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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.

We consider a 2-layer quasi-geostrophic ocean model where the upper layer is forced by a steady Kolmogorov wind stress in a periodic channel domain, which allows to mathematically study the nonlinear development of the resulting flow. The model supports a steady parallel shear flow as a response to the wind stress. As the maximal velocity of the shear flow (equivalently the maximal amplitude of the wind forcing) exceeds a critical threshold, the zonal jet destabilizes due to baroclinic instability and we numerically demonstrate that a first transition occurs. We obtain reduced equations of the system using the formalism of dynamic transition theory and establish two scenarios which completely describe this first transition. The generic scenario is that two modes become critical and a Hopf bifurcation occurs as a result. Under an appropriate set of parameters describing midlatitude oceanic flows, we show that this first transition is continuous: a supercritical Hopf bifurcation occurs and a stable time periodic solution bifurcates. We also investigate the case of double Hopf bifurcations which occur when four modes of the linear stability problem simultaneously destabilize the zonal jet. In this case we prove that, in the relevant parameter regime, the flow exhibits a continuous transition accompanied by a bifurcated attractor homeomorphic to $S^3$. The topological structure of this attractor is analyzed in detail and is shown to depend on the system parameters. In particular, this attractor contains (stable or unstable) time-periodic solutions and a quasi-periodic solution., Comment: 20 pages, 12 figures, 2 tables

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.

Additive manufacturing has many techniques and has been frequently used and studied recently. The fused deposition modeling (FDM) method, which is one of these techniques, is a widely used three-dimensional (3D) printing method that works with the extrusion principle. The parameters used during printing affect the properties of the final product. This study investigated the effect of print angle, infill density, and perimeter count parameters on the tensile strength characteristics of FDM-printed PLA samples by the response surface method (RSM), an experimental design method. The tensile strengths of the samples produced according to the test lists obtained using the Box–Behnken design were determined. A model equation showing the effect of the relevant parameters was created. From the experiments, the compatibility of the model equation with the experimental results has been demonstrated. The highest tensile strength values were achieved in cases where the print angle was 90°, the infill density was 100%, and the number of walls was 5-6.

In this study, the effect of different laser beam power on the nanoindentation and corrosion properties of Ti-6Al-4V alloys fabricated direct metal laser sintering (DMLS) method was investigated. Ti-6Al-4V samples were fabricated by DMLS method with three 34.72 J/mm3, 64.81 J/mm3 and 83.33 J/mm3 energy densities named as sample 1, sample 2 and sample 3, respectively. The microstructure and crystalline phase of Ti-6Al-4V samples were analyzed by optical microscope, SEM and XRD devices. Microstructural examinations showed that microstructure of Ti-6Al-4V samples consisted of the prior columnar β grains and α/α´ phases. The β phase proportion soared with rising laser beam power. Nanoindentation results illustrated that the order of reduced elastic modulus (Er) values was sample 1 > sample 2 > sample 3. The order of hardness (H) values followed sample 1 > sample 2 > sample 3. The H/ Er ratios were found to be 0.0338, 0.0333, and 0.032 for samples 1, 2, and 3, respectively. The H3/Er2 ratios were estimated to be 0.00734 GPa, 0.00633 GPa, and 0.00513 GPa for samples 1, 2, and 3, respectively. The presence of high values means better anti-wear capacity and good resistance to deformation for samples based on nanoindentaion findings. Moreover, electrochemical measurements depicted that the value of corrosion current density (icor) of the samples ordered as sample 3 > sample 2 > sample 1. The sample 1 exhibited better corrosion resistance than other samples. The findings showed that as the laser power is low, DMLS Ti-6Al-4V samples potentially have better mechanical and corrosion performance due to low β content and high α´ phases in the microstructure.

© 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.

Forging is one of the most important production methods in applications where the disadvantages of machining are not desired. However, the possibility of micro-cracking in the material at the end of the forging process should be considered. The main constraints here are the aging of hydraulic systems and possible casting defects. Therefore, in this study, the modernized process of an old forging press with a capacity of 30000 kN was carried out. For this purpose, the capacity of the forging press has been increased by modifying a new generation pump and hydraulic accumulator group. Thus, by increasing the cycle time of the hydraulic forging press, it is known that effective forging can be performed in a shorter time (from 1.7 to 1.3 seconds). When the modernization results are examined, it is seen that the necessary forging processes can be completed before the material cools down to the critical temperature.

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.

© 2022 American Society of Civil Engineers.Topology optimization studies have found widespread use with advances in additive manufacturing technologies. A topology optimization method is proposed for the design of a fatigue critical part, namely, a pitch arm made of Al 7050, which transmits the commands to change the pitch attitude to the rotor blades of the helicopter. This study used the bidirectional evolutionary structural optimization (BESO) method with the fatigue failure criterion of a closed Soderberg (CS). The mean stress and stress amplitude values used in the procedure were obtained by applying the Manson-McKnight method. The calculations were performed using a Python-based script compatible with Ansys version 2021 R1 software. In the finite-element calculations, the sensitivity number of the elements was determined using the results of the linear static analysis. A volume constraint was applied in the formulation of the optimization problem to minimize the weight of the structure. The results of the classical topology optimization approach and the developed optimization method were compared. The final geometries of both approaches showed that the ranges of volume reduction differed from each other. It was found that the values for the new fatigue-optimized weight of the pitch arm were 18% higher than the values for the static-based classical topology optimization method. However, in general, the weight of the part obtained as a result of fatigue-based topology optimization was reduced by 34% in total.

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.

© 2022In this research theoretical and practical studies have been highlighted about alloy design, casting and steel-slag interaction. The use of a protective slag layer was identified as an advantageous method in the production of low-density steels. For the first time in this study, a unique synthetic steelmaking (CaO/Al2O3:1.57) slag was implemented in an induction furnace with no vacuum chamber to produce high aluminum in steel. This study showed that manganese and aluminum directly influence the solidification structure and carbide formation. Coarser k-carbides have been detected at grain boundaries in austenite as-cast matrix when the Aluminum increases in the low-density steel.

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.

Bu çalışmada, Türkiye’nin tarım makine kullanım projeksiyonunun tahminlemesi yapılmıştır. Türkiye’deki tarımsal üretimlerde yaygın olarak kullanılan tarım alet ve makinelerinın 11 yıllık üretim ve kullanım miktarları esas alınarak 2030 yılına kadar geçen süre zarfındaki projeksiyon katsayıları hesaplanmıştır. Projeksiyon katsayısı değerleri 5 farklı grupta (toprak işleme, ekim, gübreleme ve bakım, ilaçlama, hasat ve diğer tarım makineleri) belirlenmiştir. Türkiye’de yaygın olarak tarımsal üretimlerde kullanılan makinelarda en yüksek projeksiyon katsayısı değerleri; toprak işleme alet ve makinelerinden toprak frezesinde %3.52; ekim, bakım ve gübreleme makinelerinden pnömatik ekim makinesinda %6.05; ilaçlama makinelerinden kuyruk milinden tahrikli pülverizatörlerde %3.29; hasat makinelerinden traktörle çekilen çayır biçme makinelerinde %5.30 ve farklı yaştaki biçerdöverlerden 0-5 yaş grubundaki biçerdöverlerde ise %4.53 olarak belirlenmiştir. Türkiye’de tarımsal üretimlerde güç kaynağı olarak traktörlerde de projeksiyon katsayısı değerleri, en yüksek tek akslı traktörlerden 0-5 BG güç grubunda %15.83 ve çift akslı traktörlerde ise >70 BG güç grubunda ise %7.95 olarak bulunmuştur. Sonuçlara göre, teknolojik özellikli olan biçerdöverler ve traktörlerin kullanımının 2030 yılına kadar artacağı öngörülmektedir., In this study, the estimation of machine use projection in Turkey has been evaluated. The projection coefficients of the agricultural equipment and machinery used commonly in agricultural production in Turkey during the period until 2030 year based on 11 years of production quantities were calculated. Projection coefficient values were determined in 5 different groups (soil cultivation, sowing, fertilization and maintenance, spraying, harvesting and other agricultural machinery). The highest projection coefficients were determined for agricultural machinery used in agricultural production were found as 3.52% for rotovator among the soil cultivation machines; 6.05% for the pneumatic precision drill among the sowing, maintenance and fertilizing machines; 3.29% for PTO driven pulverizator among the spraying machines; 5.30% for the mowers pulled with tractor among the harvester, and 4.53% for the combine harvesters in the 0-5 age group among the combine harvesters of different ages. The highest projection coefficients for tractors as a power source used in agricultural production in Turkey were calculated as 15.83% for 0-5 HP single-axle tractors and 7.95% for >70 HP double-axle tractors among tractor power groups, respectively. According to the results, it is seen that the use of combine harvesters and tractors with technological features will increase until 2030 year.

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.

One of the commonly used additive manufacturing methods is extrusion-based 3D printing (e.g., fused filament fabrication or FFF) that is generally required post-processing to make fabricated components ready to be used. This paper considers two major points to assess the surface enhancement of components made of polylactic acid (PLA) material. One is the role of processing parameters including layer thickness, print speed, and printing temperature on dimensional and surface quality of fabricated parts. The second one is post-processing and its parameters. Vibratory surface finishing (VSF) is used as post-processing operation, and treatment time is varied to observe its effect on the surface and dimensional variation of the fabricated parts. The effect of input variables on measured outputs is also presented by using statistical approaches including regression and analysis of variance. This study reveals that layer thickness is dominating parameter to control surface and dimensional quality of both as-printed and post-treated samples. However, printing temperature also plays a notable role on the dimensional accuracy of specimens. Considering post-processing, while it is notable, duration is the key factor determining the final surface quality of fabricated samples. The surface roughness of as-printed samples was reduced by 66% through post-processing operation.

Burnishing has been increasingly utilized to improve the surface integrity of manufactured components. The generation of surface and subsurface layers with ultrafine grains, attributed to severe plastic deformation (SPD) and dynamic recrystallization (DRX), leads to improved surface integrity characteristics including surface and subsurface hardness and reduction in surface roughness. Additionally, due to the generation of compressive stresses within the refined layers, increase in fatigue life and improved wear and corrosion resistance can be achieved. In this study, we apply cryogenic burnishing on Al 7050-T7451 discs and compare the surface integrity characteristics with dry conventional burnishing. A special roller burnishing tool with flexible rotating roller head was designed and used to perform the cryogenic burnishing experiments using liquid nitrogen as the coolant. The results show that cryogenic burnishing can increase the surface hardness by an average of 20–30% within a layer depth of 200 µm compared with only 5–10% increase using dry conventional burnishing. Refined layers with nano grain structure were also generated. During cryogenic burnishing, the tangential burnishing forces were higher than those of dry conventional burnishing due to rapid cooling and work hardening of the material.