Variety Of Atmospheric Conditions Research Articles

Toward Better PV Panel’s Output Power Prediction; a Module Based on Nonlinear Autoregressive Neural Network with Exogenous Inputs

Much work has been carried out for modeling the output power of photovoltaic panels. Using artificial neural networks (ANNS), one could efficiently model the output power of heterogeneous photovoltaic (HPV) panels. However, due to the existing different types of artificial neural network implementations, it has become hard to choose the best approach to use for a specific application. This raises the need for studies that develop models using the different neural networks types and compare the efficiency of these different types for that specific application. In this work, two neural network types, namely, the nonlinear autoregressive network with exogenous inputs (NARX) and the deep feed-forward (DFF) neural network, have been developed and compared for modeling the maximum output power of HPV panels. Both neural networks have four exogenous inputs and two outputs. Matlab/Simulink is used in evaluating the proposed two models under a variety of atmospheric conditions. A comprehensive evaluation, including a Diebold-Mariano (DM) test, is applied to verify the ability of the proposed networks. Moreover, the work further investigates the two developed neural networks using their actual implementation on a low-cost microcontroller. Both neural networks have performed very well; however, the NARX model performance is much better compared with DFF. Using the NARX network, a prediction of PV output power could be obtained, with half the execution time required to obtain the same prediction with the DFF neural network, and with accuracy of ±0.18 W.

Atmospheric change as a driver of change in the Canadian boreal zone1

Global anthropogenic emissions of greenhouse gases and hazardous air pollutants have produced broad yet regionally disparate changes in climatic conditions and pollutant deposition in the Canadian boreal zone (the boreal). Adapting boreal resource management to atmospheric change requires a holistic understanding and awareness of the ongoing and future responses of terrestrial and freshwater ecosystems in this vast, heterogeneous landscape. To integrate existing knowledge of and generate new insights from the broad-scale impacts of atmospheric change, we first describe historical and present trends (∼1980–2015) in temperature, precipitation, deposition of hazardous air pollutants, and atmospheric-mediated natural disturbance regimes in this region. We then examine their associations with ecosystem condition and productivity, biological diversity, soil and water, and the carbon budget. These associations vary considerably among ecozones and likely undergo further changes under the emerging risks of atmospheric change. We highlight the urgent need to establish long-term, boreal-wide monitoring for many key components of freshwater ecosystems to better understand and project the influences of atmospheric change on boreal water resources. We also formulate three divergent future scenarios of boreal ecosystems in 2050. Our scenario analysis reveals multiple undesirable changes in boreal ecosystem structure and functioning with more variable atmospheric conditions and frequent land disturbances, while continuing business-as-usual management of natural resources. It is possible, though challenging, to reduce unwanted consequences to ecosystems through management regimes focussed on socio-ecological sustainability and developing resilient infrastructure and adaptive resource-management strategies. We emphasize the need for proactive actions and improved foresight for all sectors of society to collaborate, innovate, and invest in anticipation of impending global atmospheric change, without which the boreal zone will face a dim future.

Design and Fabrication of Evaporative Cooling Air Conditioner in Tractor Cabin

The Evolution of automotive air conditioning was a remarkable milestone in the history of mankind. It has played an important role in human comfort and to some extent in human safety during vehicle driving in varied atmospheric conditions. Present work focuses on providing comfort conditioning of a tractor cab which is a key factor in ensuring optimum working performance of the driver. A closed tractor cab acts like a greenhouse and its interior could become unbearable and sometimes even dangerous. Conventionally, vapour compression refrigeration systems are the standard for air conditioning in automobiles and account for up to 25 % of fuel consumption in the cooling season. Apart from conventional vapour compression technology, the work explores applicability of evaporative cooling in comfort conditioning of a tractor cabin which is an economical and eco-friendly alternative. The results procured are positive lower cabin temperature close to acceptable limit with less than 10 % of energy consumption compared to vapour-compression units when tested under similar hot-dry conditions.

Laboratory investigation of aerosol coating and capillarity effects on particle ice nucleation in deposition and condensation modes

In this work, the ice nucleating effectiveness of Arizona Test Dust (ATD), bare and coated with NaCl, not examined in previous published papers, was investigated. The ATD, NaCl coated ATD, and NaCl aerosols were generated by nebulizing water suspensions of bare ATD, ATD-NaCl and NaCl solution. Dried aerosols were sampled on black-gridded cellulose nitrate membrane filters for ice nuclei assessment through a Dynamic Filter Processing Chamber device (DFPC). After water evaporation ATD-NaCl droplets gave rise to ATD particles coated with NaCl.Processing of filters was performed at −22 °C and − 18 °C, under sub- and super-saturated conditions (water saturation ratio Sw < 1 and Sw > 1, respectively). Additional experiments were performed by modifying the storage conditions of the filters to simulate variation of atmospheric conditions, i.e. air temperature and relative humidity. For this purpose, the filters were housed in Petri dishes, with silica gel, at +10 °C and −10 °C. As silica gel can desorb water from the aerosol capillary at low temperatures, storage conditions with and without silica gel can simulate the aerosol pre-activation atmospheric process. The highest activated fraction (AF) and ice-active surface site density (ns) values were obtained for ATD at −22 °C and Sw = 1.02. Results showed a decrease in the AF and ns between bare and ATD coated with NaCl both at Sw = 1.02 and Sw = 0.96 and for filters stored in the presence of silica gel. The results obtained show the great influence exerted by NaCl coated insoluble aerosol on the ice nucleation.

Tabebuia rosea: a prospective non-edible biodiesel feedstock

The need to seek non-conventional seed oil sources for biodiesel production is significant due to issues including limited supply, increasing demand and availability as well as food versus fuel. In this context, Tabebuia rosea seed oil was investigated for the first time, as a potential non-conventional feedstock for preparation of biodiesel, as it is suitable for production in a variety of atmospheric conditions, easy to cultivate, and has a high fruit production rate and low cost of cultivation. Tabebuia rosea seeds were collected and processed and oil was extracted by Soxhlet extraction. The important physical and chemical properties of the extracted seed oil were experimentally analysed. The crude oil was transesterified under standard conditions with sodium methoxide as catalyst, to produce Tabebuia rosea oil methyl ester (TOME). The quality of TOME/biodiesel produced was assessed in terms of physico-chemical parameters such as kinematic viscosity, flash point, ash content, acid value, calorific value and sulphur content, which are the most important properties of a fuel for application in engines. Analysis revealed the tested parameters were comparable with American Society for Testing (ASTM) D6751 and Indian Standards (IS) 15607 standard specifications for biodiesel samples. It was concluded that T. rosea seed oil is a satisfactory feedstock for biodiesel production.

Improved MPPT method to increase accuracy and speed in photovoltaic systems under variable atmospheric conditions

The changes in temperature and radiation cause visible fluctuations in the output power produced by the photovoltaic (PV) panels. It is essential to keep the output voltage of the PV panel at the maximum power point (MPP) under varying temperature and radiation conditions. In this study, a maximum power point tracking (MPPT) method has been developed which is based on mainly two parts: the first part is adapting calculation block for the reference voltage point of MPPT and the second one is Fuzzy Logic Controller (FLC) block to adjust the duty cycle of PWM applied switch (Mosfet) of the DC-DC converter. In order to evaluate the robustness of the proposed method, Matlab/Simulink program has been used to compare with the traditional methods which are Perturb & Observe (P&O), Incremental Conductance (Inc. Cond.) and FLC methods under variable atmospheric conditions. When the test results are observed, it is clearly obtained that the proposed MPPT method provides an increase in the tracking capability of MPP and at the same time reduced steady state oscillations. The accuracy of the proposed method is between 99.5% and 99.9%. In addition, the time to capture MPP is 0.021 sec. It is about four times faster than P&O and five times faster than for Inc. Cond. and, furthermore, the proposed method has been compared with the conventional FLC method and it has been observed that the proposed method is faster about 28% and also its efficiency is about 1% better than FLC method.

Methods for Asssessing Energy Efficiency of Grain Dryers

Abstract The article presents an algorithm for calculating selected energy assessment indexes for grain driers. Particular attention was paid to the conversion of the equations obtained for the operating conditions of the drying process into equations allowing the calculation of indexes for the standard drying conditions. The indexes obtained allow a proper comparison of dryers operating in varied atmospheric conditions, as well as humidity and temperature of the dried material.

Design and Construction of Wireless Instrumentation System for Measuring Solar Module Performance Parameters

This paper presents the development of a wireless instrumentation system for accurate characterization and evaluation of solar modules performance parameters under variable atmospheric conditions. A wireless transmitter that operate at 5V (315MHz) was designed and constructed to transmit electrical parameters of three different solar panels measured via current, temperature and voltage sensors to a constructed Omni-directional receiver that is made up of inbuilt storage system. The measured electrical parameters from individual panels were evaluated in terms of their responses to variable atmospheric conditions and compared with that of manufacturer’s ratings. The results based on the plotted curves show that as individual panel temperature increases the output voltage also increases. Increase in output voltage result into increase in output current measured across the load resistor which also results into corresponding increase in output power of individual panel. The measured electrical parameters are within the manufacturer’s ratings range, except maximum output currents and maximum powers which show little differences from that of manufacturer’s ratings due to choice of load resistors used in the design which impede the free flow of current to some extent. Keywords : Design, Construction, wireless instrumentation, solar modules DOI : 10.7176/JNSR/9-10-09 Publication date :May 31 st 2019

Free Space Optical Communication System under Different Weather Conditions

Abstract Free space optical communication systems (FSO’s) have surfaced as admired means of communication in the past few years. High speed of operation, low bandwidth requirements and system reliability are the major factors responsible for their wide range of applications. These communication systems use air as a medium of transmission. Since there is no component like fiber or cable, but air is only medium, the variations in atmospheric conditions play a vital role in performance of these networks. The reason behind is that the conditions like presence of humidity, haze, snowfall, rain, dust or smoke changes the attenuation coefficient of medium. The raised attenuation levels results in increased losses and need to be carefully monitored. The present work analyzes the influence of rain on the performance of FSO network in terms of quality of transmission. The paper discusses the impact of rainfall on attenuation coefficient of air. Then impact of this attenuation on network transmission is presented in terms of BER and Q-factor. In order to demonstrate the impact, BER and Q-value is calculated for 10 Gbps FSO link for clear weather and rainfall conditions.

A computationally efficient multi-domain active learning method for crop mapping using satellite image time-series

ABSTRACTCrop mapping through classification of Satellite Image Time-Series (SITS) data can provide precious information for several agricultural applications, such as crop monitoring, yield forecasting, and crop inventory. However, several issues affect the classification performance of SITS data. As one of the most challenging problems, constituent images of time-series provide different levels of information about crops. These differences are the result of dynamic spectral responses of crops and also the variable atmospheric and sensor conditions. The second issue is the unavailability of adequate high-quality samples for training the classifier. In this study, we proposed a novel computationally efficient Multi-Domain Active Learning (MDAL) method which takes advantage of Multiple Kernel Learning (MKL) and Active Learning (AL) algorithms to address these two issues. The proposed method uses MKL algorithms to address the issues associated with different information level of the data, which generally cannot be modelled using the well-known classification algorithms. AL algorithms were also used for semi-automatic selection of training samples. However, most of the MKL algorithms are very computationally demanding. Consequently, using them in the MDAL method can dramatically increase the computational costs. Thus, in this paper, we presented the similarity-based MKL algorithms. Thanks to their low computational complexities, these algorithms are the most suitable MKL algorithms that can be used in the MDAL method. We evaluated the proposed method using two multispectral SITS datasets, acquired by RapidEye and SPOT sensors. The obtained results of the MDAL method for these datasets respectively showed 8.2% and 5.87% increase in the overall accuracy of classification as compared to the accuracy of the standard AL algorithm. The results also showed that in the case of adopting the SimpleMKL algorithm (a common MKL algorithm in the literature) the computational time of the MDAL method is 577 and 474 seconds for RapidEye and SPOT datasets, respectively. However, in the case of adopting the similarity-based MKL algorithms, these computational times respectively decreases to 4 and 2 seconds.

Using the Himawari-8 AHI Multi-Channel to Improve the Calculation Accuracy of Outgoing Longwave Radiation at the Top of the Atmosphere

In this study, Himawari-8 Advanced Himawari Imager (AHI) longwave channel data that is sensitive to clouds and absorption gas were used to improve the accuracy of the algorithm used to calculate outgoing longwave radiation (OLR) at the top of the atmosphere. A radiative transfer model with a variety of atmospheric conditions was run using Garand vertical profile data as input data. The results of the simulation showed that changes in AHI channels 8, 12, 15, and 16, which were used to calculate OLR, were sensitive to changes in cloud characteristics (cloud optical thickness and cloud height) and absorption gases (water vapor, O3, CO2, aerosol optical thickness) in the atmosphere. When compared to long-term analysis OLR data from 2017, as recorded by the Cloud and Earth’s Radiant Energy System (CERES), the OLR calculated in this study had an annual mean bias of 2.28 Wm−2 and a root mean square error (RMSE) of 11.03 Wm−2. The new calculation method mitigated the problem of overestimations in OLR in mostly cloudy and overcast regions and underestimated OLR in cloud-free desert regions. It is also an improvement over the result from the existing OLR calculation algorithm, which uses window and water vapor channels.

Recovery of vanadium from calcification roasted-acid leaching tailing by enhanced acid leaching.

Vanadium contained tailing generated from the typical calcium roasting-acid leaching process is contaminant and waste of resource. Atmospheric and pressurized leaching were conducted and compared to recover the vanadium from the vanadium tailing and improve the tailing's environmental quality. Orthogonal experiments were designed and applied for the atmospheric leaching study. It is shown that the extraction efficiency of V changed from 29.6% to 43.5% while the extraction efficiency of Fe, Cr, Ti, Mg remained stable under 4% with variable atmospheric leaching conditions. In the pressurized leaching experiments, the effects of leaching temperature, H2SO4 concentration, liquid to solid ratio and leaching time on the extraction of V, Fe, and Ti were investigated. Under the optimum conditions (the temperature of 413.15 K, H2SO4 concentration of 300 g/L, liquid to solid ratio of 8:1 mL/g and the reaction time of 100 min), the extraction efficiencies of V, Fe, and Ti reached 91.7%, 60.1% and 46.5% respectively, a leach residue contains only 0.13% of stable vanadium was obtained.

IC-based Variable Step Size Neuro-Fuzzy MPPT Improving PV System Performances

This paper addresses the development of a Neuro-Fuzzy IC variable step size MPPT controller. Firstly, the proposed MPPT controller is developed in an offline mode required for testing different set of neural network architectures and parameters, using the Fuzzy-based IC variable step size MPPT data. Secondly, the optimal found neural network controller is then used to track the output power of the PV system in an online mode. The inputs variables for the proposed neural network controller are same as for the Incremental Conductance algorithm inputs (i.e. I and V); while the output is the PWM ratio used to drive the DC-DC boost converter. The effectiveness of proposed Neuro-Fuzzy IC variable step size MPPT controller is investigated by implementing the model of the entire system using Matlab/Simulink environment, composed of Solarex MSX-60W PV panel operating at variable atmospheric conditions and DC-DC boost converter drived using the proposed controller. Simulation results prove that the proposed variable step size Neuro-Fuzzy IC MPPT outperforms the classical fixed step size IC MPPT in all considered performance measures which leads to the improvement of the output power and consequently the reduction of power losses.

Impact of solar radiation variation on the optimal tilted angle for fixed grid-connected PV array—case study in Beijing

A key design parameter for fixed grid-connected photovoltaic (PV) arrays, the optimal tilt angle, does not only depend on the geographic location but is also directly affected by atmospheric conditions. In this paper, long-term variations of solar radiation (i.e. global solar irradiance, direct horizontal irradiance, diffuse irradiance, and ratios of direct and diffuse irradiance) in Beijing are considered to determine their effect on the optimal tilt angle for a fixed grid-connected PV array. We found that there is a declining trend in global solar irradiance over the past 55 years, mainly caused by the decreased direct horizontal irradiance. In contrast, the decline of diffuse irradiance is not obvious, leading to a considerable decrease in the direct irradiance ratio and consequent increase in the diffuse irradiation ratio. Likewise, the long-term optimal tilt angle shows a downward trend. Compared with the optimum in the 1960s, the optimal tilt angle has decreased by 2° in 2011–2015. These results suggest that the declining trend in the optimal tilt angle is mainly caused by the decrease in direct irradiance ratio, which is highly related to atmospheric conditions. Therefore, the design and construction of PV power stations must consider the variations of atmospheric conditions and solar irradiance to determine the optimal tilt angle.

Hydrologic impact of aspen harvesting within the subhumid Boreal Plains of Alberta

AbstractThis study examined the hydrological impact of harvesting aspen (Populus tremuloides) within the subhumid Boreal Plains of Alberta, Canada, following clear‐cutting of two stands in successive winters within a small catchment situated in glacial moraine. Impacts were evaluated using a combination of observational data collected from within the harvested catchment and an adjacent reference catchment and numerical simulations conducted with HydroGeoSphere. Sensitivity simulations evaluated the influence of post‐harvest evapotranspiration rates and variability in atmospheric conditions on the range of hydrologic response. Study results indicate that aspen harvesting had limited impact on groundwater levels and streamflows within these hydrologic systems because of the subhumid climate with low frequency of large storms, large soil‐moisture storage capacity of heterogeneous glacial materials, and high evapotranspiration rates of regenerating aspen. Despite an estimated increase in hillslope groundwater levels of up to 3 m, pond and peatland water levels increased by less than 0.3 m and were accompanied by increased streamflows of less than 10 mm. However, predicted increases in groundwater levels and streamflows were sensitive to regenerating aspen evapotranspiration rates, which can be enhanced by appropriate harvesting techniques but may be reduced by climate change. These results are consistent with previous results from within the Boreal Plains but differ from aspen harvesting studies conducted in other settings where appreciable increases in streamflows have been reported. This disparity highlights the need to consider the integrated response of the hydrologic system when evaluating impacts from disturbance and making comparisons between settings.

Grid Secondary Frequency Control by Optimized Fuzzy Control of Electric Vehicles

The major debates on environmental pollutions, abundant availability of renewable resources, scarcity of fossil fuel resources in certain parts of the world, and geopolitical conflicts have underscored the need for promoting the use of renewable and distributed power generation. Since the availability of renewable energy resources, such as wind energy depends on variable atmospheric conditions, a massive integration of renewable energy would require additional reserves to compensate the power deficiency for supplying loads. However, the increasing use of electric vehicles (EVs) along with their charging demands can apply a significant amount of load to the power grid, which can cause stability concerns if not control properly. In this paper, EVs charges are applied to the secondary frequency control and an optimized fuzzy controller is considered to compensate the variable unbalances between demand and generation. To verify the performance of the proposed control method for reducing frequency deviations, several case studies are considered in this paper and simulations are carried out in MATLAB/SIMULINK environment. The simulation results illustrate a good performance of proposed method.

Pseudoinvariant feature selection for cross-sensor optical satellite images

Processing of multitemporal satellite images generally suffers from uncertainties caused by differences in illumination and observation angles, as well as variation in atmospheric conditions. Moreover, satellite images acquired from different sensors contain not only the uncertainties but also disparate relative spectral response. Given that radiometric calibration and correction of satellite images are difficult without ground measurements during data acquisition, this study addresses pseudoinvariant feature selection for relative radiometric normalization (RRN) that minimizes the radiometric differences among images caused by atmospheric and spectral band inconsistencies during data acquisition. The key to a successful RRN is the selection of pseudoinvariant features (PIFs) among bitemporal images. To select PIFs, multivariate alteration detection (MAD) algorithm is adopted with kernel canonical correlation analysis (KCCA) instead of canonical correlation analysis (CCA). KCCA, which assumes that the relation between at-sensor radiance is spatially nonlinear, can obtain more appropriate PIFs for cross-sensor images than that of CCA, which assumes that the relation between the at-sensor radiances of bitemporal image is spatially linear. In addition, a regularization term is added to the optimization of KCCA to avoid trivial solutions and overfitting. Qualitative and quantitative analyses on bitemporal images acquired by Landsat-7 enhanced thematic mapper plus and Landsat-8 operational and imager sensors were conducted to evaluate the proposed method. The experimental results demonstrate the superiority of the proposed KCCA-based MAD to the CCA-based MAD in terms of PIF selection, particularly for images containing significant cloud covers.

A novel single sensor PV MPPT controller

This paper proposes a novel single sensor maximum power point tracking method for PV system composed of Solarex MSX-60W PV panel operating at variable atmospheric conditions and DC-DC boost converter controlled using the proposed MPPT that uses only one sensor in order to reduce the cost and the complexity of the MPPT controller. The performance of the proposed single sensor MPPT controller is evaluated using the implemented Matlab/Simulink PV power system model. Simulation results show that the proposed single sensor MPPT controller can provide similar results using only one sensor compared to the conventional two sensors MPPT.

Arbuscular Mycorrhizas: A Promising Component of Plant Production Systems Provided Favorable Conditions for Their Growth.

Arbuscular mycorrhizal (AM) fungi have become an attractive target as biostimulants in agriculture due to their known contributions to plant nutrient uptake and abiotic stress tolerance. However, inoculation with AM fungi can result in depressed, unchanged, or stimulated plant growth, which limits security of application in crop production systems. Crop production comprises high diversity and variability in atmospheric conditions, substrates, plant species, and more. In this review, we emphasize that we need integrative approaches for studying mycorrhizal symbioses in order to increase the predictability of growth outcomes and security of implementation of AM fungi into crop production. We briefly review known mechanisms of AM on nutrient uptake and drought tolerance of plants, on soil structure and soil hydraulic properties. We carve out that an important factor for both nutrient availability and drought tolerance is yet not well understood; the AM effects on soil hydraulic properties. We gave special emphasis to circular references between atmospheric conditions, soil hydraulic properties and plant nutrient and water uptake. We stress that interdisciplinary approaches are needed that account for a variability of atmospheric conditions and, how this would match to mycorrhizal functions and demands in a way that increased plant nutrient and water uptake can be effectively used for physiological processes and ultimately growth. Only with integrated analyses under a wide range of growing conditions, we will be able to make profound decisions whether or not to use AM in particular crop production systems or can adjust culture conditions in ways that AM plants thrive.

Promising Oldest Ice sites in East Antarctica based on thermodynamical modelling

Abstract. To resolve the mechanisms behind the major climate reorganisation, which occurred between 0.9 and 1.2 Ma, the recovery of a suitable 1.5 million-year-old ice core is fundamental. The quest for an Oldest Ice core requires a number of key boundary conditions, of which the poorly known basal geothermal heat flux (GHF) is lacking. We use a transient thermodynamical 1-D vertical model that solves for the rate of change of temperature in the vertical, with surface temperature and modelled GHF as boundary conditions. For each point on the ice sheet, the model is forced with variations in atmospheric conditions over the last 2 Ma and modelled ice-thickness variations. The process is repeated for a range of GHF values to determine the value of GHF that marks the limit between frozen and melting conditions over the whole ice sheet, taking into account 2 Ma of climate history. These threshold values of GHF are statistically compared to existing GHF data sets. The new probabilistic GHF fields obtained for the ice sheet thus provide the missing boundary conditions in the search for Oldest Ice. High spatial resolution radar data are examined locally in the Dome Fuji and Dome C regions, as these represent the ice core community's primary drilling sites. GHF, bedrock variability, ice thickness and other essential criteria combined highlight a dozen major potential Oldest Ice sites in the vicinity of Dome Fuji and Dome C, where GHF could allow for Oldest Ice.