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SRMs have high torque ripples. Modifying the stator and rotor shape of SRMs is one of their methods to remove torque ripples. In this study, new rotor and stator geometries have been proposed for SRM. Performance characteristics are obtained and dynamic analyses are carried out for both novel and classical 6/4 SRMs. FFT analyses of the torque characteristics are also performed for the both SRMs. The changes in performance and the FFT results of torque characteristics are determined for both the current-controlled and uncontrolled operations in both SRMs. 23.60% and 15.20% improvements in torque ripples are achieved for the uncontrolled and current-controlled operations in SRMs respectively. When the FFT analyses are applied to the torque characteristics, the THD values are obtained as 21.19% and 8.31% in the uncontrolled and controlled motor current operations respectively. A 122 W three-phase 6/4 SRM simulation based on the MATLAB–SIMULINK environment is performed to validate the improvement of the torque ripple of the proposed model.

Poly(ethylene oxide) (PEO)-based polymer electrolytes are a promising class of materials for use in lithium-ion batteries due to their high ionic conductivity and flexibility. In this study, the effects of polymer architecture including linear, star, and hyperbranched and salt (lithiumbis(trifluoromethanesulfonyl)imide (LiTFSI)) concentration on the glass transition (Tg), microstructure, phase diagram, free volume, and bulk viscosity, all of which play a significant role in determining the ionic conductivity of the electrolyte, have been systematically studied for PEO-based polymer electrolytes. The branching of PEO widens the liquid phase toward lower salt concentrations, suggesting decreased crystallization and improved ion coordination. At high salt loadings, ion clustering is common for all electrolytes, yet the cluster size and distribution appear to be strongly architecture-dependent. Also, the ionic conductivity is maximized at a salt concentration of [Li/EO ≈ 0.085] for all architectures, and the highly branched polymers displayed as much as three times higher ionic conductivity (with respect to the linear analogue) for the same total molar mass. The architecture-dependent ionic conductivity is attributed to the enhanced free volume measured by positron annihilation lifetime spectroscopy. Interestingly, despite the strong architecture dependence of ionic conductivity, the salt addition in the highly branched architectures results in accelerated yet similar monomeric friction coefficients for these polymers, offering significant potential toward decoupling of conductivity from segmental dynamics of polymer electrolytes, leading to outstanding battery performance.

© 2022 The Author(s)Battery energy storage systems (BESS) are essential for flexible and reliable grid performance as the number of renewable energy sources in grids rises. The operational life of the batteries in BESS should be taken into account for maximum cost savings, despite the fact that they are beneficial for economical grid operation. In this context, this paper present a new battery cycle counting perspective for energy management of grid-connected BESS. For this purpose battery\"s one full charge–discharge cycle characteristic is compared with the operating battery charge–discharge cycle every time step. This comparison was explained mathematically and graphically in detail. The results are compared with the rain flow counting method which is the most popular cycle counting algorithm. Consequently, this cycle counting approach successfully counts the battery charge/discharge cycles and it has shown that has an advantage for BESSs due to being specifically developed just for batteries.

The cold filter plugging point (CFPP) value of biodiesel fuel blends need to be improved because of it has been reported that utilizing high concentrations of biodiesel blends in cold climates can cause major issues. Therefore, the main objective of the study is to improve CFPP value of biodiesel blends using ethyl proxitol additive. It is also to examine combustion and emission of the blends behavior. Biodiesel blends were prepared as; 5% ethyl proxitol-10% biodiesel-85% ultra-low sulfur diesel (ULSD) named as E5B10 and 5% ethyl proxitol-20% biodiesel-75% ULSD named as E5B20 fuel. These fuel blends were compared with ULSD fuel in a compression ignition engine at a constant engine speed of 1500 rev/min and four different loads (idle, 1, 2, and 3 bar BMEP) of eddy current dynamometer. The addition of ethyl proxitol led to a decrease in in-cylinder pressure and temperature due to an about 1˚ later SOC and 2˚ earlier EOC compared to ULSD. The pressure rise rate was slightly higher compared to ULSD at both low and high loads, with a difference of about 1 bar per degree. As a result, these blends with improved CFPP value can be used in cold climates.

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which emerged in 2019, cut the epoch that will make profound fluctuates in the history of the world in social, economic, and scientific fields. Urgent needs in public health have brought with them innovative approaches, including diagnosis, prevention, and treatment. To exceed the coronavirus disease 2019 (COVID-19) pandemic, various scientific authorities in the world have procreated advances in real time polymerase chain reaction (RT-PCR) based diagnostic tests, rapid diagnostic kits, the development of vaccines for immunization, and the purposing pharmaceuticals for treatment. Diagnosis, treatment, and immunization approaches put forward by scientific communities are cross-fed from the accrued knowledge of multidisciplinary sciences in health biotechnology. So much so that the pandemic, urgently prioritized in the world, is not only viral infections but also has been the pulsion in the development of novel approaches in many fields such as diagnosis, treatment, translational medicine, virology, microbiology, immunology, functional nano- and bio-materials, bioinformatics, molecular biology, genetics, tissue engineering, biomedical devices, and artificial intelligence technologies. In this review, the effects of the COVID-19 pandemic on the development of various scientific areas of health biotechnology are discussed.

In recent years, higher penetrations of renewable energy generations helped persuade sustainable energy and environmental targets, although their intermittent and fluctuated nature raised technical challenges. However, hybrid power system can minimize these technical issues by integrating flexible generation capable renewable energy sources like biomass. This study deals with optimizing gridintegrated and stand-alone biomass-based hybrid power system for the energy demand of a rural region containing poultry farms. Besides solar and wind energy, energy storage integration is evaluated regarding overall technical-environmental-economic performance, considering the actual manure potential using HOMER Pro. In addition, sensitivity analyses are performed, considering the estimated load and inflations using an artificial neural network method. Using biomass and solar hybrid options offers more autonomous, environmentally friendly, and economic advantages. Biomass-based hybrid power system with solar energy reduced net present cost by around 12% and increased renewable fraction by 7%, and grid-connected options can provide 88.9% renewable fraction. In addition, the energy storage integration increased renewable fraction by around 10% and reduced excess energy by 16%. The proposed biomass-based hybrid power system achieves cost-effective sizing of solar or wind-based hybrid systems, empowers the reliability of renewable energy and presents good consistency of decarbonization targets.

The main outdoor factors that reduce the efficiency of the photovoltaic (PV) panel are the reflection and refraction of light, dirt, dust, and organic waste accumulating on the panel surface. In this article, an antireflection, self-cleaning coating was applied on the PV panel cover glass with a new method. With the coating, the surface has been given a hydrophobic feature. As a coating method, a 3-D printer has not been seen in the literature and used as a new method. The electrospinning method has also been tried as an alternative method. Solutions in different combinations were developed using polylactic acid or polymethylmethacrylate polymer, chloroform ($\text{CHCl}_3$) as a solvent, and silicon dioxide ($\text{SiO}_2$) and titanium dioxide ($\text{TiO}_2$) nanoparticles as primary materials in a modified 3-D printer for bioprinting. Five PV panels were obtained by applying different 3-D parameters from three solutions, which have the best results. Coating thicknesses are in the range of 3.12-8.47 mu m. Coated and uncoated PV panels were tested in outdoor conditions for ten-day periods. The power outputs of the PV panels were measured, and their ten-day average efficiency was presented. According to the results, the highest efficiency increase is 8.7%. The highest light transmittance is 88.2% at 550 nm. In addition, hydrophobic properties were observed on all surfaces and the water contact angle was measured as 96.18 degrees.

Injecting hydrogen into the natural gas network to reduce CO2 emissions in the EU residential sector is considered a critical element of the zero CO2 emissions target for 2050. Burning natural gas and hydrogen mixtures has potential risks, the main one being the flame flashback phenomenon that could occur in home appliances using premixed laminar burners. In the present study, two-dimensional transient computations of laminar CH4- + air and CH4 + H-2 + air flames are performed with the open-source CFD code OpenFOAM. A finite rate chemistry based solver is used to compute reaction rates and the laminar reacting flow. Starting from a flame stabilized at the rim of a cylindrical tube burner, the inlet bulk velocity of the premixture is gradually reduced to observe flashback. The results of the present work concern the effects of wall temperature and hydrogen addition on the flashback propensity of laminar premixed methane-hydrogen-air flames. Complete sequences of flame dynamics with gradual increases of premixture velocity are investigated. At the flame flashback velocities, strong oscillations at the flame leading edge emerge, causing broken flame symmetry and finally flame flashback. The numerical results reveal that flashback tendency increase with increasing wall temperature and hydrogen addition rate. (C) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

The Thrace Basin in northwestern Turkey is a deep Eocene–Oligocene hydrocarbon-bearing sedimentary basin. The basin has potential for geothermal energy utilization in the future due to its favorable geological conditions. In this study, we combined the available bottom hole temperature (BHT) data from 70 points with the thermal conductivity and radiogenic heat productions of the basin formations, and generated a detailed thermal model of the northern part of the basin. For heat flow determinations from the BHT data, we applied Bullard’s thermal resistance method on formation thermal conductivities and thicknesses. The results give an average surface heat flow of 65.8 ± 11.3 mW/m2. We obtained high heat flow values (75–80 mW/m2) in the eastern and western sides, and the central part of the study area. These relatively high heat flow values can be explained by the combined effect of basement topography and the variations in the radiogenic heat production of the basement rocks. The calculated subsurface temperatures in selected hydrocarbon fields vary in the range of 45–64°C at 1km depth, 99–136°C at 3km depth, and 155–208°C at 5km depth as a result of local variations of the surface heat flow and formation thermal resistances. These variations in subsurface temperatures can have significant effects on the cost of geothermal energy production in future.

We synthesized molecular weight-controlled and fluorine-containing PAEs (MWCPAEs) using decafluorobiphenyl (DFBP), 2-bis (4-hydroxyphenyl) propane (Bis A), and 2,2-Bis (4-hydroxyphenyl) hexafluoropropane (Bis AF). Later, commercially available PEEK and the MWCPAEs were sulfonated (SPEEK and SMWCPAE) and blended to fabricate blend membranes (BMs). All BMs have been prepared by solution casting method in 5 wt%-20 wt% concentrations using N,N-dimethylacetamide. The effects of chemical structures, sulfonation levels, MW and compositions on the membrane properties are discussed. We obtained miscible and mechanically compatible BMs. The highest proton conductivity (238.9 mS/cm) was determined for SPEEK70/S-DFBP-Bis-A based blend membrane (Mw: 48.355, 5wt. %). All chemical, mechanical, thermal, and hydrolytic properties of the BMs are comparable with, or better than, that of Nafion. We suggest that designing polymeric structures with desired MWs and sulfonation levels may enable enhanced properties and can help to design materials with on demand properties.

© 2023 Elsevier LtdBiomass is the most widespread among renewable energy sources and offers many advantages. However, the heterogeneous structure of biomass brings many disadvantages. Therefore, characterization of thermal degradation of biopolymeric structures in biomass such as hemicellulose (HC), cellulose (CL), and lignin (LN) is very important for the efficiency of any biomass-based thermal process. On the other hand, the characterization of these biopolymers requires various experimental procedures that consume resources and time. Artificial neural networks (ANN) as a machine learning approach provide a remarkable opportunity to identify patterns in the complex structure of biomass fuels and their thermochemical degradation processes. In this study, a new model was developed for the first time to generate differential thermogravimetric analysis (DTG) curves for HC, CL and LN in biomass using proximate analysis results of raw biomass. DTG curves were evaluated using a ANN model developed with the open-source \"TensorFlow\" library in Python software. ANN model performed excellently with R2 values above 0.998. The results show that the newly developed model can estimate the thermal degradation for any temperature, so that biopolymer fractions in the degraded biomass can be calculated immediately, which has not been reported before.

This paper presents a novel optimization approach to find the optimal STATCOM installation in the power system using Mixed Integer Distributed Ant Colony optimization (MIDACO). The proposed method is mathematically formulated based on minimizing the Voltage Deviation Index (VDI) and the installation cost of STATCOM. The ability of STATCOM to provide shunt compensation to maintain bus voltage is exploited here. The proposed approach has been tested on the standard IEEE 14-bus system.

Marine pollution has increased in recent decades, largely due to the proliferation of seafood processing plants and the improper disposal of their associated waste streams. The waste streams consist mainly of shells that are composed of chitin, which is the most abundant aminopolysaccharide biopolymer in nature. Recognizing the value of chitin, the potential for the valorization of crustacean waste for chitin production was explored. In this regard, biogenic crab waste was subjected to chemical-only, enzymatic–chemical, and microbial treatments for chitin production. The results were employed as inputs for process simulation as a precursor to undertaking performance assessments. This study subsequently showed that the net present values (NPVs) of the chemical-only, enzyme–chemical, and microbial chitin production pathways were GBP 118.63 million, GBP 115.67 million, and GBP 132.34 million, respectively, indicating that the microbial chitin production pathway constituted the most appropriate technology for future investment. Employing a cost–benefit (CB) analysis, the CB ratios for the chemical-only, enzymatic–chemical, and microbial approaches were determined to be 7.31, 0.45, and 0.23, respectively. These results reinforced the dominant status of the microbial approach for chitin production from crab waste as the preferred valorization strategy. This study was able to provide information regarding the implications of executing alternative scenarios for crustacean waste.

© 2022 Elsevier LtdIn this study, the use of Deep Cascade Forward Neural Network (DCFNN) was investigated to model both linear and non-linear chaotic relationships in co-composting of dewatered sewage sludge and biomass fly ash (BFA). Model results were evaluated in comparison with RSM, Feed Forward Neural Network (FFNN) and Feed Back Neural Network (FBNN), and Cascade Forward Neural Network (CFNN). DCFNN produced predictive results with MAPE values less than 1% for all datasets in all experimental designs except one with 1.99%. Furthermore, the decision variables were optimized by Genetic Algorithm (GA). The desirability level obtained from the optimization results was found to be 100% in a few designs and above 95% in all other designs. The results showed that DCFNN is a reliable and consistent tool for modeling composting process parameters, also GA is a satisfactory tool for determining which outputs the input parameters will produce in an experimental setup.

© 2023 by the authors.The important parts of a transformer, such as the core, windings, and insulation materials, are in the oil-filled tank. It is difficult to detect faults in these materials in a closed area. Dissolved Gas Analysis (DGA)-based fault diagnosis methods predict a fault that may occur in the transformer and take the necessary precautions before the fault grows. Although these fault diagnosis methods have an accuracy of over 95%, their validity is controversial since limited data are used in the studies. The success rates and reliability of fault diagnosis methods in transformers, one of the most important pieces of power systems equipment, should be increased. In this study, a hybrid fault diagnosis system is designed using DGA-based methods and Fuzzy Logic. A mathematical approach and support vector machines (SVMs) were used as decision-making methods in the hybrid fault diagnosis systems. The results of tests performed with 317 real fault data sets relating to transformers showed accuracy of 95.58% using a mathematical approach and 96.23% using SVMs.

Semi-transparent organic photovoltaics (OPVs) are an emerging solar-energy-harvesting technology with promising applications, such as rooftop energy supplies for environmentally friendly greenhouses. However, the poor operational stability of OPVs poses challenges to their feasibility as incessantly serving facilities. Here we report a reductive interlayer structure for semi-transparent OPVs that improves the operational stability of OPVs under continuous solar radiation. The interlayer effectively suppresses the generation of radicals from the electron transport layer under sunlight and prevents the structural decomposition of the organic photoactive layer during operation. The defects that serve as the charge carrier recombination sites are nullified by the electron-donating functional groups of the reduced molecules, which improves photovoltaic performance. The semi-transparent OPVs demonstrate a power conversion efficiency of 13.5% and an average visible transmittance of 21.5%, with remarkable operational stability (84.8% retention after 1,008 h) under continuous illumination. Greenhouse results show that the semi-transparent OPV roof benefits the survival rate and growth of the crops, indicating the importance of our approach in addressing food and energy challenges.

© 2022 Elsevier LtdIntegrated gasification combined cycle (IGCC) is a power generation technology that partially oxidizes solid feedstocks to produce syngas, drives high-efficiency gas turbines (GT), recovers waste heat and uses it to generate electricity, etc. In this study, a new IGCC model for an entrained bed gasifier/GT/Kalina cycle for CO2 co-gasification was developed for the first time using Aspen HYSYS, and an energy and exergy analysis of this model was performed to provide decision makers with a comprehensive overview of whether the energy conversion system is designed to be sustainable from a variety of perspectives. The newly proposed integrated system was used to study the CO2/air gasification process of the biochar/waste tyre blend. Although entrained bed gasifiers have been modelled using different software and different fuels, no study on the Kalina cycle integrated biochar/waste tyre co-gasification process using Aspen HYSYS has not been reported yet. In addition, most parametric studies on integrated Kalina combined cycles focus on the influence of the Kalina cycle or a few operating factors. However, in this study, the effects of a variety of gasification process operating conditions on the performance of the combined cycle are considered. The equivalence ratio (ER) for a high-efficiency integrated system ranged from 0.23 to 0.25, and the CO2 content in the gasifying agent was in the range of 5–7%. While increasing the waste tyre content in the feedstock enhances H2 production, operating the entrained bed gasifier at relatively low temperature is more efficient. Increasing the ammonia content in the working fluid also improves efficiency, and the inlet pressure of the Kalina turbine should be maintained at 28 bar to maximize performance. While the integration of the GT enables significant power generation, optimizing the operating conditions of the gasifier, which has the highest exergy dissipation, is critical to the performance of the integrated system.

© 2022 Elsevier LtdThe problems of foreign dependency in energy and the orientation towards national/international environmental policies encourage investments in renewable energy systems. In renewable energy systems (RES), photovoltaic (PV) systems based on solar energy are increasing every year. This situation reveals the need for qualified employees whose working area is photovoltaic systems. This study has conducted a literature search on occupational health and safety (OHS) issues in PV power systems. At the same time, field studies and expert interviews were conducted to examine the OHS practices and training received in the work areas. In this direction, the importance of OHS requirements and training for OHS success in PV power systems has been emphasized. The importance of training has been expressed to increase OHS\"s performance and success in the PV power system fields. In this study, sample gamification scenarios were created for two different activities in working at height during the installation of the PV. Algorithms have been developed for improving VR/AR applications in OHS training with the sample gamification scenarios created and to support the dissemination of these training applications. In this way, it is aimed to explain the necessary information for VR/AR technologies at an academic level and to contribute to content developers, researchers, relevant institutions and organizations from a sustainability perspective.

© 2022 The Authors. Published by American Chemical Society.Catalysts based on metal-free conjugated porous polymers (CPPs) are still rare for electrochemical oxygen reduction reactions (ORR). In this study, a conjugated porous polymer, TT-TPB, based on thieno[3,2-b]thiophene (TT) and triphenylbenzene (TPB), was synthesized applying palladium(0) catalyzed Suzuki coupling reaction and its ORR activity was investigated in alkaline condition. It demonstrated comparable electrocatalytic performance of ≈0.89-0.9 V Eonset vs RHE with the commercially available Pt/C. Density-functional theory (DFT) calculations revealed that TT-TPB featured efficient electrocatalytic active sites derived from volumetric, areal, and O2 adsorbing calculations, which were in line with the experimental results. Moreover, semiconducting and surface properties of TT-TPB were investigated in detail using electrochemical and spectrophotometric techniques. This work shows the potential application of thienothiophene-based metal-free CPP in the electrochemical conversion process.

© 2022 Wiley-VCH GmbH.We demonstrate a BODIPY-based small molecule photopatterned surface by thiol-ene click chemistry at room temperature utilizing only 366 nm UV light. The resulting cross-linked polymer exhibits porous surfaces according to AFM and TEM results. A dramatic blue shift occurred in absorption spectra and the photoluminescence was also intensified as a result of crosslinking. Optical and electrochemical results are compatible with the DFT calculations. The obtained results prove that the photopatterned BODIPY-based material can be easily and inexpensively applied in multilayer optoelectronic devices.

© 2022 Elsevier LtdStudies on developing strategies to predict the stability and performance of the composting process have increased in recent years. Machine learning (ML) has focused on process optimization, prediction of missing data, detection of non-conformities, and managing complex variables. This review investigates the perspectives and challenges of ML and its important algorithms such as Artificial Neural Networks (ANNs), Random Forest (RF), Adaptive-network-based fuzzy inference systems (ANFIS), Support Vector Machines (SVMs), and Deep Neural Networks (DNNs) used in the composting process. In addition, the individual shortcomings and inadequacies of the metrics, which were used as error or performance criteria in the studies, were emphasized. Except for a few studies, it was concluded that Artificial Intelligence (AI) algorithms such as Genetic algorithm (GA), Differential Evaluation Algorithm (DEA), and Particle Swarm Optimization (PSO) were not used in the optimization of the model parameters, but in the optimization of the parameters of the ML algorithms.

© 2022 IEEE.Businesses are concerned about energy losses. Electronic gadgets have become more prevalent as a result of their adoption. The frequency with which home electricity usage data is collected has grown, allowing for sophisticated data analysis that was previously unavailable. Indeed, adopting Smart Grid (SG) networks, which are freshly improved networks of linked devices, may considerably enhance the existing energy infrastructure\"s dependability, economy, and durability. The SG involves sharing a lot of data, including information on specific users\" power use. And using this information, machine learning and deep learning algorithms may be able to detect power theft users reliably. This paper presented a Convolutional Neural Network (CNN)-based model for automated network-based vulnerability scanning that has excellent classification performance in many categories. Testing from iteration two to four iterations, this study examines research to discover the ideal configuration of the sequential model (SM) for categorization. The method is validated using a two-layer design, including an efficiency of 0.92, the whole first layer is composed of 128 nodes while the second level consists of 64 nodes. This allows for the development of a higher-level classifier for electrical signals, which may be employed in a number of applications. CNN was used to create electrical signal detectors, and SM was used to extract data from an electricity usage dataset. Furthermore, the number of features in the data set can be reduced using the Blue Monkey (BM) approach, and these results are then used to develop high-performance models. In this regard, the focus of this study has been on lowering the amount of needed features in the dataset in order to establish a rising classification algorithm for electrical signals. Experiments have applied the proposed systems\" fantastic performance, with just 666 characteristics required to combine the CNN and BM methods. Comparative to 1035 traits when CNN was used alone. This shows that the CNN and BM models are better than the CNN model in terms of lowering sufficient know while maintaining the same reliability.

© 2022 IEEE.Deep transfer learning is one of the common techniques used to classify different types of cancer. The goal of this research is to focus on and adopt a fast, accurate, suitable, and reliable for classification of colorectal cancer. Digital histology images are adjustable to the application of convolutional neural networks (CNNs) for analysis and classification, due to the sheer size of pixel data present in them. Which can provide a lot of information about colorectal cancer. We used ten different types of pr-trained models with two type method of classification techniques namely (normal classification and k-fold crosse validation) to classify the tumor tissue, we used two different kinds of datasets were these datasets consisting of three classes (normal, low tumors, and high tumors). Among all these eight models of deep transfer learning, the highest accuracy achieved was 96.6% with Darknet53 for 5-Fold and for normal classification the highest results obtained was 98.7% for ResNet50. Moreover, we compared our result with many other papers in stat-of-the-art, the results obtained show clearly the proposed method was outperformed the other papers.

© 2022 IEEE.This paper proposes an optimal coordination strategy for electric vehicles and energy storage devices in distribution grids besides the optimal allocation problem of renewable distributed generation (RDGs) and energy storage devices (ESDs). By finding the optimal number, size, and site of the RDGs and ESDs, together with the operation strategy of the ESDs and smart charging of a large number of EVs, the performance of the distribution grids will be improved. An advanced grey wolf optimization (AGWO) algorithm is used to minimize energy losses and voltage violations simultaneously in the test systems. Simulations are tested on IEEE 33 and 69 bus networks to find near-optimal solutions for the optimization problem. Based on the simulation results, the proposed optimization framework reduced the systems\" losses while minimizing the voltage violations by finding the optimal control parameters of the devices.

In this research, the advantages of the e-scooter tool used in the mail or package delivery process were discussed by considering the Turkish Post Office (PTT) data in the districts of Istanbul (Kadıköy, Üsküdar, Kartal, and Maltepe) in Turkey. The optimization Poisson regression model was utilized to deliver the maximum number of packages or mails with minimum cost and the shortest time in terms of energy consumption, cost, and environmental contribution. Statistical and optimization results of dependent and independent variables were calculated using numerical and categorical features of 100 e-scooter drivers. The Poisson regression analysis determined that the e-scooter driver’s gender (p|0.05 < 0.199) and age (p|0.05 < 0.679) factors were not effective on the dependent variable. We analysed that the experience in the profession (tenure), the size of the area responsible, and environmental factors is effective in the e-scooter distribution activity. The number of packages delivered was 234 in a day, and the delivery cost per package was calculated as 0.51 TL (Turkish Lira) for the optimum values of the dependent variables. The findings show that the choice of e-scooter vehicle in the mail or package delivery process is beneficial in terms of time, cost, energy, and environmental contribution in districts with higher population density. As the most important result, the operation of e-scooter vehicles with electrical energy shows that it is environmentally friendly and has no CO2 emission. The fact that the distribution of packages or mail should now turn to micro-mobility is emerging with the advantages of e-scooter vehicles in the mail and package delivery. Finally, this analysis aims to provide a model for integrating e-scooters in package or mail delivery to local authorities, especially in densely populated areas.

Solar energy has increased in its share of global electrical energy production. The increasing reliability of solar energy has positively affected the sustainability of photovoltaic (PV) power plants. A failure in any module in the plant can reduce or interrupt the production of electrical energy, causing significant losses in both efficiency and asset value. Therefore, responding to a fault as quickly as possible in a PV power plant is critical. The ability of the PV plant operator to react to potential faults is directly related to the rapid detection of faulty modules. In this paper, different PV monitoring systems in the literature are investigated extensively from the point of view of the devices and the techniques used to measure PV systems\" current, voltage, solar radiation, and module temper-ature. In particular, the communication methods and data acquisition cards used in monitoring were examined. Remote monitoring technologies quickly detect the location of a malfunction in a large-scale power plant. In this context, traditional wire communication methods, today\"s communication technologies, and the low-cost IoT (Internet of Things) technologies used to monitor the performance of large and small-scale PV power plants are compared in detail. With the advancement of Internet of Things technologies such as Zigbee and LoRa, research on remote wireless monitoring of photovoltaic modules has accelerated in recent years. These technologies are projected to be widely deployed in the near future for the maintenance and fault detection of numerous photovoltaic installations.

In this study, a laboratory-scale cooling system, simulating a household refrigerator, was examined experimentally for an average cabinet temperature of 4 degrees C with two different types of phase change materials (PCMs) (water and eutectic solution). The system was tested with various amounts of water and different concentrations of sodium sulfate (Na2SO4) in water. The influence of different amounts and types of the PCMs on the cabinet air temperature and compressor power consumption was analyzed during the running period. The cabinet air temperature change over time was also observed during the power failure period. The examinations suggest using 1.0 wt.% Na2SO4 solution as the PCM in the cooling systems. This solution provides the lowest energy consumption (19.5 kJ), and the running time percentage (21.7%); while maximizing the energy saving (14.2%). In addition, integrating it as PCM could allow preserving products for long periods without significant quality loss during a power failure; because it prevented the rapid increase of the cabinet air temperature in this scenario. Lastly, when the cycle frequency is considered, it has the best time interval and the number of cycles (5.3). Thus, it has a positive effect on reducing the compressor cycling frequency, which eventually increases the compressor lifespan.

With the rising concerns about climate change and technological developments, the deployment of low carbon technologies (LCT) is gaining importance for reliable and sustainable power system operation. To decarbonize the heat and transportation sectors, LCTs such as heat pumps (HP) and electric vehicles (EV) are integrated into the power system from the low voltage (LV) distribution network, where predominantly end-users are connected. However, the increasing penetration of EVs and HPs, together with distributed photovoltaic (PV) systems on the demand side, can cause technical challenges in LV distribution networks, such as under/over voltages at the far end of the feeders. Therefore, the hosting capacity of a grid for LCTs is limited due to network constraints such as line ampacity, bus voltage, and frequency. In this paper, voltage-constrained management approaches for LCTs are proposed to improve the grid hosting capacity in a real large-scale distribution network. Moreover, HPs, PVs, battery energy storage systems (BESS), and EVs as well as their vehicle-to-grid (V2G) feature are evaluated as LCTs. The uncertain behavior of these technologies is taken into consideration for more realistic analyses. In addition, the charging and discharging interactions of BESSs are controlled by following the power consumption profiles from the economic point of view. Furthermore, the hourly carbon data is used to control BESSs from an emission-sensitive aspect. To demonstrate the effectiveness of the proposed algorithms, a series of tests is conducted on a real distribution network model. The results show that the hosting capacity of the considered LV network can be increased from 35% to 50% using the proposed algorithms. The promising results obtained in this study pave the way for future active LCT management studies to improve hosting capacity in urban networks, and pilot demonstrations in the field.

© 2022 IEEE.This paper solves the optimal operation problem of distribution systems using renewable energy sources and voltage regulators. The developed model makes use of Whale Optimization Algorithm based optimization. We apply the proposed optimization model to a modified low voltage distribution system of Almaty, Kazakhstan. Three different cases were simulated. The cases considered are medium load level with medium PV power output, high load level with medium PV output, and high load level and low PV power outputs. The simulation results show that the proposed algorithm is able lower the active power losses.

© 2022 IEEE.Increased photovoltaic (PV) penetration in distribution systems helps improve the system\"s performance by reducing the active power loss, in addition to the environmental benefits. However, high PV generation during light loading periods results in reverse power flows in the system. In turn, the voltage increases at the nodes located further from the main substation. This paper proposes a reactive power control method to coordinate the participation of different PV smart inverters in solving the overvoltage problem. The proposed control method operates at the lateral level based on the measured voltage at the lateral\"s end-node (or most downstream PV system). The lateral controller sequentially dispatches power factor commands to the smart inverters. This method maintains the coordination between the PV systems at different operating conditions keeping the reactive power requirement as low as possible. The proposed control method is compared to the volt/var control method from the IEEE standard 1547 and shows an improved performance in terms of reactive power requirement and active power loss.

In this study, the Aspen Plus simulator was used to develop a circulating fluidized bed (CFB) gasifier/steam turbine/proton-exchange membrane (PEM) fuel cell integrated system. Since integrated systems comprise many thermochemical, biochemical, and physical processes, equipment, chemicals, etc., determining output parame-ters is challenging and important. In this context, twenty torrefied biomass samples were parametrically analyzed for syngas properties and H2 production rates. So, using solid fuel characteristics and gasifier operating parameters, a data set including PEM fuel cell module outputs was created. Thereafter, the created data set was utilized to train the artificial neural network (ANN) model. This paper, as far as we know, examines the impacts of different torrefied biomass samples on PEM fuel cell outputs for a sophisticated integrated system dependent on gasification conditions, and provides a more generalized and rapid prediction model for the integrated system with complicated equations. Additionally, parametric studies assist in determining the proposed new integrated system\"s minimal operating condition, which is highly dependent on the fuel characteristic. High steam/fuel ratio, high carbonization degree, and low pressure lowered PEM efficiency while increasing power and voltage outputs. The ANN model also accurately forecasts PEM fuel cell output parameters (R-2 greater than 0.99 and MAPE less than 1%) based on torrefied biomass proximate analysis data and gasification process operating parameters. As a consequence, a CFB gasifier/steam turbine/PEM fuel cell system, which contains diverse modules and thermochemical processes, can be examined using ANN models trained on a large and high-quality dataset.

The rapid increase in the world population along with the rising trend of global economic growth triggers serious problems related to energy consumption. In this regard, a comprehensive analysis of the current and potential energy situation in the light of demographic trends, main economic indicators, technical conditions, social and environmental concerns has become a significant issue for developing well-planned global and regional energy strategies. In this study, a multi-objective decision-making model is constructed for renewable energy planning to determine the most appropriate resource diversity for Turkey by focusing on five renewable energy sources, namely solar, wind, geothermal, hydroelectric, and biomass. There are four objective functions presented in the proposed multi-objective model including maximization of the technical score of regions, maximization of the job creation, maximization of the environmental score, and minimization of the cost. As a solution methodology, a two-phase fuzzy goal programming approach is performed. The proposed model is solved using LINGO software as a comprehensive solver for model optimization. The appropriateness of the proposed method is analyzed by comparing it with other multi-objective methods. Results reveal that the majority of the energy demand should be met via solar and hydroelectric energies for the satisfaction of the indicated objectives. (c) 2022 Elsevier Ltd. All rights reserved.

© 2022 Elsevier B.V.BESS are now preferred to provide ancillary services due to their advantages of cost reductions and rapid response times. Based on the criteria in the regulation for the electricity grid, an advanced power management control algorithm with the technical features to provide the grid frequency support specified in the regulation on the use of BESSs in ancillary services has been developed in this study and evaluated in various scenarios. A 2 MW/1 MWh installed power BESS is considered for frequency support control, which is one of the grid ancillary services. The algorithm is tested for providing the appropriate power by optimally using the state of charge of the battery within the limits specified in the regulation. Since the maximum and minimum set limits for the state of charge cannot be exactly defined in the literature, the fuzzy logic method was chosen as the decision-maker for active power output. In the algorithm, the amount of power that must be supplied by the battery energy storage system will change according to the dynamically varied state of charge. The results show that the fuzzy logic control algorithm successfully provides the frequency response in the regulation zone with optimum SOC level and contributes the extending the battery life.

The use of energy storage systems (ESS) and distributed generators (DGs) to improve reliability is one of the solutions that has received much attention from researchers today. In this study, we utilize a multi-objective optimization method for optimal planning of distributed generators in electric distribution networks from the perspective of multi-objective optimization. The objective is to improve the reliability of the network while reducing the annual cost and network losses. A modified version of the multi-objective sine–cosine algorithm is used to determine the optimal size, location, and type of DGs and the optimal capacity, location, and operation strategy of the ESS. Three case studies of IEEE 33-bus, 69-bus and 141-bus test systems with Turkish DG and load data were conducted to validate the effectiveness of the proposed approach. The distribution of the Pareto front solutions and the optimal objective functions are compared with the other known algorithms. The simulation results show that the average energy not supplied and annual energy losses for the test systems are reduced by up to 68% and 64%, respectively. Moreover, the Pareto fronts of the proposed method show a better distribution and dominate those obtained by MOGWO, MOSMA, NSGA-II, MOPSO and MOEA-D according to three different Pareto optimization metrics. Finally, the computational effort result shows faster convergence of MOSCA compared to MOGWO, MOSMA, NSGA-II, MOPSO and MOEAD.

Tedious trial-and-error procedures are commonly practiced for surface modification of the perovskite solar cell (PSC) active layers. A guideline to rationally choose the passivating functional groups of the polymeric structure that is eminently effective in passivating the charged defects and elongating the device lifetime is hence in urgent demand. Herein, three prototypical polymers, poly(vinyl acetate) (PVA), polyethylene glycol (PEG), and poly(9-vinylcarbazole) (PVK), are selected to investigate the structural effect of the polymeric modification agents on the PSCs. We found that PVA possesses the Lewis base functional group with the minimized steric hindrance and the strongest electron-donating ability, enabling the most effective defect passivation on the perovskite film surface and the most enhanced carrier diffusion capacity. It demonstrates a power conversion efficiency (PCE) of 23.20% and noticeably enhanced operational stability. Our work provides important insights toward the selection of polymeric modification agents for high-performance and long-term stable PSCs.

Two methods for estimating the near-optimal positions and sizes of capacitors in radial distribution networks are presented. The first model assumes fixed-size capacitors, while the second model assumes controllable variable-size capacitors by changing the tap positions. In the second model, we limit the number of changes in capacitor size. In both approaches, the models consider many load scenarios and aim to obtain better voltage profiles by minimizing voltage deviations and active power losses. We use two recently developed heuristic algorithms called Salp Swarm Optimization algorithm (SSA) and Dragonfly algorithm (DA) to solve the proposed optimization models. We performed numerical simulations using data by modifying an actual distribution network in Almaty, Kazakhstan. To mimic various load scenarios, we start with the baseline load values and produce random variations. For the first model, the optimization algorithms identify the near-optimal positioning and sizes of fixed-size capacitors. Since the second model assumes variable-size capacitors, the algorithms also decide the tap positions for this case. Comparative analysis of the heuristic algorithms shows that the DA and SSA algorithms give similar results in solving the two optimization models: the former gives a slightly better voltage profile and lower active power losses.

© 2022 by the authors. Licensee MDPI, Basel, Switzerland.The use of HVDC systems is increasing in number due to technological innovations, increasing power capacity and increasing customer demand. The characteristics of insulation systems under composite DC and LI voltage must be examined and clarified. In this study, firstly, experimental circuits were designed to generate and measure composite DC and LI high voltage using a simulation program. The coupling elements used were chosen according to simulation results. Afterward, experimental circuits were established in the laboratory according to the simulation results of the designed experimental circuit. Then, breakdown voltages under composite DC and LI voltage for less uniform and non-uniform electric fields were measured with four different electrode systems for positive and negative DC voltage pre-stresses with different amplitudes. The 50% breakdown voltage was calculated using the least-squares method. Finally, 3D models were created for the electrode systems used in the experiments using the finite element method. The efficiency factors of electrode systems calculated with the FEM results were correlated with the experimental breakdown voltage results. Thus, the breakdown behavior of air under bipolar and unipolar composite voltages (CV) was investigated. In conclusion, the experimental results showed that very fast polarity change in bipolar CV causes higher electrical stress compared to unipolar CV.

© 2022, Qatar University and Springer Nature Switzerland AG.Diabetes mellitus is a chronic metabolic disease associated with long-term multisystem complications, among which are non-healing diabetic foot ulcers (DFUs). Electrospinning is a sophisticated technique for the preparation of polymeric nanofibers impregnated with drugs for wound healing, burns, and diabetic ulcers. This study describes the fabrication and characterization of a novel drug-eluting dressing made of core–shell structured hyaluronic acid (HA)–keratin (KR)-polyethylene oxide (PEO) and polycaprolactone (PCL) nanofibers to treat diabetic wounds. The core–shell nanofibers produced by the emulsion electrospinning technique provide loading of metformin hydrochloride (MH), HA, and KR in the core of nanofibers, which in return improves the sustained long term release of the drug and prolongs the bioactivity. Morphological and chemical properties of the fibers were examined by SEM, FTIR, and XRD studies. It was observed that the fibers which contain HA and KR showed thin fiber structure, greater swelling capacity, fast degradation and increased cumulative drug release amount than neat emulsion fibers due to the hydrophilic nature of HA and KR. MH showed a sustained release from all fiber samples over 20 days and followed the first-order and Higuchi model kinetics and Fickian diffusion mechanism according to kinetic analysis results. In vitro cell culture studies showed that the developed mats exhibited enhanced biocompatibility performance with HA and KR incorporation. The results show that HA and KR-based emulsion electrospun fiber mats are potentially useful new nanofiber-based biomaterials in their use as drug carriers to treat diabetic wounds.

© 2022 IEEE.This paper presents a novel two-stage optimization approach for the optimal sizing of static var compensators and their operational management to maximize the photovoltaic integration capacity of distribution systems. In the first stage, the optimal locations for fixed size photovoltaic (PV) systems are determined to minimize the sum of total voltage violations. In the second phase, the size of the PV units are resized and the optimal size, number, location, and operating strategy of the SVC units are determined to maximize PV hosting capacity. In both phases, the Marine Predators algorithm is used for the solution optimization equations. The performance of the proposed approach and solution method is validated on modified 33-node and 141-node radial distribution networks. The results are discussed from the point of view of the maximum hosting capacity and compared with the Grey Wolf optimization and Whale Optimization algorithms in terms of computational performance.

© 2023 The Author(s)Excessive penetration of renewable energy resources into the distribution grid without additional preventive measures has led to several operational problems. However, most strategies developed to accommodate more renewable energy units suffered from other operational problems. Therefore, further efforts are needed to address the other key vulnerabilities of the grid in addition to maximizing the hosting capacity. In this regard, this study is devoted to a new multi-objective formulation to maximize the hosting capacity and minimize the total energy losses while satisfying the operational constraints and maximizing the energy transferred to off-peak hours. The Multi-Objective Advanced Gray Wolf Optimization (MOAGWO) algorithm is used as a solution tool. The proposed formulation and solution algorithm are tested on IEEE-33-bus and 69-bus medium voltage test systems. The impacts of energy storage systems, voltage regulators, and static var compensators on the hosting capacity and the objective functions are identified using several scenarios. The results showed that the optimal device type and locations depend on the level of DG penetration. Finally, a comparison according to two popular multi-objective performance indices showed that the quality of the Pareto front distribution obtained by MOAGWO was better than the ones obtained with the two other popular heuristic methods.

© 2022 IEEE.Deep learning is one of the most significant advances in AI (AI). It is used in a variety of fields due to it has the ability to solve problems that cannot be handled by traditional technologies. The optimization of deep learning relevant to medical images is one of the most important recent advances in image analysis. Several developments have been done on Convolutional Neural Networks to achieve optimal accuracy and increase the learning speed. However, in this paper, we discuss the most recent innovations in convolutional neural networks within Classical method and Quantum method. We briefly provide a snapshot about the architecture, improvements, and principles of both (Classical and Quantum).

© 2022 IEEE.Lately, PM (permanent magnet) motors have started to be used frequently due to advantages such as high efficiency, high power density, high torque, lower losses, lower magnet costs, and more effective control with the development of power electronics. PMSM (permanent magnet synchronous motor) types and BLDCM (brushless direct current motor) types, two members of PM motors, have become frequently used motors in electric vehicles (EVs). In this study, PMSM and BLDCM are compared by optimizing magnet thicknesses, structures, and stator winding turn numbers, to improve the efficiency, using ANSYS Maxwell and ANSYS RMxprt software. In the study, surface mount magnet synchronous motor SPMSM with the inner rotor, V-shaped PM inner rotor IPMSM (interior permanent magnet synchronous motor), BLDCM with inner rotor embedded channel mount magnet, BLDCM with inner rotor embedded magnet, BLDCM with inner rotor surface mount magnet and BLDCM with outer rotor are compared.

© 2022 IEEE.The aim of this research is to utilize the particle swarm approach to examine the coordinated design of the unified power flow controller (UPFC) and two power system stabilizers (PSS) in a multi-machine power system. The synchronized proposed control challenge has been presented as an optimal solution with an objective function and constraint equations to complete this goal. Period models in a multi-machine power system were used to assess the effectiveness of the proposed design under distinct operating situations. It has been shown that, even if both PSSs and UPFCs perform well in their own right, power networks stability is adversely affected by an unfavorable impact or negative interaction between controllers if they are not coordinated. Inter-Area oscillations have been efficiently dampened thanks to integrated layout.

Gasification provides various environmental and technological advantages, and the efficiency of the gasification system is affected by several factors, including the kind of fuel and gasification agent used, the gasifier\"s length and diameter, the operating pressure and temperature, etc. Experimental optimization approaches are more realistic, but they are time demanding; also, a reactor operating at high temperatures and pressures could be dangerous and expensive. Thus, researchers use a variety of modeling techniques, including the process simulators. Additionally, artificial neural network (ANN) as a machine learning approach, which is one of the process modeling methods, is a remarkable approach, and several papers have been published in which it has been utilized in combination with other modeling techniques. On the other hand, a combined process simulator/ANN model that considers gasifier design/operational parameters for the kinetic modeling of gasification process has not been reported. In this study, after kinetic modeling and validation of seven different circulating fluidized gasifiers using Aspen Plus, parametric studies were performed. Parametric analysis was used to examine the impacts of gasifier diameter, length, gasifier temperature, air/fuel ratio, and fuel type, and a dataset was created for ANN training. The syngas composition and thermal value were predicted using the ANN model. Therefore, a model was developed that takes into consideration both design and operating variables. The investigations revealed that heterogeneous reactions were the most critical factor in defining syngas characteristics. Although design factors have a considerable impact on syngas characteristics, the gasifier temperature is a key factor in the whole process. Furthermore, the ANN model estimates syngas specifications with great accuracy (R2 > 0.99 and MAPE < 3%) based on fuel attributes and gasifier design/operating parameters. Hence, ANN models can be used to analyze the effectiveness of systems including a complex combination of reactions and thermochemical processes.

© 2022 Elsevier LtdAn experimental cycle-to-cycle analysis in multi-cylinder automotive common-rail compression ignition engine was performed to understand better the combustion in the hydrogen fuelled diesel engine. Hydrogen was fumigated in intake line at a wide-ranging from 0 lpm to 50 lpm in steps of 10 lpm. Test engine was operated at three different loads for a constant engine speed. The relative air–fuel ratios were between 1.55 and 3.31. The combustion occurred with an excess of air in all tests. Combustion data of 120 cycles were used to analyse the cyclic variations, determined by standard deviation of the cylinder pressure. Cyclic variations, coefficient of variance (CoV), standard deviation, frequency, and average value of peak combustion pressure (Pmax), maximum pressure rise rate (PRRmax), indicated mean effective pressure (IMEP), mass fraction burned (MFB), and MFB10-90 duration were analysed. Results showed that for all hydrogen addition levels, CoVs of Pmax, IMEP and MFB10-90 were always found below 3% at all loads, and PRRmax values with hydrogen operations in each cycle were under the limit of knocking combustion. Engine load was the most important factor to affect to CoV, standard deviation, and frequency of the combustion parameters. Minor cyclic variations in MFB traces were found at all engine loads and hydrogen addition levels, which agreed well with the cylinder pressure traces. Frequencies of Pmax and IMEP were moderate in low and medium loads, but reduced in high load, and also the values of Pmax and IMEP in high load were distributed in a wider range compared to low and medium loads. MFB10-90 duration increased for dual-fuel modes, its frequency was decreased, and its distribution was extended with hydrogen addition. Trend of standard deviation was mostly similar to that of CoV for the studied combustion parameters. Furthermore, variations of correlation coefficient (R) among Pmax, PRRmax, IMEP, and MFB10-90 were discussed in this paper, and the highest R value was found between IMEP and Pmax.