nanofluid (SWCNT−MWCNT/H2O) are found to be higher than those of mono nanofluid (H2O+SWCNT), while the concentration profile is found to be lower.
Conveyor Intermediate TT Drive with Power Transmission at the Return Belt
Authors: Bortnowski Piotr , Gładysiewicz Adam , Gładysiewicz Lech , Król Robert , Ozdoba Maksymilian
Published: August 2022

Intermediate linear booster drive can solve many problems of transport by long route conveyors. At the same time, operating costs are significantly reduced. There are solutions using intermediate belt drives, usually involving friction coupling in the carry belt. From a theoretical point of view, it is possible to transmit the friction force on an additional section in the return belt. The article presents a theoretical and experimental analysis of this solution and a comparison with a drive operating in a conventional solution. The transferred forces, the variability of the belt tension as well as the efficiency and stability of the drive for both solutions were compared. The use of additional coupling in the return belt makes it possible to increase the transmitted friction force and achieve a better rate of electricity consumption. The solution can be useful in currently existing intermediate drives, where it is possible to support the return side and transmit power.
Optimization of Fracturing Parameters with Machine-Learning and Evolutionary Algorithm Methods
Authors: Dong Zhenzhen , Wu Lei , Wang Linjun , Li Weirong , Wang Zhengbo , Liu Zhaoxia
Published: August 2022

Oil production from tight oil reservoirs has become economically feasible because of the combination of horizontal drilling and multistage hydraulic fracturing. Optimal fracture design plays a critical role in successful economical production from a tight oil reservoir. However, many complex parameters such as fracture spacing and fracture half-length make fracturing treatments costly and uncertain. To improve fracture design, it is essential to determine reasonable ranges for these parameters and to evaluate their effects on well performance and economic feasibility. In traditional analytical and numerical simulation methods, many simplifications and assumptions are introduced for artificial fracture characterization and gas percolation mechanisms, and their implementation process remains complicated and computationally inefficient. Most previous studies on big data-driven fracturing parameter optimization have been based on only a single output, such as expected ultimate recovery, and few studies have integrated machine learning with evolutionary algorithms to optimize fracturing parameters based on time-series production prediction and economic objectives. This study proposed a novel approach, combining a data-driven model with evolutionary optimization algorithms to optimize fracturing parameters. We established a significant number of static and dynamic data sets representing the geological and developmental characteristics of tight oil reservoirs from numerical simulation. Four production-prediction models based on machine-learning methods—support vector machine, gradient-boosted decision tree, random forest, and multilayer perception—were constructed as mapping functions between static properties and dynamic production. Then, to optimize the fracturing parameters, the best machine-learning-based production predictive model was coupled with four evolutionary algorithms—genetic algorithm, differential evolution algorithm, simulated annealing algorithm, and particle swarm optimization—to investigate the highest net present value (NPV). The results show that among the four production-prediction models established, multilayer perception (MLP) has the best prediction performance. Among the evolutionary algorithms, particle swarm optimization (PSO) not only has the fastest convergence speed but also the highest net present value. The optimal fracturing parameters for the study area were identified. The hybrid MLP-PSO model represents a robust and convenient method to forecast the time-series production and to optimize fracturing parameters by reducing manual tuning.
The Role of Clean Hydrogen Value Chain in a Successful Energy Transition of Japan
Authors: Salimi Mohsen , Hosseinpour Morteza , N.Borhani Tohid
Published: August 2022

The clean hydrogen in the prioritized value chain platform could provide energy incentives and reduce environmental impacts. In the current study, strengths, weaknesses, opportunities, and threats (SWOT) analysis has been successfully applied to the clean hydrogen value chain in different sectors to determine Japan’s clean hydrogen value chain’s strengths, weaknesses, opportunities, and threats as a case study. Japan was chosen as a case study since we believe that it is the only pioneer country in that chain with a national strategy, investments, and current projects, which make it unique in this way. The analyses include evaluations of clean energy development, power supply chains, regional energy planning, and renewable energy development, including the internal and external elements that may influence the growth of the hydrogen economy in Japan. The ability of Japan to produce and use large quantities of clean hydrogen at a price that is competitive with fossil fuels is critical to the country’s future success. The implementation of an efficient carbon tax and carbon pricing is also necessary for cost parity. There will be an increasing demand for global policy coordination and inter-industry cooperation. The results obtained from this research will be a suitable model for other countries to be aware of the strengths, weaknesses, opportunities, and threats in this field in order to make proper decisions according to their infrastructures, potentials, economies, and socio-political states in that field.
Porosity and Permeability Model of a Regionally Extending Unit (Upper Miocene Sandstones of the Western Part of Sava Depression, Croatia) Based on Vintage Well Data
Authors: Kolenković Močilac Iva , Cvetković Marko , Saftić Bruno , Rukavina David
Published: August 2022

The deep saline aquifer (DSA) Poljana in the Upper Pannonian Poljana Sandstones of Sava depression, the SW part of the Pannonian basin system, was identified as a potential CO2 storage object in previous works. Its boundaries have been redefined and its general model further developed, including the areal distribution of porosity based on analyses of 23 well logs. The sandstones were deposited in turbiditic and deltaic facies that caused considerable variations of porosity, which was further influenced by diagenetic processes. A comparison of altogether 355 pairs of porosity and permeability measurements on core plugs from 16 wells indicated 2 different sets of samples: impermeable samples with effective porosities reaching 18% and permeable samples which showed correlation between porosity and permeability. Accordingly, the permeability model was developed as semi-categorical with two categories: the first category comprising parts of DSA Poljana with porosity values exceeding 18%, where permeability was correlated with porosity, although with limited reliability, and the second category comprising model cells with porosity values below the threshold of 18%, where permeability should not be correlated with porosity due to the appearance of impermeable values. This approach enabled delineation of areas where permeability can be estimated with greater certainty, which is of utmost importance for the planning and development of CO2 storage projects and/or energy storage projects with respect to fluid injectivity. This approach can be used in areas with similar geological settings and limited datasets as an important step from regional capacity estimations towards the detailed, local-scale investigations.
An Examination of Consumers’ Opinions toward Adopting Electric Vehicles in the United Arab Emirates: On the Effects of Functional and Symbolic Values
Authors: Bridi Robert M. , Jabra Marwa Ben , Hosani Naeema Al
Published: August 2022

The aim of this study was to examine consumers’ opinions toward adopting electric vehicles (EVs) for light-duty transport in the United Arab Emirates (UAE) from the functional value (i.e., the utility or benefit attained by consumers from the functions or tangible features associated with EVs) and symbolic value (i.e., the social meaning that consumers associate with EVs) perspectives. The primary research question was as follows: To what extent do functional and symbolic values affect consumers’ opinions toward adopting EVs in the UAE? The objectives were to determine if relationships exist between gender, age, and residency and the functional and symbolic values of consumers’ opinions toward adopting EVs. A survey of 5459 people was conducted in 14 cities across the seven emirates (Abu Dhabi, Ajman, Dubai, Fujairah, Ras Al Khaimah, Sharjah, and Umm Al Quwain) to test the relationship. The results revealed that females, respondents aged 20–29, and residents living in Abu Dhabi City found more appealing functional and symbolic values regarding EVs.