bluuebunny/crossref_metadata_2025 huggingface dataset:
Psychological Distress Related to the COVID-19 Pandemic: The Protective Role of Hope
Authors: Flesia Luca , Adeeb Muhammad , Waseem Aqsa , Helmy Mai , Monaro Merylin
Published: January 2023

The COVID-19 outbreak and the worldwide lockdown measures had an impact on the global mental health and psychological well-being of the general population. Several studies attempted to investigate the protective and risk factors for psychological distress related to the pandemic. However, to date, little is known about the role of hope in this context. The aim of this study was to determine the relationship between hope and psychological distress related to the COVID-19 outbreak in the general population. The sample consisted of 504 Pakistani people who completed cross-sectionally the COVID-19 Peritraumatic Distress Index (CPDI) and the Adult Hope Scale (AHS). Bivariate Pearson correlation analysis was run to measure the relationship between hope and psychological distress; hierarchical regression analysis was run to investigate the association between demographics and hope with psychological distress. Higher levels of hope predicted lower levels of psychological distress. Being female, being older, lower level of education, urban residence, being married and living in nuclear family systems were associated with higher levels of psychological distress. The study highlights the protective role of hope on psychological distress related to COVID-19, contributing to knowledge on factors promoting positive mental health during emergency times and providing useful information for implementing effective public health policies and programmes.
The Role of Emotional Intelligence and Metacognition in Teachers’ Stress during Pandemic Remote Working: A Moderated Mediation Model
Authors: Iacolino Calogero , Cervellione Brenda , Isgrò Rachele , Lombardo Ester Maria Concetta , Ferracane Giuseppina , Barattucci Massimiliano , Ramaci Tiziana
Published: January 2023

During the COVID-19 pandemic, in adapting to social and work changes and new technological methods for remote teaching, teachers were subjected to increased work pressure, which affected their well-being and led to increased negative stress and burnout. This study was designed to test whether dysfunctional outcomes resulting from adapting to new ways of teaching via technological tools can be mitigated by the protective factors of emotional intelligence and metacognition. The study involved 604 teachers in Sicily filling out a questionnaire consisting of four different scales: (1) the Schutte Self-Report Emotional Intelligence Test (SREIT); (2) the Metacognitive Functions Screening Scale (MFSS-30); (3) the Link Burnout Questionnaire (LBQ); and (4) the Secondary Traumatic Stress Scale STSS-I. The results show that emotional intelligence mediates the relationship between certain remote work risk factors, as well as stress and burnout. In addition, metacognition was found to be a significant moderating factor in the relationship between risk factors and emotional intelligence. With regard to the United Nations’ Agenda 2030 Goals, our results emphasize the importance of teachers’ emotional and metacognitive skills in promoting quality of life and psychological well-being.
A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN
Authors: Alshingiti Zainab , Alaqel Rabeah , Al-Muhtadi Jalal , Haq Qazi Emad Ul , Saleem Kashif , Faheem Muhammad Hamza
Published: January 2023

In terms of the Internet and communication, security is the fundamental challenging aspect. There are numerous ways to harm the security of internet users; the most common is phishing, which is a type of attack that aims to steal or misuse a user’s personal information, including account information, identity, passwords, and credit card details. Phishers gather information about the users through mimicking original websites that are indistinguishable to the eye. Sensitive information about the users may be accessed and they might be subject to financial harm or identity theft. Therefore, there is a strong need to develop a system that efficiently detects phishing websites. Three distinct deep learning-based techniques are proposed in this paper to identify phishing websites, including long short-term memory (LSTM) and convolutional neural network (CNN) for comparison, and lastly an LSTM–CNN-based approach. Experimental findings demonstrate the accuracy of the suggested techniques, i.e., 99.2%, 97.6%, and 96.8% for CNN, LSTM–CNN, and LSTM, respectively. The proposed phishing detection method demonstrated by the CNN-based system is superior.
Decentralized Energy Management System in Microgrid Considering Uncertainty and Demand Response
Authors: Wynn Sane Lei Lei , Boonraksa Terapong , Boonraksa Promphak , Pinthurat Watcharakorn , Marungsri Boonruang
Published: January 2023

Smart energy management and control systems can improve the efficient use of electricity and maintain the balance between supply and demand. This paper proposes the modeling of a decentralized energy management system (EMS) to reduce system operation costs under renewable generation and load uncertainties. There are three stages of the proposed strategy. First, this paper applies an autoregressive moving average (ARMA) model for forecasting PV and wind generations as well as power demand. Second, an optimal generation scheduling process is designed to minimize system operating costs. The well-known algorithm of particle swarm optimization (PSO) is applied to provide optimal generation scheduling among PV and WT generation systems, fuel-based generation units, and the required power from the main grid. Third, a demand response (DR) program is introduced to shift flexible load in the microgrid system to achieve an active management system. Simulation results demonstrate the performance of the proposed method using forecast data for hourly PV and WT generations and a load profile. The simulation results show that the optimal generation scheduling can minimize the operating cost under the worst-case uncertainty. The load-shifting demand response reduced peak load by 4.3% and filled the valley load by 5% in the microgrid system. The proposed optimal scheduling system provides the minimum total operation cost with a load-shifting demand response framework.
InfoMax Classification-Enhanced Learnable Network for Few-Shot Node Classification
Authors: Xu Xin , Du Junping , Song Jie , Xue Zhe
Published: January 2023

Graph neural networks have a wide range of applications, such as citation networks, social networks, and knowledge graphs. Among various graph analyses, node classification has garnered much attention. While many of the recent network embedding models achieve promising performance, they usually require sufficient labeled nodes for training, which does not meet the reality that only a few labeled nodes are available in novel classes. While few-shot learning is commonly employed in the vision and language domains to address the problem of insufficient training samples, there are still two characteristics of the few-shot node classification problem in the non-Euclidean domain that require investigation: (1) how to extract the most informative knowledge for a class and use it on testing data and (2) how to thoroughly explore the limited number of support sets and maximize the amount of information transferred to the query set. We propose an InfoMax Classification-Enhanced Learnable Network (ICELN) to address these issues, motivated by Deep Graph InfoMax (DGI), which adapts the InfoMax principle to the summary representation of a graph and the patch representation of a node. By increasing the amount of information that is shared between the query nodes and the class representation, an ICELN can transfer the maximum amount of information to unlabeled data and enhance the graph representation potential. The whole model is trained using an episodic method, which simulates the actual testing environment to ensure the meta-knowledge learned from previous experience may be used for entirely new classes that have not been studied before. Extensive experiments are conducted on five real-world datasets to demonstrate the advantages of an ICELN over the existing few-shot node classification methods.
Machine Learning Based Interference Mitigation for Intelligent Air-to-Ground Internet of Things
Authors: Liu Lei , Li Chaofei , Zhao Yikun
Published: January 2023

With the continuous development of the Internet of things (IoT) technology, the air-to-ground (ATG) system has attracted more and more attention. The system will effectively increase communication coverage and improve communication quality. The ATG system uses frequency reuse technology in the ground layer to further utilize frequency resources. This paper focuses mostly on the cochannel interference between the 5G BS and the ATG airborne CPE terminal in the 3.5 GHz range. The ATG airborne CPE terminal has to be further isolated from 5G BS in order to prevent interference. We must manage the transmitting power of the ATG airborne CPE terminal in order to comply with the additional isolation criteria. The RSRP value of 5G BS determines the transmit power of the ATG airborne CPE terminal. We creatively suggested a machine learning (ML) approach based on multihead attention to anticipate the RSRP of 5G BS because it is highly challenging for the ATG aerial CPE terminal to monitor the RSRP of 5G BS in real time. By comparing the suggested ML-based approach with the actual measured values, its efficacy is confirmed.
VoiceJava: A Syntax-Directed Voice Programming Language for Java
Authors: Zan Tao , Hu Zhenjiang
Published: January 2023

About 5–10% of software engineers suffer from repetitive strain injury, and it would be better to provide an alternative way to write code instead of using a mouse and keyboard and sitting on a chair the whole day. Coding by voice is an attractive approach, and quite a bit of work has been done in that direction. At the same time, dictating plain Java text with low accuracy through the existing voice recognition engines or providing complex panels controlled by the voice makes the coding process even more complex. We argue that current programming languages are suitable for programming by hand, not by mouth. We try to solve this problem by designing a new programming language, VoiceJava, suitable for dictating. A Java program is constructed in a syntax-directed way through a sequence of VoiceJava commands. As a result, users do not need to dictate spaces, parentheses, and commas, reducing the vocal load.
GEAR: A General Inference Engine for Automated MultiStrategy Reasoning
Authors: Ferilli Stefano
Published: January 2023

The pervasive use of AI today caused an urgent need for human-compliant AI approaches and solutions that can explain their behavior and decisions in human-understandable terms, especially in critical domains, so as to enforce trustworthiness and support accountability. The symbolic/logic approach to AI supports this need because it aims at reproducing human reasoning mechanisms. While much research has been carried out on single inference strategies, an overall approach to combine them is still missing. This paper claims the need for a new overall approach that merges all the single strategies, named MultiStrategy Reasoning. Based on an analysis of research on automated inference in AI, it selects a suitable setting for this approach, reviews the most promising approaches proposed for single inference strategies, and proposes a possible combination of deduction, abduction, abstraction, induction, argumentation, uncertainty and analogy. It also introduces the GEAR (General Engine for Automated Reasoning) inference engine, that has been developed to implement this vision.
Blockchain and Machine Learning for Future Smart Grids: A Review
Authors: Mololoth Vidya Krishnan , Saguna Saguna , Åhlund Christer
Published: January 2023

Developments such as the increasing electrical energy demand, growth of renewable energy sources, cyber–physical security threats, increased penetration of electric vehicles (EVs), and unpredictable behavior of prosumers and EV users pose a range of challenges to the electric power system. To address these challenges, a decentralized system using blockchain technology and machine learning techniques for secure communication, distributed energy management and decentralized energy trading between prosumers is required. Blockchain enables secure distributed trust platforms, addresses optimization and reliability challenges, and allows P2P distributed energy exchange as well as flexibility services between customers. On the other hand, machine learning techniques enable intelligent smart grid operations by using prediction models and big data analysis. Motivated from these facts, in this review, we examine the potential of combining blockchain technology and machine learning techniques in the development of smart grid and investigate the benefits achieved by using both techniques for the future smart grid scenario. Further, we discuss research challenges and future research directions of applying blockchain and machine learning techniques for smart grids both individually as well as combining them together. The identified areas that require significant research are demand management in power grids, improving the security of grids with better consensus mechanisms, electric vehicle charging systems, scheduling of the entire grid system, designing secure microgrids, and the interconnection of different blockchain networks.
Exploring the Pivotal Significance of Microalgae-Derived Sustainable Lipid Production: A Critical Review of Green Bioenergy Development
Authors: Vasistha Shrasti , Khanra Anwesha , Rai Monika , Khan Shakeel , Ma Zengling , Munawaroh Heli , Tang Doris , Show Pau
Published: January 2023

The persistent fight against global environmental threats and energy catastrophe is currently a major concern for the economic development of bioenergy across the entire country. Hence, traditional fuel-based reserves are overburdened to cope with the rapid energy crisis, necessitating an urgent need for an innovative carbon-neutral green-energy resource. In order to address these critical bottlenecks, microalgae with incredible metabolic versatility have paved the way for a pivotal attention towards sustainable biofuel production. However, due to high operational costs and low lipid productivity, the microalgae-based biofuel resource is still in its infancy. As a result, this problem can be overcome by incorporating engineered microalgal strains which can pave the way for significant lipid augmentation for biofuel production. Thus, our current review depicts an in-depth understanding of a multi-omics approach to microalgae, the broad scope of self-sustaining microalgae cultivation, lipid-extraction strategies, and conversion processes to improve economic commercialization in the bioenergy framework. The present review also provides a detailed analysis of the international and national status of bioenergy development by several federal agencies.
Increasing Turbine Hall Safety by Using Fire-Resistant, Hydrogen-Containing Lubricant Cooling Liquid for Rotor Steel Mechanical Treatment
Authors: Balitskii Alexander , Havrilyuk Maria , Balitska Valentina , Kolesnikov Valeriі , Ivaskevych Ljubomyr
Published: January 2023

This paper is devoted to the development of hydrogen-containing, environmentally safe, fire-resistant, and corrosion-protected lubricant cooling liquids (LCLs) from vegetable oils with improved sanitary and hygienic parameters for the machining of parts and equipment made from high-strength steels for application during the interoperation period in turbine halls. The use of plant raw materials as ecologically and fire-safe LCLs increased the efficiency of LCLs when evaluating drilling steel in terms of the dependence of the stability of the cutting tool on the drilling speed. Chips formed from LCLs during turning had a compact, cylindrical appearance, and the addition of both water and coolant during turning significantly changed the morphology of the cutting particles. Using water and LCL intensified the physical and chemical destruction processes. After the use of water and LCL, the concentration of hydrogen in the cutting products of 38KHN3MFA steel increased, which indicated its participation in facilitating the destruction during machining. In the chips formed when using LCL, the amount of hydrogen increased by 2.25 times compared to the chips obtained with the dry treatment, while with coolants, it increased by 2.6 times, indicating the intense flow of decomposition products of LCL through diffusion processes in the cutting zone. Hydrogen reduces the energy costs for the destruction of structural and phase components and promotes their dispersion. The creation of 2D and 3D images allowed for a more detailed approach to the study of the influence of LCL on surface treatments.
Compressed Air Energy Storage System with Burner and Ejector
Authors: Yang Dahui , Wen Xiankui , Zhong Jingliang , Feng Tingyong , Deng Tongtian , Li Xiang
Published: January 2023

The timescale of the energy-release process of an energy storage system has put forward higher requirements with the increasing proportion of new energy power generation in the power grid. In this paper, a new type of compressed-air energy storage system with an ejector and combustor is proposed in order to realize short-timescale and long-timescale energy-release processes under the non-supplementary combustion condition and ejector supplementary combustion condition, respectively. A simulation model of the new system is established in APROS software. The results of this study show that the new system can realize continuous power output when energy storage and energy release operate simultaneously, and especially when the ejector coefficient is 0.8 and burner thermal power is 10 MW, the power-generation time is 12.45 h and the total generated power is 140,052 kW∙h, which are 15.6 and 17.5 times greater those of the short-timescale condition, respectively. In summary, the compressed-air energy storage system with an ejector and combustor that is proposed in this paper can flexibly meet the demands of multiple timescales’ power generation.
Analysis of Power to Gas Technologies for Energy Intensive Industries in European Union
Authors: Borge-Diez David , Rosales-Asensio Enrique , Açıkkalp Emin , Alonso-Martínez Daniel
Published: January 2023

Energy Intensive Industries (EII) are high users of energy and some of these facilities are extremely dependent on Natural Gas for processing heat production. In European countries, where Natural Gas is mostly imported from external producers, the increase in international Natural Gas prices is making it difficult for some industries to deliver the required financial results. Therefore, they are facing complex challenges that could cause their delocalization in regions with lower energy costs. European countries lack on-site Natural Gas resources and the plans to reduce greenhouse gas emissions in the industrial sector make it necessary to find an alternative. Many different processes cannot be electrified, and in these cases, synthetic methane is one of the solutions and also represents an opportunity to reduce external energy supply dependency. This study analyzes the current development of power-to-gas technological solutions that could be implemented in large industrial consumers to produce Synthetic Methane using Green Hydrogen as a raw source and using Renewable Energy electricity mainly produced with photovoltaic or wind energy. The study also reviews the triple bottom line impact and the current development status and associated costs for each key component of a power-to-gas plant and the requirements to be fulfilled in the coming years to develop a cost-competitive solution available for commercial use.
A Review on Heat Transfer Enhancement of Phase Change Materials Using Fin Tubes
Authors: Ma Fei , Zhu Tianji , Zhang Yalin , Lu Xinli , Zhang Wei , Ma Feng
Published: January 2023

Latent heat thermal energy storage (LHTES) has received more and more attention in the thermal energy storage field due to the large heat storage density and nearly constant temperature during phase change process. However, the low thermal conductivity of phase change material (PCM) leads to poor performance of the LHTES system. In this paper, the research about heat transfer enhancement of PCM using fin tubes is summarized. Different kinds of fins, such as rectangular fin, annular fin, spiral fin, etc., are discussed and compared based on the shape of the fins. It is found that the longitudinal rectangular fins have excellent heat transfer performance and are easy to manufacture. The effect of fins on heat transfer enhancement is closely related to the number of fins and its geometric parameters.
An Algorithm for Managerial Actions on the Rational Use of Renewable Sources of Energy: Determination of the Energy Potential of Biomass in Lithuania
Authors: Kukharets Valentyna , Juočiūnienė Dalia , Hutsol Taras , Sukmaniuk Olena , Čėsna Jonas , Kukharets Savelii , Piersa Piotr , Szufa Szymon , Horetska Iryna , Shevtsova Alona
Published: January 2023

This paper offers an algorithm to account for potential actions on the efficient production of renewable energy. The algorithm consists of a substantiated choice of a certain type of renewable energy, the evaluation of its potential, and the regulation of the processes of obtaining that renewable energy. Also, potential resources for agricultural biofuel production have been analyzed and it has been determined that there is real biomass potential in Lithuania. It will thus be beneficial to make appropriate managerial decisions on the methods of biofuel processing and consumption, as well as on means of receiving the economic, energy and environmental effects. The total potential of by-product biomass of crop production was determined, and the thermal and electric potential of the crop by-products were calculated. Additionally, the potential for production of gas-like types of fuel (biomethane, biohydrogen, and syngas) from crop by-products was determined. The potential for the production of diesel biofuel from oil crop waste (bran) was also found, and the potential for livestock by-products for receiving gas-like types of fuel (biomethane, biohydrogen) was established. The corresponding thermal and electric equivalents of the potential were found and the potential volumes of the biomethane and biohydrogen production were calculated. The total energy equivalent equals, on average, 30.017 × 106 GJ of the thermal energy and 9.224 × 106 GJ of the electric energy in Lithuania. The total potential of biomethane production (taking into account crop production and animal husbandry wastes) on average equals 285.6 × 106 m3. The total potential of biohydrogen production on average equals 251.9 × 106 m3. The cost equivalents of the energy potential of agrarian biomass have been calculated. The average cost equivalent of the thermal energy could equal EUR 8.9 billion, electric energy—EUR 15.9 billion, biomethane—EUR 3.3 billion and biohydrogen—EUR 14.1 billion. The evaluation of the agricultural biomass potential as a source of renewable energy confirmed that Lithuania has a large biomass potential and satisfies the needs for the production of renewable energy. Thus, it is possible to move to the second step, that of making a decision concerning biomass conversion.
Green Hydrogen and Energy Transition: Current State and Prospects in Portugal
Authors: Bairrão Diego , Soares João , Almeida José , Franco John F. , Vale Zita
Published: January 2023

Hydrogen is a promising commodity, a renewable secondary energy source, and feedstock alike, to meet greenhouse gas emissions targets and promote economic decarbonization. A common goal pursued by many countries, the hydrogen economy receives a blending of public and private capital. After European Green Deal, state members created national policies focused on green hydrogen. This paper presents a study of energy transition considering green hydrogen production to identify Portugal’s current state and prospects. The analysis uses energy generation data, hydrogen production aspects, CO2 emissions indicators and based costs. A comprehensive simulation estimates the total production of green hydrogen related to the ratio of renewable generation in two different scenarios. Then a comparison between EGP goals and Portugal’s transport and energy generation prospects is made. Portugal has an essential renewable energy matrix that supports green hydrogen production and allows for meeting European green hydrogen 2030–2050 goals. Results suggest that promoting the conversion of buses and trucks into H2-based fuel is better for CO2 reduction. On the other hand, given energy security, thermoelectric plants fueled by H2 are the best option. The aggressive scenario implies at least 5% more costs than the moderate scenario, considering economic aspects.
Future Development of an Energy-Efficient Electric Scooter Sharing System Based on a Stakeholder Analysis Method
Authors: Macioszek Elżbieta , Cieśla Maria , Granà Anna
Published: January 2023

E-scooters as a new form of mobility are gaining more and more popularity. This popularity results from the flexibility of this mode of transport, but above all from the positive impact on the natural environment through the much higher energy efficiency of an e-scooter compared to a motor vehicle (according to the literature the rate is 2 km per kWh equivalent for a motor vehicle and the range is 90–100 km per kWh in the case of an e-scooter). This paper introduces a discussion on the future development of an energy-efficient electric scooter sharing system based on stakeholder analysis methods. The implementation of the e-scooter sharing system involves linking several areas of human activity, including social activity. This, in turn, relates to the interactions and building of relationships with entities, particularly those influencing the provision of services and their effects. The large number of entities and the complexity of the relations between them make it a challenge both to identify stakeholders in the development of the public e-scooter system and to indicate their roles in shaping the sustainable development strategy for urban mobility. The following study was based on the methodological foundations of stakeholder theory and social network analyses. The main research objective of the article is to identify and assign to different groups the stakeholders influencing the sustainable development of energy-efficient e-scooter sharing systems based on Polish cities. An evaluation was carried out using expert methods with a stakeholder analysis, based on matrix and mapping methods, and with the MACTOR application. Relationships and cooperation suggestions were established for each of the stakeholder groups, which could become an important part of the strategic approach to supporting public transport service providers and organizers, as well as allowing for further reductions in energy consumption in the city by introducing such services on a large scale. The cooperation of the entities participating in the implementation of bike-sharing services can contribute to their greater sustainable development and assurance using the new mobility modes, which consume less energy and at the same time make the city energy-efficient.
Energy Efficient Received Signal Strength-Based Target Localization and Tracking Using Support Vector Regression
Authors: Molla Jahir Pasha , Dhabliya Dharmesh , Jondhale Satish R. , Arumugam Sivakumar Sabapathy , Rajawat Anand Singh , Goyal S. B. , Raboaca Maria Simona , Mihaltan Traian Candin , Verma Chaman , Suciu George
Published: January 2023

The unpredictable noise in received signal strength indicator (RSSI) measurements in indoor environments practically causes very high estimation errors in target localization. Dealing with high noise in RSSI measurements and ensuring high target-localization accuracy with RSSI-based localization systems is a very popular research trend nowadays. This paper proposed two range-free target-localization schemes in wireless sensor networks (WSN) for an indoor setup: first with a plain support vector regression (SVR)-based model and second with the fusion of SVR and kalman filter (KF). The fusion-based model is named as the SVR+KF algorithm. The proposed localization solutions do not require computing distances using field measurements; rather, they need only three RSSI measurements to locate the mobile target. This paper also discussed the energy consumption associated with traditional Trilateration and the proposed SVR-based target-localization approaches. The impact of four kernel functions, namely, linear, sigmoid, RBF, and polynomial were evaluated with the proposed SVR-based schemes on the target-localization accuracy. The simulation results showed that the proposed schemes with linear and polynomial kernel functions were highly superior to trilateration-based schemes.
Simulating the Diffusion of Residential Rooftop Photovoltaic, Battery Storage Systems and Electric Cars in Italy. An Exploratory Study Combining a Discrete Choice and Agent-Based Modelling Approach
Authors: Danielis Romeo , Scorrano Mariangela , Massi Pavan Alessandro , Blasuttigh Nicola
Published: January 2023

Rooftop solar photovoltaic (PV) systems could significantly contribute to renewable energy production and reduce domestic energy costs. In Italy, as in other countries, the current incentives generate a modest annual increase after the generous fiscal incentives that kick-started the PV market in the 2008–2013 period. Several factors are, however, at play that can speed up the installation process, such as the improvements in PV technology at declining prices, the increased availability of battery-storage (BS) systems, the growing use of electric appliances, the uptake of electric cars, and the increased environmental awareness. We integrate two research methodologies, discrete choice modeling and agent-based modeling, to understand how these factors will influence households’ decisions regarding PV and BS installations and how agents interact in their socioeconomic environment. We predict that in Italy, given the preference structure of homeowners, the continuing decline in costs, and the social interaction, 40–45% of homeowners will have PV or PV and BS installed by 2030, thanks to the existing investment tax credit policy.
Thermal Disintegration of Sewage Sludge as a Method of Improving the Biogas Potential
Authors: Myszograj Sylwia , Płuciennik-Koropczuk Ewelina
Published: January 2023

Operating and research experiments indicate that the potential benefits of thermal treatment of sewage sludge before methane fermentation include increasing the biodegradability of substrates, reducing the amount and improving the dewaterability of the fermentate and its hygienization, reducing the emission of odours during stabilization, higher production of biogas, and improving the energy balance of the process. The process of disintegration (liquefaction) can be carried out, for example, through the use of mechanical homogenization, microwaves and ultrasonic waves, chemical agents, thermal methods, and biological processes. The article reviews the literature data on thermal hydrolysis research, from the first source information to the present. The thermal hydrolysis achieved enhanced hydrolysis, biogas potential, and faster sludge degradation during anaerobic digestion without compromising the quality of the end products.
Numerical Investigation and Optimization of Variable Guide Vanes Adjustment in a Transonic Compressor
Authors: Wang Ziyuan , Ren Xiaodong , Zhu Wei , Li Xuesong , Gu Chunwei
Published: January 2023

In the present work, numerical simulation and optimization was carried out to analyze the mechanism of the variable guide vanes (VGVs) of a transonic compressor. A seven-stage transonic compressor including three-stage VGVs was studied. The VGVs were adjusted individually and jointly under different IGV opening degrees. Changes in performance and shock wave were analyzed, and the coupling effect of the VGV joint adjustment was summarized. Aiming at the maximum efficiency, the joint turning angles were optimized. A novel phenomenon was found wherein the VGV adjustment can affect not only its own performance and that of adjacent downstream blades, but also that of upstream blades. Incidence and performance of upstream blades are improved, but those of the VGV and its adjacent downstream blades are deteriorated. VGV adjustment weakens the shock wave and shock-induced boundary layer separation. The optimal solution for VGV joint adjustment is the combination of the optimal solutions for single VGV adjustments. The joint adjustment optimization improves the efficiency by 0.2–1.93% under different IGV opening degrees.
Impact of Digital Economy on Energy Supply Chain Efficiency: Evidence from Chinese Energy Enterprises
Authors: Fu Shuke , Liu Jiabei , Tian Jiali , Peng Jiachao , Wu Chuyue
Published: January 2023

The global industrial chain and energy supply chain are being reconfigured at an accelerated pace, and the uncertainty of China’s energy supply security is growing significantly. Empowering energy supply chains through the digital economy (diec) has a positive effect on accelerating the transformation of China’s energy supply structure. This paper discusses the effect and mechanisms of the digital economy on energy supply chain efficiency (esce). Specifically, based on the panel data of 112 energy enterprises in China from 2011 to 2019, energy supply chain efficiency and digital economy at the enterprise level were evaluated through three-stage DEA and content analysis, respectively. A two-way fixed effects model and mediation effect mode were adopted to investigate the nexus of diec and esce. The results show that the digital economy improves energy supply chain efficiency, and the conclusion holds water even after a series of robustness tests and endogenous treatment. Meanwhile, its promotion effect is more significant among large enterprises, non-state enterprises and enterprises in high market-oriented regions. The main impact mechanisms are regional industrial agglomeration and technological innovation of enterprises. Based on the above conclusions, it is suggested to take advantage of the industrial aggregation effect and technological innovation effect of the digital economy to further improve the efficiency of the energy supply chain for the purpose of maintaining energy supply security.
Automatic Evaluation of an Interwell-Connected Pattern for Fractured-Vuggy Reservoirs Based on Static and Dynamic Analysis
Authors: Zhang Dongmei , Jiang Wenbin , Kang Zhijiang , Hu Anzhong , Wang Ruiqi
Published: January 2023

The types of fractured-vuggy reservoirs are diverse, with dissolution holes and fractures of different scales as the main reservoir spaces. Clarifying the connectivity between wells is crucial for improving the recovery rate of fractured-vuggy reservoirs and avoiding problems of poor water- flooding balance and serious water channeling. A traditional dynamic connected model hardly describes the geological characteristics of multiple media, such as karst caves and fractures, which cause multiple solutions from the calculation. The static analysis is the basis for connectivity evaluation. In this study, we designed an intelligent search strategy based on an improved A* algorithm to automatically find a large-scale fractured-vuggy connected path by seismic multi-attribute analysis. The algorithm automatically evaluates the interwell-connected mode and clarifies the relationship between the static connected channel and the fractured-vuggy space configuration. Restricted by various factors, such as seismic identification accuracy, a static connectivity study can hardly determine the filling and half-filling inside the channel effectively, even if it can identify the main connectivity channels. An injection-production response analysis based on dynamic production data can more accurately reflect the reservoir’s actual connectivity and degree of filling. This paper further studies dynamic response characteristics based on multifractals combined with production data. To reduce the evaluation uncertainty, we combined the static and dynamic connected analysis results to comprehensively evaluate the main connected modes, such as large fracture connectivity, cavern connectivity, and fractured-vuggy compound connectivity. We use the Tahe oilfield as an example to carry out an automatic evaluation of the connected pattern. The comprehensive evaluation results of the new algorithm were basically consistent with the tracer test results and can better reflect the interwell space-configuration relationship. Our model has certain guiding significance for the adjustment of working measures during waterflooding in fractured-vuggy reservoirs.
Analysis of the Influencing Factors of the Leak Detection Method Based on the Disturbance-Reflected Signal
Authors: Guo Dongsheng , Cui Zhaoxue , Liu Cuiwei , Li Yuxing
Published: January 2023

Leak detection technology, based on the disturbance-reflected signal, can realize pipeline state inspection without relying on the transient characteristics of leakage. However, the lack of research on the factors affecting the detection effect of this method greatly restricts its popularization and application. Therefore, this paper realizes the valve opening and closing through dynamic mesh technology and further establishes a 2D pipeline disturbance and reflection signal detection model. The correctness of the computational fluid dynamics (CFD) model detection mechanism was verified by theoretical analysis and indoor pipe flow experiments. In this process, it was found that reflections from boundaries, such as the pipe end, could also be identified and did not interfere with leak-related signals. In addition, the positioning errors of the leakage hole and the pipe end were 4.447% and 0.121%, respectively, and accurate positioning with zero error was able to be achieved in the calculation results of the CFD model. Finally, the influence factors of the detection effect of this method were analyzed by inputting the determined disturbance signal. Both the disturbance signal characteristics and the leakage hole characteristics affected the reflected signal, and the former played a more prominent role. Surprisingly, the results showed that pipeline flow and pressure had very limited influence on this method.
Detection of Outliers in Time Series Power Data Based on Prediction Errors
Authors: Li Changzhi , Liu Dandan , Wang Mao , Wang Hanlin , Xu Shuai
Published: January 2023

The primary focus of smart grid power analysis is on power load forecasting and data anomaly detection. Efficient and accurate power load prediction and data anomaly detection enable energy companies to develop reasonable production and scheduling plans and reduce waste. Since traditional anomaly detection algorithms are typically for symmetrically distributed time series data, the distribution of energy consumption data features uncertainty. To this end, a time series outlier detection approach based on prediction errors is proposed in this paper, which starts by using an attention mechanism-based convolutional neural network (CNN)-gated recursive unit (GRU) method to obtain the residual between the measured value and its predicted value, and the residual data generally conform to a symmetric distribution. Subsequently, for these residual data, a random forest classification algorithm based on grid search optimization is used to identify outliers in the power consumption data. The model proposed in this paper is applied to both classical and real energy consumption datasets, and the performance is evaluated using different metrics. As shown in the results, the average accuracy of the model is improved by 25.2% and the average precision is improved by 17.2%, with an average recall improvement of 16.4% and an average F1 score improvement of 26.8% compared to the mainstream algorithms.
A Second-Order Singular Perturbation for Model Simplification for a Microgrid
Authors: Yin Xiao-Qi , Yang Bao-Shun , Tao Jun
Published: January 2023

As the integration of electronic-interfaced devices have increased, microgrid models have become too complex to perform a stability analysis. Thus, an effective model simplification method keeping most dynamics of the system becomes very essential. Singular perturbation is a common way for model simplification. However, its accuracy is insufficient when nonlinear properties dominate. This is caused by the “Quasi-Steady State Assumption” that traditional singular perturbation holds. By assuming that microgrid can only be stabilized when fast variables stop variating, the traditional method ignores some common phenomena before a stabilization occurs, leading to a loss of dynamics. To improve the accuracy, this paper proposes a “second-order singular perturbation”. Here, the traditional “Quasi-Steady State” is updated to a scenario that third-order derivatives of fast variables become zero before the microgrid stabilizes. The updated assumption covers more common phenomena before a stabilization occurs. This leads to a more precise simplification. Simulation results indicate that the proposed method outperforms traditional singular perturbation in accuracy.
A Nonlinear Dynamic Model for Characterizing the Downhole Motions of the Sidetracking Tool in a Multilateral Well
Authors: Zhu Xiuxing , Zhou Weixia , Lei Yujian , Jia Peng , Xue Shifeng , Zhou Bo , Xia Yuanbo
Published: January 2023

It is of practical interest to investigate the mechanical behaviors of a sidetracking tool system and to describe the sidetracking tool’s vibration mechanical response, as this can provide an important basis for evaluating and optimizing the tool structure and effectively controlling the profile of the sidetracking window. In this article, three nonlinear dynamic models with ten, six, and two degrees of freedom, respectively, are established using the Lagrange method to characterize the behavior of the sidetracking tool. It should be noted that in these models, the axial, lateral, and torsional vibration of the tool system are fully coupled. The process of the sidetracking tool mills in the casing-pipe wall is divide into three typical stages, i.e., the window mill, pilot mill, and watermelon mill grinding the casing, respectively. The dynamic response of the three stages is studied to more effectively analyze the influence of the sidetracking tool vibration deformation on the window width. The Runge–Kutta method, which is easy to implement, is applied to solve the supposed nonlinear dynamic model, and some useful findings are as follows. The effects of sidetracking tool vibrations at different stages on window widening size are illustrated quantitatively. The vibration trajectory pattern of the sidetracking tool is different from that of the conventional drilling tool due to the influence of the whipstock, which changes from the general whirling motion pattern to the X reciprocating pattern, and the vibration amplitude decreases. Due to the influence of the tool’s lateral amplitude, the window profile is widened. The widened window size of the window mill and the pilot mill are 3.30 mm and 2.74 mm, respectively, and the extended window size of the watermelon mill is 0.07 mm, while the maximum window width formed by the sidetracking tool is 374.34 mm. This work proposes, for the first time, the coupled vibration model of the sidetracking tool system, which is helpful to better understand the nonlinear dynamic effects of the sidetracking tool, laying the foundation for the optimization of the sidetracking parameters.
Optimal Power Flow Solution for Bipolar DC Networks Using a Recursive Quadratic Approximation
Authors: Montoya Oscar Danilo , Gil-González Walter , Hernández Jesus C.
Published: January 2023

The problem regarding of optimal power flow in bipolar DC networks is addressed in this paper from the recursive programming stand of view. A hyperbolic relationship between constant power terminals and voltage profiles is used to resolve the optimal power flow in bipolar DC networks. The proposed approximation is based on the Taylors’ Taylor series expansion. In addition, nonlinear relationships between dispersed generators and voltage profiles are relaxed based on the small voltage voltage-magnitude variations in contrast with power output. The resulting optimization model transforms the exact nonlinear non-convex formulation into a quadratic convex approximation. The main advantage of the quadratic convex reformulation lies in finding the optimum global via recursive programming, which adjusts the point until the desired convergence is reached. Two test feeders composed of 21 and 33 buses are employed for all the numerical validations. The effectiveness of the proposed recursive convex model is verified through the implementation of different metaheuristic algorithms. All the simulations are carried out in the MATLAB programming environment using the convex disciplined tool known as CVX with the SEDUMI and SDPT3 solvers.
Interpretation and Quantification of the Flexibility Sources Location on the Flexibility Service in the Distribution Grid
Authors: Mehinovic Ajla , Zajc Matej , Suljanovic Nermin
Published: January 2023

The economic and technical requirements of current changes in the distribution system are reflected in the use of all available resources and the activation of mechanisms for local use of flexibility. Local flexibility markets are evolving and face numerous obstacles for which appropriate solutions must be found. The local flexibility market will be complemented by the development of a local flexibility register, which will contain all relevant information about the flexibility assets necessary for the efficient operation of the local flexibility market. In this paper, interpretation and quantification of the flexibility sources location on the flexibility service in the distribution grid is given. The information is derived from power flow simulation results and finally written down in the form of line coefficients, which are determined by applying the least squares method to the power flow results. We have developed a Python-based simulator to perform the methodology to determine the information and test it on a realistic medium voltage distribution grid in Bosnia and Herzegovina. This paper confirms the approximate linearity of the active power changes on the demand side to the line load and to the voltage at the nodes for a given operating condition of the distribution grid.
Study of a Pilot Scale Microbial Electrosynthesis Reactor for Organic Waste Biorefinery
Authors: Tian Jiang-Hao , Lacroix Rémy , Yaqoob Asim Ali , Bureau Chrystelle , Midoux Cédric , Desmond-Le Quéméner Elie , Bouchez Théodore
Published: January 2023

Microbial electrochemical technologies now enable microbial electrosynthesis (MES) of organic compounds using microbial electrolysis cells handling waste organic materials. An electrolytic cell with an MES cathode may generate soluble organic molecules at a higher market price than biomethane, thereby satisfying both economic and environmental goals. However, the long-term viability of bioanode activity might become a major concern. In this work, a 15-L MES reactor was designed with specific electrode configurations. An electrochemical model was established to assess the feasibility and possible performance of the design, considering the aging of the bioanode. The reactor was then constructed and tested for performance as well as a bioanode regeneration assay. Biowaste from an industrial deconditioning platform was used as a substrate for bioanode. The chemical oxygen demand (COD) removal rate in the anodic chamber reached 0.83 g day−1 L−1 of anolyte. Acetate was produced with a rate of 0.53 g day−1 L−1 of catholyte, reaching a maximum concentration of 8.3 g L−1. A potential difference (from 0.6 to 1.2 V) was applied between the bioanode and biocathode independent of reference electrodes. The active biocathode was dominated by members of the genus Pseudomonas, rarely reported so far for MES activity.