bluuebunny/crossref_metadata_2025 huggingface dataset:
Smart IoMT Framework for Supporting UAV Systems with AI
Authors: Shankar Nathan , Nallakaruppan Musiri Kailasanathan , Ravindranath Vaishali , Senthilkumar Mohan , Bhagavath Bhuvanagiri Prahal
Published: December 2022

The health monitoring system is one of the most innovative technologies that has gained traction in the Internet of Medical Things (IoMT). It allows the connection of multiple sensors and actuators that can capture and monitor the data through the web page or mobile application. IoMT technology not only provides communications but also will provide monitoring, recording, storage, and display. IoMT in healthcare is used for measuring the vital signs of the human body, which allows medical professionals to assess the well-being of a patient. The doctor may recommend lifestyle modifications, prescribe more tests, or diagnose a disorder according to the results. This paper illustrates the remote-control health monitoring system (HMS) with the integration of a UAV, which allows the doctor to access the data and analyze the patient data remotely. Thus, the proposed HMS-UAV system aims to measure the temperature, humidity, blood pressure, heart rate, and SpO2 and stores the data on the UAV. Several sensors were thus used namely DHT11, MAX30102, Myoware and K24C16, and the Raspberry Pi camera. Reduced hospital stays and avoidance of readmissions are benefits of remote patient monitoring with IoMT-based UAVs. Contrary to its advantages, IoMT has flaws in information processing since a huge volume of data are needed to be handled in a single environment. One major novel inclusion in this work is to measure multiple parameters and provide a comparative analysis for all of them. Furthermore, the functionality of video recorded and stored is included where the doctor can surveil the patient.
An Enhanced Deep Learning-Based DeepFake Video Detection and Classification System
Authors: Awotunde Joseph Bamidele , Jimoh Rasheed Gbenga , Imoize Agbotiname Lucky , Abdulrazaq Akeem Tayo , Li Chun-Ta , Lee Cheng-Chi
Published: December 2022

The privacy of individuals and entire countries is currently threatened by the widespread use of face-swapping DeepFake models, which result in a sizable number of fake videos that seem extraordinarily genuine. Because DeepFake production tools have advanced so much and since so many researchers and businesses are interested in testing their limits, fake media is spreading like wildfire over the internet. Therefore, this study proposes five-layered convolutional neural networks (CNNs) for a DeepFake detection and classification model. The CNN enhanced with ReLU is used to extract features from these faces once the model has extracted the face region from video frames. To guarantee model accuracy while maintaining a suitable weight, a CNN enabled with ReLU model was used for the DeepFake-detection-influenced video. The performance evaluation of the proposed model was tested using Face2Face, and first-order motion DeepFake datasets. Experimental results revealed that the proposed model has an average prediction rate of 98% for DeepFake videos and 95% for Face2Face videos under actual network diffusion circumstances. When compared with systems such as Meso4, MesoInception4, Xception, EfficientNet-B0, and VGG16 which utilizes the convolutional neural network, the suggested model produced the best results with an accuracy rate of 86%.
ROS System Facial Emotion Detection Using Machine Learning for a Low-Cost Robot Based on Raspberry Pi
Authors: Martínez Javier , Vega Julio
Published: December 2022

Facial emotion recognition (FER) is a field of research with multiple solutions in the state-of-the-art, focused on fields such as security, marketing or robotics. In the literature, several articles can be found in which algorithms are presented from different perspectives for detecting emotions. More specifically, in those emotion detection systems in the literature whose computational cores are low-cost, the results presented are usually in simulation or with quite limited real tests. This article presents a facial emotion detection system—detecting emotions such as anger, happiness, sadness or surprise—that was implemented under the Robot Operating System (ROS), Noetic version, and is based on the latest machine learning (ML) techniques proposed in the state-of-the-art. To make these techniques more efficient, and that they can be executed in real time on a low-cost board, extensive experiments were conducted in a real-world environment using a low-cost general purpose board, the Raspberry Pi 4 Model B. The final achieved FER system proposed in this article is capable of plausibly running in real time, operating at more than 13 fps, without using any external accelerator hardware, as other works (widely introduced in this article) do need in order to achieve the same purpose.
Training a Logic Dendritic Neuron Model with a Gradient-Based Optimizer for Classification
Authors: Song Shuangbao , Xu Qiang , Qu Jia , Song Zhenyu , Chen Xingqian
Published: December 2022

The logic dendritic neuron model (LDNM), which is inspired by natural neurons, has emerged as a novel machine learning model in recent years. However, recent studies have also shown that the classification performance of LDNM is restricted by the backpropagation (BP) algorithm. In this study, we attempt to use a heuristic algorithm called the gradient-based optimizer (GBO) to train LDNM. First, we describe the architecture of LDNM. Then, we propose specific neuronal structure pruning mechanisms for simplifying LDNM after training. Later, we show how to apply GBO to train LDNM. Finally, seven datasets are used to determine experimentally whether GBO is a suitable training method for LDNM. To evaluate the performance of the GBO algorithm, the GBO algorithm is compared with the BP algorithm and four other heuristic algorithms. In addition, LDNM trained by the GBO algorithm is also compared with five classifiers. The experimental results show that LDNM trained by the GBO algorithm has good classification performance in terms of several metrics. The results of this study indicate that employing a suitable training method is a good practice for improving the performance of LDNM.
Unsupervised Domain Adaptation for Image Classification Using Non-Euclidean Triplet Loss
Authors: Jabbar Sarhan Riyam , Balafar Mohammad Ali , Feizi Derakhshi Mohammad Reza
Published: December 2022

In recent years, computer vision tasks have increasingly used deep learning techniques. In some tasks, however, due to insufficient data, the model is not properly trained, leading to a decrease in generalizability. When trained on a dataset and tested on another similar dataset, the model predicts near-random results. This paper presents an unsupervised multi-source domain adaptation that improves transfer learning and increases generalizability. In the proposed method, a new module infers the source of the input data based on its extracted features. By making the features extractor compete against this objective, the learned feature representation generalizes better across the sources. As a result, representations similar to those from different sources are learned. That is, the extracted representation is generic and independent of any particular domain. In the training stage, a non-Euclidean triplet loss function is also utilized. Similar representations for samples belonging to the same class can be learned more effectively using the proposed loss function. We demonstrate how the developed framework may be applied to enhance accuracy and outperform the outcomes of already effective transfer learning methodologies. We demonstrate how the proposed strategy performs particularly well when dealing with various dataset domains or when there are insufficient data.
Effect of Coolant Temperature on Performance and Emissions of a Compression Ignition Engine Running on Conventional Diesel and Hydrotreated Vegetable Oil (HVO)
Authors: Mancarella Alessandro , Marello Omar
Published: December 2022

To meet future goals of energy sustainability and carbon neutrality, disruptive changes to the current energy mix will be required, and it is expected that renewable fuels, such as hydrotreated vegetable oil (HVO), will play a significant role. To determine how these fuels can transition from pilot scale to the commercial marketplace, extensive research remains needed within the transportation sector. It is well-known that cold engine thermal states, which represent an inevitable portion of a vehicle journey, have significant drawbacks, such as increased incomplete combustion emissions and higher fuel consumption. In view of a more widespread HVO utilization, it is crucial to evaluate its performance under these conditions. In the literature, detailed studies upon these topics are rarely found, especially when HVO is dealt with. Consequently, the aim of this study is to investigate performance and exhaust pollutant emissions of a compression ignition engine running on either regular (petroleum-derived) diesel or HVO at different engine thermal states. This study shows the outcomes of warm-up/cool-down ramps (from cold starts), carried out on two engine operating points (low and high loads) without modifying the original baseline diesel-oriented calibration. Results of calibration parameter sweeps are also shown (on the same engine operating points), with the engine maintained at either high or low coolant temperature while combustion phasing, fuel injection pressure, and intake air flow rate are varied one-factor at a time, to highlight their individual effect on exhaust emissions and engine performance. HVO proved to produce less engine-out incomplete combustion species and soot under all examined conditions and to exhibit greater tolerance of calibration parameter changes compared to diesel, with benefits over conventional fuel intensifying at low coolant temperatures. This would potentially make room for engine recalibration to exploit higher exhaust gas recirculation, delayed injection timings, and/or lower fuel injection pressures to further optimize nitrogen oxides/thermal efficiency trade-off.
Particle Size Distribution and Enrichment of Alkali and Heavy Metals in Fly Ash on Air and Oxy-Fuel Conditions from Sludge Combustion
Authors: Jang Ha-Na , Yoo Heung-Min , Choi Hang Seok
Published: December 2022

Comparative tests in air and oxy-fuel combustion were conducted in a 30 kWth circulating fluidized bed (CFB) pilot plant for waste sludge combustion. General combustion characteristics of the CFB, such as pressure profiles, temperatures along the bed, and flue gas composition, were different under the air and oxy-fuel conditions. At the bottom and in the fly ash, alkali and heavy metals had different distributions under the air and oxy-fuel combustion conditions. The particle size distribution in fly ash from air combustion was dominated by coarse particles, over 2.5 μm in size, whereas with oxy-fuel combustion, most particles were submicron in size, approximately 0.1 μm, and a smaller quantity of coarse particles, over 2.5 μm in size, formed than with air combustion. Mass fractions of Al, Ca, and K, below 2.5 μm in size, were found in the ashes from oxy-fuel combustion and in higher quantity than those found in air combustion. Submicron particle formation from Cr, Ni, Cu, and Zn in the fly ash occurred more during oxy-fuel combustion than it did in air combustion.
Using Simulation Modelling for Designing Optimal Strategies of Fuel Mix to Comply for SOx and NOx Emission Standards in Industrial Boilers
Authors: Kunche Akhil , Mielczarek Bozena
Published: December 2022

SOx and NOx emissions originating from the combustion of fuels during the operation of industrial boilers are one of the primary contributors to regional and local air pollution, which have major effects on human well-being and ecosystems. As governmental bodies attempt to regulate and enforce emission norms, the industries operating the boilers are presented with an opportunity to optimise their fuel mix configurations to achieve a reduction in SOx, NOx, and CO2 emissions while ensuring the financial sustainability of plant operations. In this study, a System Dynamic simulation model is constructed from the perspective of an individual plant to facilitate the analysis of SOx, NOx, and CO2 emissions and the expenditure incurred through energy use and pollution control systems under different fuel-mix configurations, market conditions, and policy scenarios. The model facilitates the identification of alternative fuel-mix configurations to replace existing single-fuel firing systems while also reducing both emissions and expenditure. To demonstrate the model, exemplar data based on prevalent market and policy conditions in India are used to identify alternative fuel-mixes for typical 40TPH boiler. The experiments additionally highlight the significance of having an appropriate penalty or tax on emissions to foster compliance with emission norms in the industry without adversely impacting their financial sustainability.
Unsteady Pressure Pulsations in Pumps—A Review
Authors: Zhang Ning , Li Delin , Gao Bo , Ni Dan , Li Zhong
Published: December 2022

Unsteady pressure-pulsation-induced severe vibration and high alternating stress can cause some unexpected results, including impeller crack and structural damage of the entire pumping system. In the present paper, a review on pressure pulsations in pumps is carried out based on the published studies. A comprehensive view on pressure pulsations from several aspects is discussed in detail. The contents of the studies include the mechanism of rotor–stator interaction; a prediction model and experimental and numerical investigations of pressure pulsations; unsteady rotating-stall-induced pressure pulsations at off-design flow rates; the relationship between pressure pulsation and the internal flow structure and cavitation; and the reduction in pressure pulsation caused by some effective approaches. It is concluded that unsteady pressure pulsation in pumps is closely associated with complex flow structures, for instance flow separation, cavitation, and rotating stall. The rotor–stator interaction mainly dominates pressure pulsation characterized by the discrete components in pressure spectrum. To reduce pressure pulsation, some effective approaches are proposed, such as increasing the rotor–stator gap, staggered blade, and blade modification. Finally, several suggestions for future works are given and discussed considering the current research. The review contributes to better understanding of pressure pulsations in centrifugal pumps, and may also benefit engineers in controlling pressure pulsations in some fields, such as pumps in nuclear reactor.
Functional Model of Power Grid Stabilization in the Green Hydrogen Supply Chain System—Conceptual Assumptions
Authors: Frankowska Marzena , Rzeczycki Andrzej , Sowa Mariusz , Drożdż Wojciech
Published: December 2022

Green hydrogen supply chain includes supply sources, production, and distribution of hydrogen produced from renewable energy sources (RES). It is a promising scientific and application area, as it is related to the problem of instability of power grids supplied with RES. The article presents the conceptual assumptions of the research on the design of a functional multi-criteria model of the stabilization model architecture of energy distribution networks based on a hydrogen energy buffer, taking into account the applicable use of hydrogen. The aim of the research was to identify the variables contributing to the stabilization of the operation of distribution networks. The method used to obtain this result was a systematic review of the literature using the technique of in-depth analysis of full-text articles and expert consultations. The concept of a functional model was described as a matrix in two dimensions in which the identified variables were embedded. The first dimension covers the phases of the supply chain: procurement and production along with storage and distribution. The second dimension divides the separate factors into technical, economic, and logistic. The research was conducted in the context of system optimization from the point of view of the operator of the energy distribution system. As a result of the research, several benefits resulting from stabilization using a hydrogen buffer were identified. Furthermore, the model may be used in designing solutions stabilizing the operation of power grids in which there are surpluses of electricity produced from RES. Due to the applied multidimensional approach, the developed model is recommended for use, as it enables the design of solutions in a systemic manner. Due to the growing level of energy obtained from renewable energy sources, the issue of stabilizing the energy network is becoming increasingly important for energy network distributors.
Hotspots and Tendencies of Energy Optimization Based on Bibliometric Review
Authors: Hanafi Issam , Samoudi Bousselham , Ben Halima Ahlem , Canale Laurent
Published: December 2022

Due to the highly recent innovations in energy optimization research, this study presents a systematic overview of the area, exposing gaps and interesting directions for future research. The current study investigated the trend of research growth using 24,946 research paper with 731,873 references data gathered from the Web of Science Core Collection database (WOS), from the period between 1990 and September 2022, using bibliographic coupling and keyword co-occurrence. The aim of this research is to analyze bibliometric efficiency criteria, such as publishing and citation trends, the top ten institutions, countries, and the most frequently referenced articles. The Matlab and Vos viewer software illustrate the current state of visualization research articles. Science maps as result of indicators, such as the most often used keywords plus and keywords, citation and co-citation, and collaboration is also being established for in-depth research. The study findings enable us to conclude that the number of publications in this field show that the importance has grown tremendously over the last 30 years, with China, United States, Germany, and India emerging as the countries that publish the most. The top ten researchers in led energy optimization were also identified. Academics should become acquainted with the hotspots in this discipline as soon as possible. This is also the first study to use bibliometric research to thoroughly evaluate the energy optimization literature. It will help researchers widen their knowledge and quickly comprehend the present condition and trend of development.
A Review on Optimization Objectives for Power System Operation Improvement Using FACTS Devices
Authors: Mirsaeidi Sohrab , Devkota Subash , Wang Xiaojun , Tzelepis Dimitrios , Abbas Ghulam , Alshahir Ahmed , He Jinghan
Published: December 2022

In recent decades, the rapid rise in electricity demand has compelled transmission and distribution systems to operate at almost their maximum capacity. This can pose numerous technical challenges such as excessive power losses, voltage and transient instabilities, as well as reduced power quality and reliability. Employment of Flexible Alternating Current Transmission System (FACTS) devices can be an effective approach to obviate such challenges and reinforce the power system functionality. Nevertheless, FACTS devices require a high initial investment, and hence their optimal allocation in terms of various aspects such as type, size and location is of utmost importance. This cannot be achieved without the deployment of optimization techniques. The aim of this paper is to provide a comprehensive review of the existing proposals for the enhancement of power system performance adopting FACTS devices. Adhering to that, an in-depth analysis is carried out, in which the most pertinent options are classified into specific groups based on their optimization objectives. Finally, a comparative analysis is accomplished in which the main attributes and drawbacks of each optimization technique are presented.
A Study of Digital Measurement and Analysis Technology for Transformer Excitation Magnetizing Curve
Authors: Chen Chien-Hsun , Chou Chih-Ju
Published: December 2022

Transformer excitation magnetizing curves (TEMC) reflect the dynamic operation characteristics of iron-core materials. Using numerical analysis and the waveform recording function of digital oscilloscopes, we developed a cost-effective method for determining the TEMC. This approach eliminates the need for conventional analog integrator circuits. To address the potential obstacles to the digital generation of TEMC—namely, curve offsets and curve transients—we proposed solutions involving Fourier filtering and determining the initial point of integration. The results indicate that the proposed approach yields results consistent with those of conventional analog integrator circuits and highlight its promise for applications in data processing.
How to Assess the Carbon Footprint of a Large University? The Case Study of University of Bologna’s Multicampus Organization
Authors: Battistini Roberto , Passarini Fabrizio , Marrollo Rita , Lantieri Claudio , Simone Andrea , Vignali Valeria
Published: December 2022

University campuses represent a heterogeneous ecosystem as to social, economic, energetic, and personal travel planning with a huge impact on hosting cities and territories. Sustainable policies are thus fundamental to reduce this impact and to adopt ecological behaviors. The measures for any University Sustainability Plan should be evaluated in terms of GHG emissions, as well as the overall impact of the university itself. Carbon footprint (CF) calculation is a relevant Decision Support tool that allows university organizations to measure and communicate the environmental effects of their activities. The aim of this paper is to present a carbon footprint methodology specifically designed to calculate the carbon footprint of large universities. The methodology was applied to calculate the CF of the University of Bologna by following international standards—i.e., the GHG protocol, the ISO 14064, and the ISO/TR 14069 guide—to understand the environmental impact caused by greenhouse gas emissions from direct and indirect university activities. The study was conducted upon the data available in 2020 and then was compared to the 2018 data, with the aim to recognize if the effect of the pandemic could have altered the results. In 2020, the University of Bologna emitted 16,467 tCO2e which became 15,753 tCO2e considering the offset and avoided emission provided by the internal production of energy from renewable sources. Comparison between 2020 and 2018 shows how, in 2018, most of the emissions came from transportation, representing 74% of the total emissions, while in 2020 almost 50% of total emissions derived by IT procurements. The case application demonstrates the way with which the methodology may be applied to assess environmental impact for complex university campuses.
Evaluation of Bi-Functional Electrochemical Catalytic Activity of Co3O4-CoFe2O4 Composite Spinel Oxide
Authors: Park Ji-Woo , Ju Young-Wan
Published: December 2022

Oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) are important for developing energy systems such as fuel cells and metal–air batteries. Precious metal catalysts, such as Pt and IrO2, have been considered electrochemical catalysts because of their excellent activity for the ORR and OER. However, their disadvantages, such as low durability for long-term operation and high price, necessitate the development of alternative electrochemical catalysts. Transition metal oxides with excellent electrical conductivity, high efficiency, and stability have been considered alternative electrochemical catalysts owing to their ORR and OER activities, which are similar to those of precious metal catalysts. Therefore, in this study, composite catalyst materials comprising Co3O4 and CoFe2O4 spinel oxides were synthesized via hydrothermal synthesis. The synthesized composite oxides exhibit bi-functional electrochemical catalytic activity for ORR and OER owing to the large active surface area and increased number of oxygen vacancies via the nanostrain in Co3O4 nanoparticles.
Energy Performance Analysis of a Heat Supply System of a University Campus
Authors: Bendea Gabriel , Felea Ioan , Hora Cristina , Bendea Codruta , Felea Adrian , Blaga Alin
Published: December 2022

The energy efficiency of a system and the performance level of its equipment and installations are the two key elements based on which the investment decision in its modernization is made. They are also very important for setting up optimal operation strategies. The energy audit is a well-known and worldwide recognized tool for calculating energy performance indicators and developing improvement measures. This paper is a synthesis of the energy audit results performed for a district heating network that uses geothermal energy as its primary source of energy. The location of the heating system is inside a university campus. The first part explains the necessity of a comprehensive study on district heating networks and introduces the defining elements that characterize the analyzed equipment and installations. The complex energy balance methodology that has been developed and applied to this district heating system is presented in the second part of the paper. Next, the methodology for collecting the input data for the energy and mass balance is explained. In the final part, the numerical values of the performance indicators and the technical measures that must be applied to improve energy efficiency are shown, and conclusions are drawn.
Valorizing Biodiesel and Bioethanol Side-Streams: Sustainability Potential Assessment through a Multicriteria Decision Analysis Framework and Appraisal of Valuable Compound Recovery Prospects
Authors: Macias Aragonés Marta , Ondrejíčková Petra , Ugarte Lodeiro Raul , Arroyo Torralvo Fátima
Published: December 2022

A framework for assessing, from a sustainability and circular bioeconomy point of view, the best valorization approach for biorefineries side-streams has been developed and validated. Two biorefinery side streams are considered as case-studies for validation: rapeseed meal from biodiesel and corn oil from bioethanol production. Firstly, a methodology to evaluate different valorization scenarios has been developed following a holistic approach that addresses technical aspects, environmental impact, and economic analysis. This way, a framework (inspired by the Battelle Method and using insights from Multicriteria Decision Analysis) has been produced where the sustainability potential of each scenario can be assessed. Such framework has been validated for five valorization scenarios for rapeseed meal and seven scenarios for corn oil. It can be concluded that protein extraction through alkaline (NaOH) hydrolysis is the best approach for rapeseed meal valorization while carotenoids recovery through ion exchange extraction is the most suitable strategy for corn oil. Secondly, for the selected scenarios, an estimation of the maximum recoverable amount of valuable compounds is conducted at the European and country-level. The use of this framework substantially aids in the best choice of the cutting-edge conversion technologies, supporting industry practitioners in the selection of processes to be further scaled-up.
Characteristics of Real-World Non-Exhaust Particulates from Vehicles
Authors: Mun Sunhee , Chong Hwansoo , Lee Jongtae , Lim Yunsung
Published: December 2022

The need to regulate the non-exhaust particulate matter (PM) emissions from vehicles has been discussed worldwide due to the bad environmental impact and the toxicity to the human body. In-depth studies have been precisely conducted on the analysis of the non-exhaust particulate matters, in particular, the amount of tire, brake and road wear particles and their proportion in the atmosphere. In this study, the influence of tire and road wear particles (TRWP) on PM in the atmosphere was investigated with tire and PM samples. The PM samples suspended in the atmosphere were collected with a high-volume sampler equipped with a quartz filter. Additionally, polycyclic aromatic hydrocarbons (PAHs) and metal components in tire rubber were analyzed as markers by pyrolysis–gas chromatography/mass spectrometry (pyrolysis–GC/MS), GC/MS, and inductively coupled plasma/mass spectrometry (ICP/MS). More vinylcyclohexene was detected than dipentene in the markers measured in the samples of tires equipped with vehicles driving on the road, while more dipentene was measured in total suspended particles (TSP) samples. Among the PAHs in tire samples, pyrene exhibited the highest concentration. Benzo(b)fluoranthene showed the highest concentration in the TSP samples. Among the metals, the highest concentration was zinc in all tire samples and calcium in TSP samples.
Energy and Environmental Assessment of Cogeneration in Ceramic Tiles Industry
Authors: Ancona Maria , Branchini Lisa , Ottaviano Saverio , Bignozzi Maria , Ferrari Benedetta , Mazzanti Barbara , Salvio Marcello , Toro Claudia , Martini Fabrizio , Benedetti Miriam
Published: December 2022

Ceramic tile manufacturing is a highly energy-intensive process. Concerns about carbon emissions and energy costs make energy management crucial for this sector, which holds a leading role in Italian industry. The paper discusses the energetic and environmental performance of cogeneration (CHP) in the ceramic industry, where prime mover exhaust heat is supplied to a spray-dryer system, contributing to the satisfaction of the thermal demand and decreasing natural gas consumption. A thermodynamic model of a dryer unit, validated against real data, has been set-up to provide a detailed representation of the thermal fluxes involved in the process. Then, the thermal integration with two types of CHP prime movers of similar electric size (4 MW) is investigated. Energetic results show that the gas turbine can contribute up to 81% of dryer thermal consumption, whilst internal combustion engine contribution is limited to 26%. A methodology was ad-hoc defined for the environmental assessment of CHP, accounting for global (CO2) and local (CO and NOX) emissions. Results confirm that CHP units guarantee reduction of CO2 and NOX compared to separate generation, with maximum values equal to 81 g/kWhth and 173 mg/kWhth, respectively; CO emission is decreased only in the case of gas turbine operation, with savings equal to 185 mg/kWhth.
A Method of Multi-Criteria Assessment of the Building Energy Consumption
Authors: Stachera Aleksandra , Stolarski Adam , Owczarek Mariusz , Telejko Marek
Published: December 2022

The aim of this study is to develop a universal method for the unequivocal selection of the optimal combination of components determining the energy efficiency of buildings by means of an introduced global building energy consumption indicator. The basis of this method is a multi-criteria optimization of the components influencing the energy efficiency of buildings. The method requires the development of a detailed description of the set of components influencing the energy efficiency of buildings and the definition of the analysis criteria. The following analysis criteria were adopted: relative annual demand for final energy, cost, durability, and investment outlays payback time. The normalized values of these criteria are calculated in relation to the structure of a reference building. The proposed method makes it possible to explicitly indicate the most advantageous solution from the point of view of the weighted share of the individual criteria. The verification of the method was presented on the basis of the thermo modernization case study analysis for the adopted reference building. As a result, the most advantageous variant of modernization was determined, defined by the lowest value of the global building energy consumption indicator. A high effectiveness of the proposed method has been shown in relation to both the selection of the modernization option and the assessment of the individual component share in each modernization solution.
Optimal Scheduling of Battery-Swapping Station Loads for Capacity Enhancement of a Distribution System
Authors: Alharbi Walied , Humayd Abdullah S. Bin , R. P. Praveen , Awan Ahmed Bilal , V. P. Anees
Published: December 2022

A battery-swapping station (BSS) can serve as a flexible source in distribution systems, since electric vehicle (EV) batteries can be charged at different time periods prior to their swapping at a BSS. This paper presents an EV battery service transformation from charging to swapping batteries for EVs for the capacity enhancement of a distribution system. A novel mathematical model is proposed to optimally quantify and maximize the flexibility of BSS loads in providing demand response for the utility operator while considering technical operations in the distribution grid. Case studies and numerical findings that consider data from the National Household Travel Survey and a 32-bus distribution system are reported and discussed to demonstrate the effectiveness of the proposed model. Offering battery-swapping services helps reduce not only the peak load, but also the station operation cost.
Machine Learning Predictions of Electricity Capacity
Authors: Harris Marcus , Kirby Elizabeth , Agrawal Ameeta , Pokharel Rhitabrat , Puyleart Francis , Zwick Martin
Published: December 2022

This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This research accomplishes these aims. The models built in this paper identify wind forecast, sunrise/sunset and the hour of day as primary predictors of net load imbalance, among other variables, and show that the average size of the INC and DEC capacity requirements can be reduced by over 25% with the margin of error currently used in the industry while also significantly improving closeness and exceedance metrics. The reduction in INC and DEC capacity requirements would yield an approximate cost savings of $4 million annually for one of nineteen Western Energy Imbalance market participants. Reconstructability Analysis performs the best among the machine learning methods tested.
An Overview of Current Printing Technologies for Large-Scale Perovskite Solar Cell Development
Authors: Valsalakumar Sreeram , Roy Anurag , Mallick Tapas K. , Hinshelwood Justin , Sundaram Senthilarasu
Published: December 2022

The power conversion efficiencies (PCEs) of Perovskite solar cells (PSCs) have seen significant performance improvements between 2012 and 2022. PSCs have excellent optoelectronic properties and can be built using low-cost materials. In order to compete with first-generation photovoltaic technologies, it will be necessary to scale up production. This review article explores the advancements in several scalable perovskite deposition techniques, including recent developments in the fabrication of high-quality perovskite film, their stabilities and commercialization status. Several scalable deposition techniques are discussed, including user-friendly solution-techniques (spin coating, slot die coating, etc.), vapour-assisted deposition approaches in the laboratory and full-scale commercial applications. The aforementioned deposition techniques have advantages compared to deposition techniques based on cost, effective mask-less patterning and unparalleled-design freedom. Other potential advantages include optimal use of materials, scalability, contactless deposition in high-resolution and a rapid transformation from small laboratory-scale work to large industrial-scale roll-to-roll production. Most recent technological advancements and structural developments relate to long-term thermal stability and moisture resistance. Many of the developments are still in the evolving field of lab-scale devices. The improvement roadmap and commercialization aspects of PSC manufacture involve two significant milestones: bridging the gap between the performance characteristics of small-scale and large-scale devices and the scalable printing techniques for all the layers in the device.
New Trends and Prospects for Developing Local Power Sources Based on Fuel Cells and Power Storage Units for Critical Infrastructure Customers
Authors: Loskutov Alexey , Kurkin Andrey , Shalukho Andrey , Lipuzhin Ivan
Published: December 2022

A reliable and efficient power supply for critical infrastructure customers is key to ensuring energy security. Critical infrastructure requires local power sources. Currently, performance requirements for such sources have significantly increased. Apart from high energy efficiency, important requirements include quick start-up time, small size, environmental friendliness, low noise, etc. These may be provided by fuel cells, which are considered the most prospective sources of electric power. However, it is necessary to overcome a number of obstacles limiting fuel cell efficiency in power supply systems for critical infrastructure customers. This paper presents the results of design analysis in the field of fuel cell, hydrogen conversion and power storage technologies. An assessment is given of promising studies aimed at combining the abovementioned technologies to create local power sources to ensure reliable power supply to critical infrastructure objects.
Classification of Partial Discharges Recorded by the Method Using the Phenomenon of Scintillation
Authors: Płużek Aleksandra , Nagi Łukasz
Published: December 2022

Classification is one of the most common methods of supervised learning, which is divided into a process of data acquisition, data mining, feature analysis, machine learning algorithm selection, model learning and validation, as well as prediction of the result, which was done in the current work. The data that were analyzed concerned ionizing radiation signals generated by partial discharges, recorded by a method using the phenomenon of scintillation. It was decided to check if the data could be classified and if it was possible to determine the defect of an electrical power device. It was possible to find out which classifier (algorithm) worked best for the task, and that the data obtained can be classified, as well as that it is possible to determine the defect. In addition, it was possible to check what effect changing the default values of the classifier’s parameters has on the effectiveness of classification.
Consumer Adoption of Electric Vehicles: A Systematic Literature Review
Authors: Bryła Paweł , Chatterjee Shuvam , Ciabiada-Bryła Beata
Published: December 2022

Electric vehicle (EV) disposition may challenge serious environmental issues such as excessive dependence on oil, especially in the transport sector. Despite this understanding, the adoption intention has been disappointing to date. This review tries to present a comprehensive overview of the methodologies, theories, and variables used in 57 peer-reviewed articles published between 2015 and 2022 covering the main forms of consumer adoption of EVs, consisting of purchase as well as behavioral and usage intentions. Governments may stimulate consumer adoption of EVs with exemptions on roadway tolls, convenient access to charging infrastructures, and tax and economic incentives considering energy trading and vehicle sharing. Second, it is important to create intensive awareness revolving around the EV segment. Furthermore, respecting and understanding consumer preferences would also pave the way for the success of EV acceptance. Finally, consumers’ risk–benefit belief while adopting new technology will play a significant role in choosing an EV in the future, while the biggest barriers to adopting EV will be consumers’ lack of confidence in EV performance, safety standard measures, and range per charge.
An Application of Relative Entropy in Structural Safety Analysis of Elastoplastic Beam under Fire Conditions
Authors: Kamiński Marcin , Strąkowski Michał
Published: December 2022

The main aim of this work is to propose a new algorithm of reliability assessment for steel civil engineering structures subjected to fire temperatures. This new algorithm is based upon the relative probabilistic entropy concept elaborated by Bhattacharyya, and this probabilistic distance is sought in-between extreme and admissible deformations of some structural beam subjected to higher temperatures. Similar to the First Order Reliability Method, this approach uses the first two probabilistic characteristics of the structural response, when structural output may be modelled with the use of Gaussian distribution. The probabilistic structural response is found here using hybrid computational technique–the Finite Element Method system ABAQUS with its fully coupled thermo-elastic analysis with 3D solid elements and probabilistic modules implemented in the computer algebra system MAPLE. The probabilistic response is determined via a triple stochastic analysis, which is based on the classical Monte-Carlo simulation, iterative generalized stochastic perturbation technique, and also using semi-analytical probabilistic calculus. Final determination of the relative entropy in the Serviceability Limit State of the given structure and its comparison with the results obtained using the FORM analysis enables to calibrate this new technique to numerical values proposed in the engineering designing codes. Hence, a more accurate probabilistic method may use some experimental-based admissible values included in the existing design of legal provisions.
Production of Fuel Range Hydrocarbons from Pyrolysis of Lignin over Zeolite Y, Hydrogen
Authors: Ali Ghulam , Afraz Marrij , Muhammad Faisal , Nisar Jan , Shah Afzal , Munir Shamsa , Tasleem Hussain Syed
Published: December 2022

In the current study, plain and lignin loaded with Zeolite Y, hydrogen was decomposed in a pyrolysis chamber. The reaction parameters were optimized and 390 °C, 3% catalyst with a reaction time of 40 min were observed as the most suitable conditions for better oil yield. The bio-oil collected from the catalyzed and non-catalyzed pyrolytic reactions was analyzed by gas chromatography mass spectrometry (GCMS). Catalytic pyrolysis resulted in the production of bio-oil consisting of 15 components ranging from C3 to C18 with a high percentage of fuel range benzene derivatives. Non-catalytic pyrolysis produced bio-oil that consists of 58 components ranging from C3 to C24; however, the number and quantity of fuel range hydrocarbons were lower than in the catalyzed products. The pyrolysis reaction was studied kinetically for both samples using thermogravimetry at heating rates of 5, 10, 15 and 20 °C/min in the temperature range 20–600 °C. The activation energies and pre-exponential factors were calculated using the Kissinger equation for both non-catalytic and catalytic decomposition and found to be 157.96 kJ/mol, 141.33 kJ/mol, 2.66 × 1013 min−1 and 2.17 × 1010 min−1, respectively. It was concluded that Zeolite Y, hydrogen worked well as a catalyst to decrease activation energy and enhance the quality of the bio-oil generated.
Electric Vehicle Charging Schedules in Workplace Parking Lots Based on Evolutionary Optimization Algorithm
Authors: Poniris Stavros , Dounis Anastasios I.
Published: December 2022

The electrification of vehicles is considered to be the means of reducing the greenhouse gas (GHG) emissions of the transport sector, but “range anxiety” makes most people reluctant to adopt electric vehicles (EVs) as their main method of transportation. Workplace charging has been proven to counter range anxiety and workplace charging is becoming quite common. A workplace parking lot can house hundreds of EVs. In this paper, a program has been developed in MATLAB that uses the well-known evolutionary optimization algorithm, the genetic algorithm (GA), to optimize the charging schedule of fifty EVs that aims at achieving three goals: (a) keeping the electricity demand low, (b) reducing the cost of charging and (c) applying load shifting. Three schedules were developed for three scenarios. The results demonstrate that each schedule was successful in achieving its goal, which means that scheduling the charging of a fleet of EVs can be used as a method of demand-side management (DSM) in workplace parking lots and at the same time reduce the energy cost of charging. In the scenarios examined in this paper, cost was reduced by approximately 2%.
Countercurrent Flow Limitation in a Pipeline with an Orifice
Authors: Zhao Danni , Xu Chende , Wang Zhengguang , Zhu Xixi , Li Yaru , Chi Xiangyu , Wang Naihua
Published: December 2022

Countercurrent flow limitation (CCFL) refers to an important class of gravity-induced hydrodynamic processes that impose a serious restriction on the operation of gas–liquid two-phase systems. In a nuclear power plant, CCFL may occur in the liquid level measurement system where an orifice is applied in the pipeline, which may introduce error into the level measurement system. CCFL can occur in horizontal, vertical, inclined, and even much more complicated geometric patterns, and the hot-leg channel flow passage has been widely investigated; however, a pipeline with variable cross-sections, including an orifice, has not yet been investigated. An experimental investigation has been conducted in order to identify the phenomenon, pattern, and mechanism of CCFL onset in this type of geometry. Both visual and quantified experiments were carried out. A high-speed camera was applied to capture the flow pattern. Visual experiments were implemented at atmospheric pressure, while quantified pressurizer experiments were implemented at higher pressures. It was determined that if the condensate drainage is low and the liquid level is also low, with a stable stratified flow upstream of the orifice, there is no oscillation of the differential pressure. However, at higher condensate drainage levels, when the liquid level increases, a stratified wavy flow occurs. One of these waves can suddenly rise upstream of the orifice to choke it, which subsequently gives rise to differential pressure across the orifice, with periodic variation. This pattern alternately features stratified flow, stratified wavy flow, and slug flow, which indicates the occurrence of CCFL. The CCFL occurring under these experimental conditions can be expressed as a Wallis type correlation, where the coefficients m and C are 0.682 and 0.601, respectively.