Sunshine Fraction Research Articles

Water consumption assessment in mineral processing integrating weather information and geometallurgical modeling

Water is essential in mineral processing. Because water is a limited resource and many mining operations are located in water-scarce areas, proper water management is crucial. An alternative to facing water scarcity in mining is its reuse in the process. Water recirculation is limited because there are water losses associated with the plant operation and the tailings facility. Tailings composition and particle size affect the losses related to entrainment and rewetting. These parameters can be estimated by simulating the plant using geometallurgical models. Weather variables such as radiation, temperature, wind speed, and the fraction of possible sunshine, impact the evaporation expected in the tailings facility. The variability of weather conditions throughout the year implies that evaporation depends on the season. This information is used to assess the water balance in a tailings facility located in a specific region, estimating the water consumption for a given operation. Water consumption can be economically valued and included in the mine plan. This methodology is used to provide examples where a mine plan is optimized incorporating water consumption and using efficient water management criteria.

Performance Evaluation of Newly Developed Generalized Correlations for the Prediction of Solar Diffuse Fraction for Various Climatic Regions

Abstract The objective of this work is to develop empirical correlations describing Diffuse Fraction (DF) as a function of (1) Sunshine Fraction (SF), (2) Clearness Index (CI) and (3) both SF and CI. Four years instantaneously measured data was changed to monthly data at five locations belonging to five different climatic regions in Pakistan which was used as training dataset and nine correlations for each location (a total of forty-five) were formulated and their performance was assessed. Moreover, nine general empirical models were developed using the entire dataset (11 years) for five locations which were termed as Generalized Correlations (GCs). These GCs were validated by applying them to five other locations and comparing the generated results with measured results for those locations (validation dataset). The best model among GCs were found as GC8 which was, then applied to compute DF for five more locations for which short term (8 months) measured data was also available and thus a reasonable comparison could be made. Results showed that (1) new models were better than literature models, (2) GCs correlations were found in good agreement and (3) 2nd degree multivariate polynomial models are the best performance models with minimum errors e.g. Mean Absolute Biased Error (MABE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Sum of Square of Relative Error (SSRE) and Standard Relative Error (SRE) for GC8 were estimated as 0.018, 6.397, 0.021, 0.006 and 0.022 respectively (all values for Karachi).

Prediction of diffuse radiation in solar energy applications: Turkey case study and compare with satellite data

In solar engineering applications, the components of the solar radiation a location must be known to design the systems. Due to the difficulties in the diffuse solar radiation (DSR) measurements, as this component has not been measured in everyplace, the DSR models have been established for various locations over the world. In this study, the models developed to constitute a relation between the monthly mean daily diffuse fraction and diffuse coefficient with clearness index and sunshine fraction in the open literature for Turkey are reviewed. In addition, the comparisons of the DSR values taken from the NASA – Surface meteorology and Solar Energy (NASA-SSE) and computed from the best models for example two cities are carried out. The performances of the models are investigated by various statistical test techniques. These statistical test techniques are root mean square error (RMSE) and mean absolute error (MAE). The statistical comparisons show that the best models give well agreement with the DSR values taken from the NASA-SSE. The difference between the yearly total DSR calculated from the best models and NASA-SSE is approximately 1.94% for Erzurum and 0.57% for Gebze.

The improvement and comparison of diffuse radiation models in different climatic zones of China

Diffuse radiation is vital for climatology, sustainable energy, agriculture and biological activities. It is often estimated based on some meteorological factors but seldom takes total cloud cover into consideration. In this study, cloud cover data are used to establish models, compared with other regular models for estimating diffuse radiation in different climatic zones of China during 1992–2015. The results showed that the performance of the new model based on sunshine fraction (n/N), clearness index (kt), total cloud cover (CI), air temperature (Ta), relative humidity (RH), wind speed and the day of the year outperformed other models and further improved the predictive accuracy. Generally, the kd models based on the single kt was generally more precise compared with single n/N-based or single CI-based models. And models on the basis of multiple factors performed better than the single factor-based model. Comparisons between these models indicated that neural network models (with the largest R and smallest RMSD values) provided better overall accuracy than other models. The parameter of CI as the co-factor can improve the prediction of diffuse radiation. The models developed and evaluated in this study can contribute to developing and utilizing solar energy in China, especially in areas without diffuse radiation records.

Establishing New Diffuse Solar Radiation Equations for Antalya, Turkey

In this paper new diffuse solar radiation equations were established for Antalya, Turkey. For this purpose current equations from the literature which were functions of the clearness index and/or the sunshine fraction were examined. 9 equations were selected to determine the regression constants of the new equations. In conclusion the best equation among the new equations was introduced on the basis of different statistical indicators.

A Markov-chain probability distribution mixture approach to the clear-sky index

This paper presents a Markov-chain probability distribution mixture approach to the clear-sky index (CSI). The main assumption is that the temporal variability of the state of clear and the state of cloudy can be described by a two-state Markov-chain, and the variability within each state can be approximated by a probability distribution, unique for each state. Measurables such as the mean clear-sky index, fraction of bright sunshine, expected duration of clearness and expected duration of cloudiness events are shown to be related to the parameters of the method. Additionally, the Ångström equation, which relates mean normalized solar irradiance to the fraction of bright sunshine, is shown to arise as the expectation of the method. In order to numerically verify the method, a simulation model is constructed based on data sets for two different climatic regions: Norrköping, Sweden and Oahu, Hawaii, USA. Results from the simulation model based on training data shows good agreement with testing data, and when comparing the results to existing models in the literature it is comparable to the state of the art. It is shown that the simulation model generates a non-trivial, generally non-zero, autocorrelation function. Finally, challenges with the method and open problems are discussed.

Determining optimum tilt angles of solar surfaces in Sakarya, Turkey

This paper proposes the best tilting case for Sakarya city, Turkey. The main contribution of this work is the correlation of the tilt angle and the output of a photovoltaic (PV) module mathematically. For this purpose, new diffuse solar radiation equations are developed using existing equations in three groups: (i) group 1: the diffuse fraction (K d ) calculated by the clearness index (K t ); (ii) group 2: the diffuse fraction calculated by the sunshine fraction (K n ); and (iii) group 3: the diffuse fraction calculated by both the clearness index and the sunshine fraction. The most suitable equation of new and existing equations is selected for the city on the basis of different statistical indicators. This equation is used to predict the monthly average daily diffuse, beam, and total solar radiation for the city. Using the predicted data, optimum tilt angles of solar surfaces for different time periods are established. Finally, a PV module is modelled in Matlab and output of the module tilted at optimum tilt angles is evaluated.

Comparison of the daily global solar radiation from different data sources in Northwest China climate

ABSTRACTThe solar radiation data are of high importance to the solar energy systems. Conventional methods to obtain the solar radiation data are from weather stations, solar radiation models, commercial software databases, and field measurements. In the present study, a new daily global solar radiation model is proposed, by combining the quadratic function of sunshine fraction and sine function of the day of the year. The solar radiation model calculated data are then compared with China Meteorological Data Sharing System (CMDSS) data, TRNSYS data, and field-measured data in Northwest China climate. It is found that the newly proposed solar radiation model has better performance than the other nine solar radiation models in the literature. The solar radiation model calculated data fit well with the CMDSS annually average data. The TRNSYS data are a bit larger than the CMDSS annually average data in summer half year and a little smaller than those in winter half year. The solar radiation model and the CMDSS annually average data have the best correlation, whereas the TRNSYS data and the field-measured data have the worst correlation. The solar radiation model calculated data have the best correlation with the other three data sources.

Mapping and analyzing the spatial distribution of thetribe Triticeae Dumort. (Poaceae) in Turkey

Triticeae Dumort. (Poaceae), or true grasses, the fifth-largest plant family in the world, are of great importance in terms of human life and civilization. Therefore, understanding the spatial distribution of the family members is important for botanists, plant producers, and breeders. Turkey is one of two gene centers of this family. In this study, the spatial distribution of Triticeae in Turkey was mapped in geographic information systems (GIS) by utilizing a large number of specimens collected from 1006 georeferenced sampling sites between 2004 and 2010. Canonical correspondence analysis (CCA) was carried out in order to understand the spatial distribution of determined taxa. Environmental data for CCA were extracted from the available environmental raster map layers of Turkey. We identified and mapped a total of 76 species including 42 annuals and 34 perennials. CCA indicated that 6 environmental variables, namely potential evapotranspiration, elevation, distance to seas, maximum temperature, slope, and longitude, are the most effective in explaining the spatial distribution of the 12 annual species. However, the 6 environmental variables potential evapotranspiration, water vapor pressure, latitude, longitude, distance to seas, and sunshine fraction were the most effective in explaining the spatial distribution of the 20 perennial species. Aegilops triuncialis L. subsp triuncialis (annual) and Hordeum bulbosum L. (perennial) were the most abundant and widespread taxa, while Aegilops juvenalis (Thell.) Eig (annual) and Elymus libanoticus (Hack.) Melderis (perennial) were the scarcest and most rarely distributed species.

Comparative Evaluation of Spatial Interpolation Methods for Estimation of Missing Meteorological Variables over Ethiopia

In developing countries like Ethiopia where there is abundant water resources potential and also luck of reliable meteorological quality data, it expected to face the problem of missing meteorological data. Therefore, in conducting any water resources studies in any river basin for water resource project planning and management (like small scale irrigation), the first step before starting data analysis is to fill up the missing values of the meteorological variables (like rainfall, temperature, sunshine, wind speed etc.) which are required to start the study. One way of filling these missing variables is using datasets from other stations in the surrounding and applying appropriate spatial interpolation methods. A lot of studies have been conducted around the world to identify which method is the best to be applied to particular study area among the available spatial interpolation techniques. But when we come to Ethiopia, the study area, few or no studies are conducted to recommend the best performed method. Therefore, the objective of this paper is to conduct comparative evaluation of five interpolation techniques Nearest Neighbour (NN), Inverse Distance Weighting Average (IDWA), Modified Inverse Distance Weighting Average (MIDWA), Kriging Method (KM) and Thin Plate Spline (TPS) for estimation of four climatic variables (rainfall, mean temperature, wind speed and sunshine fraction) over complex topography of Ethiopia. Performance assessment is done using Mean Error (ME), Mean Absolute Error (MAE), Mean Relative Error (MRE) and Root Mean Square Error (RMSE); and the number of the meteorological stations selected for validation is ten (10) and these are distributed over the study area taking into account the variation of elevation ranging from 860 m (Awash) to 2420 m (Debremarkos) above sea level. The radial distances of 100 km and 200 km were selected and it was found that 100 km radial distance was not appropriate to compare all methods as some variables could not be estimated by KM and TPS. Therefore, 200 km was selected for further analysis and the result showed that NN, IDWA, and MIDWA were best methods relative to the remaining two methods (KM and TPS) for all variables and all stations except at Dire Dawa and Addis Ababa-Bole for estimation of wind speed using all methods except NN, and rainfall using TPS, respectively. Hence, NN, IDWA, and MIDWA methods could be used for estimation of missing meteorological variables over Ethiopia whenever necessary.

Estimation of available global solar radiation using sunshine duration over South Korea

Besides designing a solar energy system, accurate insolation data is also a key component for many biological and atmospheric studies. But solar radiation stations are not widely available due to financial and technical limitations; this insufficient number affects the spatial resolution whenever an attempt is made to construct a solar radiation map. There are several models in literature for estimating incoming solar radiation using sunshine fraction. Seventeen of such models among which 6 are linear and 11 non-linear, have been chosen for studying and estimating solar radiation on a horizontal surface over South Korea. The better performance of a non-linear model signifies the fact that the relationship between sunshine duration and clearness index does not follow a straight line. With such a model solar radiation over 79 stations measuring sunshine duration is computed and used as input for spatial interpolation. Finally monthly solar radiation maps are constructed using the Ordinary Kriging method. The cross validation results show good agreement between observed and predicted data.

Towards increasing availability of the Ångström–Prescott radiation parameters across China: Spatial trend and modeling

Application of the Ångström–Prescott (A–P) model, one of the highly rated global solar radiation (Rs) models based on sunshine, is often limited by scarcity of model parameters. Increasing availability of its parameters in the absence of Rs measurements is an effective way to overcome this problem. Although the A–P parameters have been extensively studied and calibrated worldwide, models relating them to easily obtainable variables are less investigated. Furthermore, it is uncertain whether the existing models are applicable under Chinese climate. Using a two-step approach and data from 80 sites covering three agro-climatic zones in China, this paper focuses on spatial modeling of the A–P parameters in order to increase their availability and consequently to extend the applicability of the model. It was found that the countrywide average was a=0.18, b=0.56 and (a+b)=0.75, and that a had the largest spatial variability followed by b and (a+b). Values of a and (a+b) increased from east towards west while b showed no regular trend. The influence of various variables on the parameters was investigated, finding that a was mostly affected by P (annual average precipitation) followed by n/N‾ (annual average daily sunshine fraction), Z (altitude), T (annual average daily temperature), ϕ (longitude) and φ (latitude), while b was only influenced by Z, and the sum (a+b) mostly by Z followed by T, ϕ, P and n/N‾. Parameter models of varying complexity were proposed and validated. The three-variable model with Z, n/N‾ and ϕ for a and Z, φ and ϕ for (a+b) performed the best. However, the best- and the poorest-performing model obtained Rs prediction differed insignificantly from the calibrated parameters. Particularly, both Z and P alone could be used with confidence to predict these parameters, implying a large potential for simplifying the current parameter models. We therefore recommend choosing the simplest one-variable models in applications. Meanwhile, we question the necessity of the earlier developed complex models. Variation in model for b affected little on Rs prediction, while accurate modeling for a was more important. This questions the current approach of modeling in b: are efforts for model of b aiming at improving its prediction worthwhile? Could b be considered as a constant across whole China? These questions deserve further investigation. The findings in this study using large datasets have both theoretical implication in guiding the research direction of the A–P model and practical implications in increasing parameter availability and thus in facilitating a wider application of the A–P model.

Spatial modelling of rheumatoid arthritis in Turkey by geographic information systems (GIS)

We described the recent spatial distribution of rheumatoid arthritis in Turkey and assessed the role of environmental variables in this distribution. We developed an observed rheumatoid arthritis (RA) incidence grid map by using georeferenced rheumatoid arthritis case data (2011) from the centres of 81 provinces and the kriging method with a spherical variogram model in geographic information systems (GIS). We also modelled rheumatoid arthritis incidence in GIS by using complementary spatial database including the grid map layers of 14 environmental variables of Turkey. We conducted principle component analysis and multiple regression to investigate the relationships among variables and develop a model, respectively. The produced model was run in GIS to obtain a predicted (model) RA map. We tested the reliability of the model map by residual statistics and found the model map dependable. Observed and model incidence maps revealed the geographic distribution of rheumatoid arthritis cases in Turkey. The mean temperature, minimum temperature, maximum temperature, water vapour pressure, elevation, potential evapotranspiration, latitude, distance to seas, sunshine fraction, precipitation, longitude and aspect variables were found to have significant impacts on rheumatoid arthritis. Consequently, the model incidence map established a good background to predict rheumatoid arthritis cases following environmental changes.

Relationship between sunshine duration and solar radiation

This paper examines the relationship between sunshine duration and solar radiation received on the earth’s surface. Sixty-nine thousand pairs of sunshine–radiation readings from 670 sites were analyzed. A generalization of the Ångström–Prescott equation of the form K¯=K¯clear[β+(1-β)Sγ] was found to most efficiently fit the data and suggests the relationship between the average daily atmospheric transmittance K¯ and the sunshine fraction S is non-linear. The suggested reason for this non-linearity is that a reduced sunshine fraction not only decreases the clear sky radiation duration, but also the radiation transmitted through clouds, i.e. clouds get optically thicker with decreasing S. This finding is supported on theoretical grounds and by analyzing instantaneous solar radiation measurements from Australia and Germany.Representing the sunshine fraction in terms of the proportion of beam radiation reaching the earth’s surface S=H¯b/H¯b,clear leads to a fundamental connection between the monthly average diffuse fraction and the sunshine–radiation relationship. Moreover, it confirms the non-linearity of the latter relationship, which was previously questioned because of limited data and/or poor quality sunshine measurements.

Quality control and estimation hourly solar irradiation on inclined surfaces in South Korea

The measured data of global solar irradiation on a horizontal surface, the number of bright sunshine hours, and the amount of cloud cover for major cities of South Korea, during the period (1986–2005) are analyzed. Quality controls tests were carried out to eliminate spurious data and inaccurate measurements resulting from the cosine response error of the pyranometers. Accordingly, in six locations of South Korea where hourly global solar irradiation is currently measured, the hourly global solar irradiation was calculated using two Sunshine Fraction Radiation Model (SFRM) and a Cloud Cover Radiation Model (CRM), were compared and analyzed. Data for Ulsan, where hourly global solar irradiation is not currently measured, can be calculated from the data of the six other locations. The total solar irradiation on horizontal surfaces is separated into the beam and diffuses components. Empirical correlations for estimating the diffuse solar irradiation incident on horizontal surfaces have been proposed. The purpose of our work consists of predicting the solar irradiation on inclined surfaces based on horizontal measurements. To reach this goal, two studies were performed. They consisted of quantifying the accuracy of various well-known models. The first type of models calculated diffuse horizontal irradiations from global ones and the second type computed global irradiations on inclined planes from diffuse and global components on a horizontal surface. The proposed model can provide an alternative to building designers in estimating the solar irradiation on inclined surfaces where only the horizontal measurements are available.

Solar Energy Potential Assessment in the Algerian South Area: Case of Ghardaïa Region

Due to its geographical position in the solar belt, Algeria is blessed with an abundance of solar energy and has the opportunity to utilize this bounty of natural energy effectively, promoting a clean environment and developing renewable energy technologies in the region. This paper assesses and analyses the solar energy potential in Ghardaïa area (south Algeria) to help users for solar energy applications. A database of solar radiation components has been employed for this purpose. The data presented in the paper are compared with other data supplied by renowned regional and international establishments, such as the solar atlas for the Mediterranean and the NASA. The frequency and the hourly distribution of solar radiation components indicate that the region is considered as an economical area and favorable for solar applications, such as the photovoltaic and the concentrating solar power (CSP) technologies. In addition to solar radiation, data of two functions, namely, clearness index and sunshine fraction have been examined; their results specify that the site has a clear sky conditions in most time of the year. In general, the obtained results illustrate that the site under consideration can use solar energy as a promising solution to conventional energy.

Comparative Study in Predicting the Global Solar Radiation for Darwin, Australia

This paper presents a comparative study in predicting the monthly average solar radiation for Darwin, Australia (latitude 12.46 deg S longitude 130.84 deg E). The city of Darwin, Northern Territory (NT), has the highest and most consistent sunshine duration among all the other Australian states. This unique climate presents an opportunity for photovoltaic (PV) applications. Reliable and accurate predictions of solar radiation enable potential site locations, which exhibit high solar radiations and sunshine hours, to be identified for PV installation. Three predictive models were investigated in this study—the linear regression (LR), Angstrom–Prescott–Page (APP), and the artificial neural network (ANN) models. The mean global solar radiation coupled with the climate data (mean minimum and maximum temperatures, mean rainfall, mean evaporation, and sunshine fraction) obtained from the Australian Bureau of Meteorology (BoM) formed the basis of the dataset. Using simple and easily obtainable climate data presents an added advantage by reducing model complexity. Predictive results showed the root mean square errors (RMSEs) obtained were 6.72%, 13.29%, and 8.11% for the LR, APP, and ANN models, respectively. The predicted solar exposure from the LR model was then compared with the satellite-derived data to assess the accuracy of the LR method.

Active Trachoma among Children in Mali: Clustering and Environmental Risk Factors

BackgroundActive trachoma is not uniformly distributed in endemic areas, and local environmental factors influencing its prevalence are not yet adequately understood. Determining whether clustering is a consistent phenomenon may help predict likely modes of transmission and help to determine the appropriate level at which to target control interventions. The aims of this study were, therefore, to disentangle the relative importance of clustering at different levels and to assess the respective role of individual, socio-demographic, and environmental factors on active trachoma prevalence among children in Mali.Methodology/Principal FindingsWe used anonymous data collected during the Mali national trachoma survey (1996–1997) at different levels of the traditional social structure (14,627 children under 10 years of age, 6,251 caretakers, 2,269 households, 203 villages). Besides field-collected data, environmental variables were retrieved later from various databases at the village level. Bayesian hierarchical logistic models were fit to these prevalence and exposure data. Clustering revealed significant results at four hierarchical levels. The higher proportion of the variation in the occurrence of active trachoma was attributable to the village level (36.7%), followed by household (25.3%), and child (24.7%) levels. Beyond some well-established individual risk factors (age between 3 and 5, dirty face, and flies on the face), we showed that caretaker-level (wiping after body washing), household-level (common ownership of radio, and motorbike), and village-level (presence of a women's association, average monthly maximal temperature and sunshine fraction, average annual mean temperature, presence of rainy days) features were associated with reduced active trachoma prevalence.Conclusions/SignificanceThis study clearly indicates the importance of directing control efforts both at children with active trachoma as well as those with close contact, and at communities. The results support facial cleanliness and environmental improvements (the SAFE strategy) as population-health initiatives to combat blinding trachoma.

A comparative study to estimate daily diffuse solar radiation over India

Abstract A detailed study of monthly average daily diffuse solar radiation for selected Indian locations have been performed using five years (2001–2005) measured data. The data of four prominent locations (Jodhpur, Calcutta, Bombay and Pune), representing varying weather conditions of the entire country, have been taken for the present study. The correlations between the diffuse fraction (Hd/H) and the sunshine fraction (S/S0) have been developed using regression analysis method for each selected location as well as for all Indian locations, we call it All India Correlation (AIC). The results obtained from present AIC are well compared with the measured data along with the estimates of Liu and Jordan, Gopinathan and Iqbal for different locations. The comparisons between various data conclude that AIC can be used to estimate diffuse fraction for any location in India. For further validation and to show the accuracy of present correlations, statistical tests of root mean square error (RMSE), mean bias error (MBE) and mean percentage error (MPE) are also performed.

Diffuse solar radiation estimation models for Turkey’s big cities

A reasonably accurate knowledge of the availability of the solar resource at any place is required by solar engineers, architects, agriculturists, and hydrologists in many applications of solar energy such as solar furnaces, concentrating collectors, and interior illumination of buildings. For this purpose, in the past, various empirical models (or correlations) have been developed in order to estimate the solar radiation around the world. This study deals with diffuse solar radiation estimation models along with statistical test methods used to statistically evaluate their performance. Models used to predict monthly average daily values of diffuse solar radiation are classified in four groups as follows: (i) From the diffuse fraction or cloudness index, function of the clearness index, (ii) From the diffuse fraction or cloudness index, function of the relative sunshine duration or sunshine fraction, (iii) From the diffuse coefficient, function of the clearness index, and (iv) From the diffuse coefficient, function of the relative sunshine duration or sunshine fraction. Empirical correlations are also developed to establish a relationship between the monthly average daily diffuse fraction or cloudness index ( K d) and monthly average daily diffuse coefficient ( K dd) with the monthly average daily clearness index ( K T) and monthly average daily sunshine fraction ( S/ S o) for the three big cities by population in Turkey (Istanbul, Ankara and Izmir). Although the global solar radiation on a horizontal surface and sunshine duration has been measured by the Turkish State Meteorological Service (STMS) over all country since 1964, the diffuse solar radiation has not been measured. The eight new models for estimating the monthly average daily diffuse solar radiation on a horizontal surface in three big cites are validated, and thus, the most accurate model is selected for guiding future projects. The new models are then compared with the 32 models available in the literature in terms of the widely used statistical indicators, namely; the relative percentage error ( E), coefficient of determination ( R 2), the mean percentage error (MPE), the mean absolute percentage error (MAPE), the sum of the squares of relative errors (SSRE), the relative standard error (RSE), the mean bias error (MBE), the root mean square error (RMSE), and the t-statistic ( t-stat) method combining the last two errors. It may be concluded that the new models predict the values of cloudness index ( K d) and diffuse coefficient ( K dd) as a function of clearness index ( K T) and sunshine fraction ( S/ S o) for three big cities in Turkey better than other available models, while all the models tested appear to be location independent models for diffuse radiation predictions, at least for three big cities in Turkey. It is also expected that the models reviewed and developed will be beneficial to everyone involved or interested in the design and study of solar energy.