Abstract: Abstract A gravitational water vortex turbine is a new development suitable for low to ultralow head with medium to low flow that also facilitates aeration of the water and harvesting of power during water transit. Recent investigations have shown that curved blade profiles are more efficient to harness kinetic energy of the vortex. However, understanding of the optimum runner position along the vortex flow field in a scroll basin is still incomplete considering the design parameters such as vortex–blade interaction, the runner-to-basin diameter ratio and its effect on torque and power output. In this regard, a numerical investigation on the performance of such a turbine has been carried out using OpenFOAM. The effect of runner to basin diameter ratio on performance parameters such as effective head, torque, power and efficiency has been characterized after validation of the methodology using analytical model predictions. The results suggest that relative size of a runner strongly influences the rotational speed acquired and torque developed on account of a stronger vortex near the air-core and lower tangential velocity at higher radii. This work demonstrates that either reducing the size of runner blades close to the orifice region or extending to the far-field region can both result in a reduction of the runner performance. The maximum efficiencies predicted are 25.8%, 42.9% and 41.2% for runner-to-basin diameter ratios 0.18, 0.27 and 0.36, respectively. The optimum runner size is observed to be one-fourth of the basin diameter and predictive performance correlations have been developed for power output and efficiency in this regard as a function of the rotational speed and runner diameter. Hence, the outcomes from this study will be helpful to design and predict performance of a gravitational water vortex power plant with scroll basin for a given flow and head at a particular site. Moreover, it can also be used as a pointer towards further development of gravitational water vortex turbine technology. PubDate: 2022-05-09
Abstract: Abstract Global warming will lead to adverse consequences for human health and well-being. This research ought to determine whether passive low-cost strategies freely controlled by users (ventilation strategies, solar shadings or window operation) could be applied in low-income dwellings to meet acceptable thermal comfort to retrofit the Mediterranean social housing stock of southern Spain towards climate change. On-site measurements registered in some test cells (controlled environment with no users’ influence) were used to calibrate dynamic energy simulation models. The impact of several future periods, climate zones of southern Spain and orientations on thermal comfort was assessed. The results show that climate change triggers a more significant increase in outdoor temperatures in summer than in winter. Should ventilation be kept to minimum and blinds opened during daytime in winter, higher comfort would be achieved, with great differences between orientations and south reporting the best results. The higher the outdoor temperatures due to climate change, the higher the percentage of comfort hours (i.e. 23–68% in the present and 50–75% in 2080). In summer, natural night ventilation and blinds closed during daytime lead to the best comfort result, with negligible temperature differences between orientations. Future climate change scenarios worsen the percentage of comfort hours (i.e. 96–100% in the present, while up to 17% in 2080). Mechanical ventilation and blind aperture schedules were found to have the highest influence on overheating discomfort. Likewise, mechanical and natural ventilation schedules had the highest impact on undercooling discomfort. PubDate: 2022-05-09
Abstract: Abstract Nowadays, cloud computing is one of the most up-to-date topics conducted by many researchers. The specialists and researchers try to create a new generation of data centers using virtual machines to supply the network service virtually and dynamically. These services will lead everyone to access their required application worldwide via the Internet. Furthermore, the number of datacenters (DC) is growing exponentially. Therefore, a novel concept called green computing has been raised to decrease the negative effect of data centers to protect the environment. Green cloud computing solutions strive to reduce carbon dioxide emissions, energy, power, and water consumption that are harmful to the environment. In this paper, the approaches moving toward green computing are investigated and categorized to help the researchers and specialists in cloud computing expand green cloud computing and improve the environment quality. The "green cloud computing" has been searched in this survey. We have searched ACM, IEEE, Elsevier, and Springer and surveyed the papers between 2010 and 2022. This paper is a holistic survey useful for researchers who work on green cloud computing and its environmental influence. This paper can lead researchers to move toward green computing to protect the environment against these days’ environmental issues. These days, environmental issues like climate change make this subject more important than before. PubDate: 2022-04-23
Abstract: Abstract Multigeneration systems have proven to be one of the most spectacular and cutting-edge technologies over the previous few decades, producing a diverse range of useful outputs that include electricity, space heating, space cooling, and hydrogen production. However, only single-generation systems have received attention when it comes to using the thermal potential of abandoned wells. Therefore, this study exploits ambient geothermal wells by utilizing a multigeneration system. Oil and natural gas units are typically found to use a large amount of natural gas to meet the electrical demands of worksite personnel and the natural gas compression system which is a costly and environmentally unfriendly process. This study offers a way of dealing with energy demands and environmental contamination that is both sustainable and cost-effective. To make remote oil and natural gas units self-sufficient, a suggested multigeneration system gathers energy from ambient geothermal wells and produces 15.78 MW of electricity. A total of 52,187 kW of heat is produced by the abandoned well. To make industry a negative emission unit, this system collects H2S from natural gas at a rate of 30 g/s and CO2 from ambient air at a rate of 45.39 kg/s (zero self-emission and absorb from the air). PubDate: 2022-04-22
Abstract: Abstract Restrictive legislations on the use of fossil fuels encourage the research and development of clean and renewable energies. Renewable energy is characterized by random behavior, which hampers its integration into the current energy base system. Thus, estimating solar irradiation is important for the adoption of renewable energies into the current energy matrix. In this paper, two machine learning estimation models for global horizontal (GHI) and direct normal solar irradiance (DNI) are proposed: the first uses XGBoost and the second employs a convolutional neural network (CNN) combined with a long short-term memory (LSTM) network, forming the hybrid CNN-LSTM model. The case studies apply both models to process images from the GOES-16 satellite, taken from the city of Petrolina, Pernambuco, Brazil. Their results are compared against the reference Copernicus Atmosphere Monitoring Service, Solcast and the Physical Solar Model (PSM) provided by the National Solar Radiation Database. For the GHI estimation, the PSM model achieved the lowest RMSE, 147.23 W/m2, while for DNI estimation, the CNN-LSTM model performed best, with an RMSE equal to 238.22 W/m2. In this case, the proposed models achieved lower RMSE for DNI estimation when compared against the benchmark models, improving by 2.89% and 1.70% for the CNN-LSTM and XGBoost models, respectively. PubDate: 2022-04-19
Abstract: Abstract The present investigation involves the numerical studies on the thermochemical conversion of bamboo biomasses conducted in a Double Tapered Bubbling Fluidized Bed Reactor. Six different bamboo biomass species suitable for the gasification process available in Mizoram state, India, are selected for the study. The 0D equilibrium-based model predicts the percentage composition of syngas constituents viz; H2, CO, CO2, H2O, and CH4 obtained through the gasification process. The global gasification reaction of biomass is formulated from the chemical reactions at various gasification stages. The composition of constituents in the syngas obtained is numerically determined at varied temperature ranges (400–1400 K) and Moisture content (0–40%). The percentage of syngas constituents obtained for Bambusa vulgaris Wamin is outstanding compared to the other biomass species used in the study. The production of CH4 is found suitable at low temperature (< 1000 K) and moisture content (< 35%). The result presented over the equivalence ratio range of 0.2–0.5, and gasification temperature of 1073 K, better recognizes the percentage yield of the syngas components. However, the percentage of H2 and CO2 increases due to the water gas shift reaction with the temperature rise. The obtained results are suitably compared with the literature in the same areas. PubDate: 2022-04-12
Abstract: Abstract A hybrid renewable energy system (HRES) is a promising power system for supplying electricity to remote communities. In this paper, four configurations of HRESs with energy storage have been designed and optimized in hybrid optimization model for electric renewable (HOMER) software for a remote community of Balnasari Qani village in Ghazni province, Afghanistan, upon on-site visit to determine the required electrical load and available energy resources. The site is located in a high mountain plateau and has potential to set up off-grid HRESs using solar, wind, and biomass resources. The optimized system is proposed to meet the electricity demands for 300 families. Results indicated that a HRES consisting of solar photovoltaic–biomass–diesel is the most optimal solution. This system is able to provide electricity at a levelized cost of 0.340 $/kWh with a net present cost of 411,491 $. Sensitivity analysis with parametric studies on the primary load suggested wind might not be a suitable energy source at the location. PubDate: 2022-04-10
Abstract: Abstract Although photovoltaic cells are good technology that converts sunlight into electricity, it suffers from low efficiency in hot weather conditions. Photovoltaic–thermal technologies (PV/T) have addressed the problem of overheating PV cells utilizing several cooling methods. These technologies can improve the electrical efficiency of PV cells and provide thermal energy simultaneously. This work presents an updated review of the most critical PV cooling technologies and their impact on electrical and thermal efficiency, in addition to the performance formulas for each technology. An analytical comparison of the results of the studies conducted on each technique is presented to determine the best performance obtained. The strengths and weaknesses are presented and the most effective techniques that can be relied upon to develop and popularize PV systems in the future. PubDate: 2022-04-05
Abstract: Abstract This paper presents the impact of the alternative fuels properties on the parameters characterizing the combustion process in a turbojet engine, expressed in the form of a mathematical model. Laboratory tests, bench tests and a regression analysis of the obtained results were conducted. The developed and published combustion process models were briefly described. It has been demonstrated that these models were insufficient in taking into account the impact of fuel properties on the course of the combustion process. The experimental data enabled developing a mathematical model of the combustion process using statistical methods. The developed model, unlike other currently known models, takes into account the chemical composition of the fuel to a greater extent, which is characterized by its physicochemical properties. Mathematical model enables predicting engine operating parameters and the emissions characteristics, based on analysing laboratory test results, and can be used as a tool verifying the environmental impact of new fuels, through predicting the exhaust gas emissions. PubDate: 2022-03-28
Abstract: Abstract Buildings account for one-third of the world’s energy consumption. Reducing this consumption is only possible by making buildings more energy efficient. One of the most efficient methods for increasing the energy efficiency of buildings is thermal insulation. Fuel consumption and therefore emission values can be reduced by achieving adequate thermal insulation of buildings. In this study, the optimum insulation thicknesses for cities in the various climatic regions of Turkey were determined using statistical methods. Insulation thickness, thermal conductivity, heating degree-days (HDD), and fuel type were determined as variable parameters, and the optimum insulation thickness and total heating cost for cities in four different climate zones were determined using the response surface method (RSM). The effect ratios for each parameter on total costs were also reviewed and analyzed using the RSM method. Mathematical models have been developed that estimate the total cost of natural gas, coal, and fuel oil based on thermal insulation thickness, thermal conductivity, and heating degree days. With the mathematical models presented in the study, dependent parameters (total heating cost) can be obtained as a function of independent parameters (fuel type, thermal conductivity of insulation material, and HDD). The models provide a calculation of direct costs for different types of fuels and provide a basis for various research. As a result, the optimum insulation thicknesses for İzmir (HDD: 1781), İstanbul (HDD: 2531), Ankara (HDD: 3303), and Erzurum (HDD: 5393) are 0.059 m, 0.066 m, 0.075 m, and 0.080 m, respectively; reductions in annual total costs were found to be 40.7%, 39.7%, 41.9%, and 50.1%, respectively. PubDate: 2022-03-20
Abstract: Abstract This paper presents an overview of power quality improvement of the distributed network by using a novel robust control algorithm. The novel robust control algorithm is modified fourth-order generalized integral action with frequency-locked loop feature (Mod.FOGI-FLL) for a three-phase grid-interfaced single-stage solar photovoltaic (PV) system with distribution static compensator capabilities. The functions taken into features for robust control algorithm (Mod.FOGI-FLL) are load balancing, reactive power compensation, power factor correction with active power generation for proposed topology, and behavior of proposed control with different faults at point of common coupling (PCC). The perturb and observe (P&O) algorithm is used to extract maximum power from the PV array under variable atmospheric conditions. To adapt with variation in terminal voltage at the point of common contact (PCC), PV feed-forward term is also added in the control algorithm to make fewer oscillations in grid current. Test results demonstrate satisfactory response for steady-state and dynamic conditions at load unbalancing, variations in insolation level, and fault profile at VSC. The DC rejection capabilities of a proposed control methodology obtained are around 60 decibel which is 22 decibel more than FOGI-FLL techniques with the same gain parameters 38 decibel. The distortion in grid currents and voltages is obtained from MATLAB/SIMULINK within limits IEEE-519 standard. PubDate: 2022-03-19
Abstract: Abstract An attribute technique is applied to forecast countrywide solar capacity. Attributes relate to the prior 12 h of a univariate, hourly time series. The approach avoids uncertainties relating to weather-related variables averaged at the country level. It captures impacts of system curtailments due to abnormal market conditions or grid-offtake limitations. Fifteen attributes relating to each hourly record are input to machine/deep learning (ML/DL) models. 43,824 h of solar capacity factor for Britain from 2015 to 2019 is evaluated. Fifteen ML/DL models are trained with 2015–2018 data with cross-validation. Trained models are then applied to forecast unseen 2019 hourly data. The ML/DL model forecast accuracy is compared with that of ARIMA and regression models. Extreme gradient boosting, random forest and adaptive boosting models outperform ARIMA and regression methods in forecasts for hours t0 to t + 12. Those three ML models are more accurate and faster to execute than six DL models evaluated. Suboptimal convergence and/or overfitting hinder the forecasts of DL models with unseen data. A transparent multi-linear regression model is used to identifying attribute influences on the different time period forecasts. The trend attributes are shown to influence the forecasts for different hours ahead in distinct ways. PubDate: 2022-03-18
Abstract: Abstract Variable refrigerant flow (VRF) air conditioning systems have become highly preferred in the air conditioning sector has enabled many new companies to enter the industries of VRF Air Conditioning Systems manufacturer and applicant. It has become difficult for decision-makers to select the best applicant company among the alternatives. In this article, the applicant company selection model is developed for heat-pump VRF air condition systems to meet the heating and cooling needs of buildings. The operation and structure of the VRF systems and their selection criteria are determined for the applicant company. Using the Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) method, one of the multi-criteria decision-making methods, an application company selection model has been presented for three different buildings according to the building characteristics and climate conditions. PubDate: 2022-03-13
Abstract: Abstract Photovoltaic (PV) power generation systems know widespread in the power generation world due to their production efficiency of clean energy. This system is exposed to several faults and errors during the production process, which reduces the quality and quantity of the produced energy, among the most common defects is partial shading. This paper proposes a simplified method for fault detection based on the generation of residual signals sensitive to these faults. For this detection, we have developed a model of the healthy photovoltaic system based on an artificial neural network (ANN). The output of this model is compared to the PV generator controlled by maximum power point tracking (MPPT) to form a residue used to feed a mechanism dedicated to fault detection. For the detection mechanism, an ANN was used as a fault classifier. The proposed method makes it possible to determine the percentage of partial shading, even in the presence of climate change. The results have been verified and validated using MATLAB/Simulink. PubDate: 2022-03-11
Abstract: Abstract The development of a robust control system for a variable speed wind turbine (VSWT) is presented in this paper. The proposed controller is designed for torque and pitch control of VSWT operation based on the uncertainty estimator and output feedback for the extraction of utmost power from the wind. A suitable reference model has been obtained for smooth tracking of rotor speed and estimating the uncertainty. The simulation study has been made to show the efficacy of the robust controller designed for VSWT. The performance of the proposed controller has been analyzed by a comparative evaluation with the standard controllers of the wind turbines in terms of the degree of rotor speed tracking, elimination of the effect of uncertainties, maximum power extraction, etc. It is found that the performance of the VSWT operation using the proposed control scheme has been improved significantly in comparison to a few existing control schemes. PubDate: 2022-03-10
Abstract: Abstract The effect of coating parameters of NMC622 cathodes and graphite anodes on their physical structure and half-cell electrochemical performance is evaluated by design of experiments. Coating parameters include the coater comma bar gap, coating ratio and web speed. The electrochemical properties studied are gravimetric and volumetric capacity, rate performance, areal specific impedance (ASI) and C-rate. Differences in the manufacturing effects on the electrode physical structure and electrochemical performance are observed between the electrodes and are modelled by linear regression. The effect of cell coating weight and porosity on half-coin cell electrochemical performance is also evaluated by linear regression. The cathode performance at high gravimetric and volumetric C-rates is mainly influenced by coating weight, whereas porosity is the only explanatory variable for volumetric C-rates of 1C and below. For anode, correlations are only found for the C/20 and 5C gravimetric and volumetric capacities and are related to coating weight. An inverse relationship between ASI and coating weight is observed for cathode, but in general the cell physical characteristics cannot completely explain the observed ASI for both electrodes. The obtained models are useful for the design and robust manufacturing of electrodes since present a quantitative relationship between the coating parameters, cell characteristics and final cell electrochemical performance. PubDate: 2022-03-04
Abstract: Abstract Energy sources generated from different modes other than the conventional types have become more demanding in combating climate change issues that the world has been facing today. To cater the need for electricity in countries of Africa like Ethiopia can be initiated by such modern practices. The hydro, wind, and solar power output of Bilate in the Central Rift Valley Basin was estimated envisaging satellite information and Hybrid Optimization Model for Electrical Renewable (HOMER). Three off-grid sites located in the central part of the catchment namely Shashego, Weira, and Siraro were considered in this study. The demographic and hydro-meteorological data of the selected sites were collected from various sources and ERA5 climate variables have been utilized at their optimum level to find a better and more accurate solution. Power transformation and variance scaling techniques were applied to correct biases in precipitation and temperature, respectively. Due to rarely available hydrological gauge stations in the catchment, there has been difficult to analyze the data of the stream flows at the sites and hence HBV-IHMS model was used during the study to find an amicable solution for the compliance. The performance of the model was checked before the use and resulted in NSE above 80% in replicating the observed hydrograph. The total power output of the best feasible hybrid system at Shashego, Weira and Siraro site is calculated as 26240 kW, 51298 kW and 46245 kW per annum, respectively. Except in Shashego, the configuration of hybrid systems in Weira and Siraro were technically and economically viable. The system was iteratively reconfigured to check the percent of the load demand of the Shashego site that could have been fulfilled with minimal LCOE but unpredictably it was found that only 50% of the load can be provided without any impairment. The LCOE of hydro is comparably very low in all the sites through the energy output from the scheme was inadequate to accomplish the required demand of the community. PubDate: 2022-03-04
Abstract: Abstract Proton exchange membrane fuel cells (PEMFC) can be considered as one of the most effective devices in power generation, which converts hydrogen to electrical power directly. The scalability of this system for stationary and mobile utilities is the main advantage of the PEMFC. But still, this technology is not fully mature. Nevertheless, there is a high demand to research in the field of fault detection and monitoring for PEMFC to promote its lifetime and performance and also decrease its operating expenses. Hence, an accurate model for these devices is essential. The accuracy of these models should be sufficient for providing the PEM fuel cell treatment to detect its various internal signals. The present paper aims to model the PEMFC system. The considered fuel cell utilizes the exit temperature in a closed loop. Therefore, the model should provide the thermal and electrical conditions. The Nexa-Ballard fuel cell with the 1.2 kW capacity is regarded here; thus, optimally obtaining the parameters is essential for presenting an accurate behavior. This paper presented four optimization methods in this regard. From these algorithms, the results of three methods are taken from other works, and the chaotic binary shark smell optimizer is suggested here. Eventually, validation of the proposed model by the obtained coefficients is performed using experimental data. PubDate: 2022-03-03
Abstract: Abstract This study focused on an industrial area, i.e., Champagne-Ardenne, France, containing 25 wind turbines with a lifespan of 25 years. We assessed the economic situation from the beginning of the operation of this plant to the end of its lifetime using the levelized cost of energy (LCOE) indicator, which assesses the average cost of energy production during a project. We also considered the environmental cost associated with the wind sector. The objective of this study was to explore the effects of all parameters, including the calculation of the LCOE indicator, to provide decision-makers and local authorities with optimization solutions. We also developed an optimization algorithm to provide the best combination of all LCOE parameters for producing sustainable energy at the lowest cost. PubDate: 2022-03-01
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