Direct Irradiation Research Articles

Application of the Photo‐Fries Reaction as a Key Step in the Preparation of Valuable Aryloxyethyl Selenocyanates

AbstractA multistep sequence leads to the preparation of a series of aryl selenocyanate WC‐9 homologues, which are interesting compounds with potential biological activity against Trypanosoma cruzi, the etiological agent of Chagas disease. The synthetic sequence involves the application of the photo‐Fries rearrangement reaction as a key step for the preparation of a series of 2‐hydroxyphenones bearing alkyl chains of different length and a phenyl group. These intermediates are obtained upon direct irradiation (254 nm) of 4‐phenoxyphenyl esters in different solvents at room temperature. Then, reaction of the 2‐hydroxyphenones with 2‐(2‐bromoethoxy)tetrahydro‐2H‐pyran followed by acid deprotection, tosylation (or mesylation) reaction and, finally, nucleophilic attack with potassium selenocyanate provides the target aryloxyethyl selenocyanate homologues in good yields.

Revealing the Mechanisms of Smoke during Electron Beam–Powder Bed Fusion by High-Speed Synchrotron Radiography

Electron beam–powder bed fusion (PBF-EB) is an additive manufacturing process that utilizes an electron beam as the heat source to enable material fusion. However, the use of a charge-carrying heat source can sometimes result in sudden powder explosions, usually referred to as “Smoke”, which can lead to process instability or termination. This experimental study investigated the initiation and propagation of Smoke using in situ high-speed synchrotron radiography. The results reveal two key mechanisms for Smoke evolution. In the first step, the beam–powder bed interaction creates electrically isolated particles in the atmosphere. Subsequently, these isolated particles get charged either by direct irradiation by the beam or indirectly by back-scattered electrons. These particles are accelerated by electric repulsion, and new particles in the atmosphere are produced when they impinge on the powder bed. This is the onset of the avalanche process known as Smoke. Based on this understanding, the dependence of Smoke on process parameters such as beam returning time, beam diameter, etc., can be rationalized.

Sustainable coatings for green solar photovoltaic cells: performance and environmental impact of recyclable biomass digestate polymers

The underutilization of digestate-derived polymers presents a pressing environmental concern as these valuable materials, derived from anaerobic digestion processes, remain largely unused, contributing to pollution and environmental degradation when left unutilized. This study explores the recovery and utilization of biodegradable polymers from biomass anaerobic digestate to enhance the performance of solar photovoltaic (PV) cells while promoting environmental sustainability. The anaerobic digestion process generates organic residues rich in biodegradable materials, often considered waste. However, this research investigates the potential of repurposing these materials by recovering and transforming them into high-quality coatings or encapsulants for PV cells. The recovered biodegradable polymers not only improve the efficiency and lifespan of PV cells but also align with sustainability objectives by reducing the carbon footprint associated with PV cell production and mitigating environmental harm. The study involves a comprehensive experimental design, varying coating thickness, direct normal irradiance (DNI) (A), dry bulb temperature (DBT) (B), and relative humidity (C) levels to analyze how different types of recovered biodegradable polymers interact with diverse environmental conditions. Optimization showed that better result was achieved at A = 8 W/m2, B = 40 °C and C = 70% for both the coated material studied. Comparative study showed that for enhanced cell efficiency and cost effectiveness, EcoPolyBlend coated material is more suited however for improving durability and reducing environmental impact NanoBioCelluSynth coated material is preferable choice. Results show that these materials offer promising improvements in PV cell performance and significantly lower environmental impact, providing a sustainable solution for renewable energy production. This research contributes to advancing both the utilization of biomass waste and the development of eco-friendly PV cell technologies, with implications for a more sustainable and greener energy future. This study underscores the pivotal role of exploring anaerobic digestate-derived polymers in advancing the sustainability and performance of solar photovoltaic cells, addressing critical environmental and energy challenges of our time.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 7 Given name: [Ashok] Last name [Kumar Yadav]. Also, kindly confirm the details in the metadata are correct.correct

New Decomposition Models for Hourly Direct Normal Irradiance Estimations for Southern Africa

This research develops and validates new decomposition models for hourly direct Normal Irradiance (DNI) estimations for Southern African data. Localised models were developed using data collected from the Southern African Universities Radiometric Network (SAURAN). Clustered areas within Southern Africa were identified, and the developed cluster decomposition models highlighted the potential advantages of grouping data based on shared geographical and climatic attributes. This clustering approach could enhance decomposition model performance, particularly when local data are limited or when data are available from multiple nearby stations. Further, a regional Southern African decomposition model, which encompasses a wide spectrum of climatic regions and geographic locations, exhibited notable improvements over the baseline models despite occasional overestimation or underestimation. The results demonstrated improved DNI estimation accuracy compared to the baseline models across all testing and validation datasets. These outcomes suggest that utilising a localised model can significantly enhance DNI estimations for Southern Africa and potentially for developing similar models in diverse geographic regions worldwide. The overall metrics affirm the substantial advancement achieved with the regional model as an accurate decomposition model representing Southern Africa. Two stations were used as a validation study, as an application example where no localised model was available, and the cluster and regional models both outperformed the comparative decomposition models. This study focused on validating the model for hourly DNI in Southern Africa within a range of Kt-intervals from 0.175 to 0.875, and the range could be expanded and validated for future studies. Implementing accurate decomposition models in developing countries can accelerate the adoption of renewable energy sources, diminishing reliance on coal and fossil fuels.

Formation of secondary aerosol by 222 nm Far-UVC irradiation on SO2

222 nm UV indoor disinfection using KrCl* excimer lamps has been gaining popularity due to claims of minimal ocular and skin damage from direct irradiation. However, the secondary aerosol formation under irradiation of KrCl* excimer lamps, which could be an inhalation hazard, is less explored. SO2, a well-known precursor of outdoor sulfate aerosol, is also ubiquitous in indoor environments in urban cities in northern China where coal is used for domestic heating and cooking. In this work, we studied secondary aerosol formation by 222 nm irradiation on SO2, using a Go: PAM flow reactor, a scanning mobility particle sizer (SMPS), and a time-of-flight aerosol chemical composition monitor (ToF-ACSM). The formation of sulfate nanoparticles was found much more effective at 222 nm than at 254 nm and under fluorescent lamp (FL) irradiation at the same UV doses and RH, likely due to different SO2 oxidation mechanisms. We have also found that NH3 and cooking volatile organic compounds (CVOC), as other indoor-relevant gases, promoted the formation of secondary aerosols by 222 nm radiation on SO2. Overall, 222 nm disinfection can generate secondary pollutants in indoor environments. Caution should be taken during its indoor applications, especially in areas with high SO2 concentrations such as coal-fueled households.

Synergistic CdS@CeO2 nanocomposites: Harnessing Z-scheme electron transfer for enhanced photocatalytic CO2 conversion and aqueous Cr(VI) reduction

The present work describes the fabrication of CdS-D@CeO2 Z-scheme heterojunctions as highly stable, efficient and recyclable photocatalysts for the visible light assisted reduction of CO2 to methanol as well as Cr(VI) to Cr(III). The bare CeO2 produced methanol with the rate of 111 μmolh−1g−1 that is much lower in comparison to CdS-D@CeO2 (501 μmolh−1g−1). In addition, the photocatalytic efficiency remained nearly same for three successive runs, indicating the higher stability and recycling ability of the heterojunctions CdS-D@CeO2. It was also observed that under direct sunlight irradiation, CdS-D@CeO2 efficiently reduced 100% Cr(VI) to Cr(III) in an aqueous medium in just 40 minutes. Moreover, >95% removal was maintained even after 5 catalytic cycles. Additionally, for mimicking real contaminated water samples, the influence of the foreign ions on Cr(VI) reduction was evaluated by using lake water sample and no adverse effects on the result were observed. Thus, this work showed a way to develop cost-effective, easily synthesized, durable and recyclable composite photocatalysts for CO2 utilization and proficient removal of waste water pollutants by employing the clean, green, and renewable solar energy.

A hybrid MCDM optimization for utilization of novel set of biosynthesized nanofluids on thermal performance for solar thermal collectors

Recent advancements in solar technology have spurred researchers to develop a precise and cost-effective method for monitoring solar radiation across diverse environmental conditions. This study focuses on optimizing performance parameters by replacing conventional nanofluids, produced through chemical and physical processes, with biosynthesized counterparts. Biosynthesized nanoparticles, derived from biological sources like microorganisms or plants, offer a promising avenue for enhancing solar panel efficiency. Through the construction and testing of an integrated photovoltaic thermal system, varying operating parameters have been explored to maximize photovoltaic efficiency. The highest overall efficiency, reaching approximately 62 %, was achieved with biosynthesized Graphene oxide nanofluid under higher Direct Normal Irradiance (DNI) levels, while the lowest was observed with Aluminum oxide nanofluid at lower DNI values. Employing Multi-Criteria Decision methods such as Analytical Hierarchical Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), nanofluids were ranked based on criteria including Exergy loss, Surface temperature, Overall efficiency, and Electrical efficiency, with weights of 49.58 %, 28.43 %, 13.45 %, and 8.54 % respectively. TOPSIS prioritized Graphene Oxide as the most favorable nanofluid, followed by Copper-Oxide and Cerium-Oxide, while Aluminum-Oxide received the lowest priority in optimizing PV panel performance.

Energy, Exergy, Economic, and environmental assessment of a trigeneration system for combined power, cooling, and water desalination system driven by solar energy

Energy efficiency, exergy efficiency & destruction, economic, environmental, and error analysis analyses have been presented for the proposed trigeneration system. The metrics have provided an inclusive understanding of the proposed system's ability to effectively convert energy inputs to useful outputs while minimizing losses and waste. The goal is to optimize the cycle performance and find the most efficient configuration for meeting the Middle East's energy, cooling, and freshwater needs using a sustainable, solar-driven trigeneration system. The study has observed a system's increased energy and exergy efficiencies by 60.34 to 63.27 % and 25.98 to 29.59 % for the direct normal Irradiance (DNI) values varied from 600 W/m2 to 1000 W/m2. The total energy saving accounted by the proposed configuration is 4,453,220 USD/year as compared to the commercial electricity which includes the net energy production, energy saving through ejector and absorption cooling, and cost of water purification (through Reverse Osmosis Plant) in one year. The payback period of the plant mentioned in this is evaluated by 6.66 years. The CO2 mitigation is evaluated by 22,185.24 MT/year and the other pollutants can also be mitigated like SO2 by 40.425 MT/year and NOx by 136.415 MT/year.

FORECASTING THE CONSUMPTION OF ELECTRICAL ENERGY ON THE CEB ELECTRICAL NETWORK IN LOME IN TOGO BY APPROACHES: SIMPLE LINEAR REGRESSION, RECURRENT NEURAL NETWORKS AND GENETIC ALGORITHMS

Forecasting the electrical power to be consumed requires planning production on several levels. In this work we used data from the Electric Community of Benin. Temperature, relative humidity, wind speed, normal direct irradiance, precipitation and diffuse radiation are the meteorological variables that made it possible to analyze the forecasts. The objective is to do learning with genetic algorithms, LSTM recurrent neural networks and simple linear regression after a characterization and a correlation study and then to submit the results to performance evaluation criteria. The results of the characterization made it possible to understand that certain variables are significant and influence the consumption of electrical energy. The study of the correlation gives 94% between direct normal irradiance and diffuse irradiance. Both give with the temperature 67% for one and 68% for the other. Regarding modeling, the results are bad with genetic algorithms if we take into account the correlation coefficient (R² = 28.84%), good with simple linear regression (R² = 69.08%) and very interesting for networks of recurrent neurons where we find: MAE = 0.11 MSE = 0.02 MAPE = 18.50% RMSE = 13.09% RRMSE = 18.25% and R² = 96.11%. Given these results, we deduce that short- and long-term memory recurrent neural networks (LSTM) are very well suited to predicting the electrical power consumed on the CEB electrical network.

Evaluation of a concentrated solar power plant under meteorological and climatological forcing

The performance of renewable energy-conversion systems depends strongly on the background climate and ambient meteorological conditions. However, common approaches to simulating such systems use aggregated climatic data as forcing. Given strong variability in the climate, along with possible future long-term changes, it is important to understand the trade-offs between high-resolution forcing data and representative data. Here we simulate a concentrated solar power plant driven by hourly-resolved reanalysis data using both air and water-based cooling systems. These simulations allow us to analyze plant performance under realistic meteorological variability at two locations. In addition, we simulate the plant under average climatic conditions, where all sub-seasonal and interannual variability has been removed. These simulations are cheaper to run, but do not represent extreme values in the forcing. Our analysis shows that variability in the direct normal irradiance (DNI) is a critical factor for simulating the plant realistically. We show that where (and when) DNI variability is low, average forcing provides a realistic picture of power output. Average forcing can also provide a good estimate of how the plant will perform on average. However, in cases where understanding how robustly the plant performs, it is critical to include realistic variability in the input data.

A CERES-based dataset of hourly DNI, DHI and global tilted irradiance (GTI) on equatorward tilted surfaces: Derivation and comparison with the ground-based BSRN data

The NASA CERES SYN1deg(Ed4.1) satellite-based products include hourly global horizontal irradiance (GHI), diffuse horizontal irradiance (DHI) and direct horizontal irradiance (DirHI) as well as hourly solar zenith angle (SZA), and the direct normal irradiance (DNI) can thus be calculated by dividing the DirHI by cos(SZA). The spatial resolution of the dataset is 1° latitude by 1° longitude, and the time span is from March 2000 to near present (October 2023 as of this writing). While the GHI of the dataset agrees well with the ground-based Baseline Surface Radiation Network (BSRN) data, the DHI and DirHI, and thus DNI, show appreciable biases against the BSRN data. Nevertheless, the uncertainty of the DHI and DNI, as exhibited in the standard deviations of their differences from the BSRN data, are smaller than results from the DirIndex model, a global-to-beam model, applied to the CERES data, albeit the model is one of the two best out of 140 models according to a study. This means that the original CERES hourly DNI, though biased, renders the spatiotemporal variability better than does the DirIndex model. For this reason, we decided to perform a bias correction on the hourly DHI and DNI. With the corrected hourly DHI and DNI, we are able to use the isotropic model to calculate the global tilted irradiance (GTI) and the global tracker irradiance (GTrI) on an hourly basis. The BSRN DHI and DNI, on the other hand, are available on near-instantaneous time scales (1-, 2-, 3, or 5-minute intervals) and thus are more conducive to the isotropic model, and for the first time, we use the BSRN data to validate the GTI and GTrI, as opposed to GHI and DHI that have previously been used for validation purpose. For comparison and validation, we also use the monthly-mean-based LJCR method, an independent method as used by RETScreen®, which requires monthly mean GHI and DHI, to directly calculate the monthly mean DNI, GTI and, to extend the model, GTrI, and the results agree well with the BSRN data, which proves the LJCR method itself and corroborates the corrected DHI and DNI as well as the derived GTI and GTrI. The monthly mean DHI and DNI from the Whitlock Method, also monthly-mean-based, are evaluated as well, and we find that minor modification can dramatically improve the method.

Photocatalytic degradation of biological contaminant (E. coli) in drinking water under direct natural sunlight irradiation using incorporation of green synthesized TiO2, Fe2O3 nanoparticles

AbstractGlobally, there is a severe problem of widespread water contamination. Adsorption and photocatalytic degradation are considered the most suitable methods for removing these water pollutants because of their simplicity, environmental friendliness, and capacity to generate high-quality water. By a completely green route, in this recent study, the fungus Aspergillus tubingensis was able to synthesize TiO2 and Fe2O3 NPs with an average diameter of 28.0 and 65 nm, respectively. The smallest NPs diameters were produced when the precursor salt concentrations were 10−3 M and 10−2 M for TiO2 and Fe2O3, respectively, at pH 3 and an incubation time of 72 h. The biosynthesized NPs were characterized using DLS, TEM, EDX, and VSM. They were then applied in the preparation of titanium-iron nanocomposites with different ratios (1:1, 1:2, and 2:1 (w/w)) and characterized by FTIR and XRD. In this study, Escherichia coli (E. coli) was photo-catalytically inactivated using a TiO2–Fe2O3 nanocomposite as a photocatalyst in the presence of direct sunlight. This material’s photocatalytic performance was evaluated in comparison to that of TiO2 and Fe2O3 NPs. After 60 min of exposure to direct sunlight, the cell death was estimated as 97.97%, 99.32%, 89.06%, 30.96%, and 25.14% in the presence of TF-1, TF-2, TF-3, TiO2, and Fe2O3, respectively. Under direct natural sunlight irradiation for 60 min, TiO2 and Fe2O3 nanoparticles alone have the least impact on E. coli, whereas TF-2 nanocomposite has a higher level of photocatalytic bacterial inactivation competency than TF-1 and TF-3 nanocomposites. No significant toxicity has been observed for TF-2-treated water samples in the toxicity assessment.

Synthesis and characterization of absorption-enhanced alumina solid particle materials for direct irradiation solar-thermal conversion

Based on solar-thermal application more than 1000 °C, a novel combination of sol-gel synthesis and stepwise calcination to prepare metal-ion doped alumina composite particles as heat transfer media were proposed, enhancing solar energy absorption and lowering preparation temperature. Investigations focused on the elemental distribution, phase composition, oxidation states, optical properties and thermal stability of the composites, which were doped with Cr, Co, Cu, Fe, and Mn metal ions in a 100:5 M ratio. The first-principles calculations elucidate the mechanism behind the impact of transition metal ions on alumina’s optical properties. Results show that introducing a small quantity of metal atoms results in new impurity energy levels and electron states around EF = 0. Specifically, the band gap energy of Cu- and Mn–Al2O3 decreases from 6.017 eV to 4.464 eV and 2.170 eV, respectively. Correspondingly, the absorptivity of these composite particles increases from around 20 %–76.6 %. And Tauc analysis indicates a decrease in the optical band gap of the particle materials from 5.37 eV to a range of 2.88–4.90 eV. Additionally, doping with Mn, Fe, and Cu accelerates the in-situ formation of α-Al2O3 crystals, with a phase transition temperature below 1000 °C and a crystallinity exceeding 93 %. Thermal stability tests show that the particles are highly stable, exhibiting a mere 0.49 % mass change between 30 °C and 1200 °C. The stepwise inert annealing strategy and metal-iron doping positively influence the optical performance of alumina, synthesizing long-term high-temperature stable alumina ceramic particles with absorptance enhancement and a low α-Al2O3 phase transition temperature. These attributes make a favorable choice for solar-thermal applications in direct irradiation.

Predicting global horizontal irradiance of north central region of India via machine learning regressor algorithms

The ability to predict global horizontal irradiance (GHI) in the wake of changing weather conditions is becoming increasingly important in driving economic growth in the renewable energy industry. The choice of relevant meteorological variables for predicting GHI is crucial because it influences the prediction accuracy due to various geographical conditions. To fulfill this aim, this proposed study identifies a subset of the relevant input variables for the prediction of GHI by applying two methods Feature Combination (FC) and Feature Selection (FS). The results reveal that the most significant input variables for predicting GHI are Solar Zenith Angle, Dew Point, Diffuse Horizontal irradiance, Direct Normal Irradiance, and Wind speed obtained by the FS method. The predictive performance of the selected features is evaluated by feeding them into six different types of Machine Learning (ML) regressor algorithms such as Multiple Linear regressor (MLR), Decision Tree (DT), Random Forest (RF), Gradient Boost (GB), Light Gradient Boost Machine (LGBM) and Extra Tree (ET). The performances of models are evaluated by using statistical measures such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). ET regressor gives the best prediction performance among all the six models. It is observed that the percentage reduction in error for the FS method as compared to the FC method in terms of the statistical indicators MAE, RMSE, and MAPE are 8.5%, 7.72%, and 25.84% respectively. It shows that the FS method gives better predictive performance than the FC method for the estimation of GHI.

Analysis of Volatile Aroma Components in Different Parts of Shiitake Mushroom (Lentinus edodes) Treated with Ultraviolet C Light-Emitting Diodes Based on Gas Chromatography-Ion Mobility Spectroscopy.

Further assessment of ultraviolet C light-emitting diode (UVC-LED) irradiation for influencing shiitake mushrooms' (Lentinus edodes) volatile and sensory properties is needed. In this study, a comparison of UVC-LED irradiation treatment on the flavor profiles in various parts of shiitake mushrooms was conducted using gas chromatography-ion mobility spectrometry (GC-IMS) and sensory analysis. Sixty-three volatile compounds were identified in shiitake mushrooms. The fresh shiitake mushrooms were characterized by the highest values of raw mushroom odors. After UVC-LED treatment, the content of C8 alcohols decreased, especially that of 1-octen-3-ol, while the content of aldehydes increased, especially the content of nonanal and decanal. The score of fatty and green odors was enhanced. For fresh samples, the mushroom odors decreased and the mushroom-like odors weakened more sharply when treated in ethanol suspension than when treated with direct irradiation. The fruit odors were enhanced using direct UVC-LED irradiation for fresh mushroom samples and the onion flavor decreased. As for shiitake mushroom powder in ethanol suspension treated with UVC-LED, the sweaty and almond odor scores decreased and the vitamin D2 content in mushroom caps and stems reached 668.79 μg/g (dw) and 399.45 μg/g (dw), respectively. The results obtained from this study demonstrate that UVC-LED treatment produced rich-flavored, quality mushroom products.

Single-phase Si-modified β-NiAl coating formed by irradiation of Al-Si plasma: Microstructure and oxidation behavior

A single-phase Si-modified β-NiAl coating was formed by direct irradiation of Al-Si plasma in vacuum, which is distinctively different from that of the conventional AlSi slurry-diffusion process that normally generates silicides precipitation. A large amount of nanocavities was observed in the plasma-irradiated coating, which might relate to the formation of the single phase. Oxidation of the two coatings at 1000 ℃ shows that the single-phase coating formed an intact and uniform alumina scale whereas the conventional one formed a void-containing and rumpling scale, indicating the uniquely beneficial effects by forming the single-phase Si-modified β-NiAl coating.

Facile synthesis of Au–TiO2/Bacterial cellulose aerogel nanocomposites with enhanced photocatalytic degradation of methylene blue under UV irradiation

The Au–TiO2/Bacterial cellulose aerogel nanocomposites (Au–TiO2/BC NCs) with remarkable photocatalytic properties and designed to solve agglomeration and reusability challenges in wastewater treatment have been successfully synthesized. The integration of gold nanoparticles (Au NPs) with TiO2, in conjunction with bacterial cellulose (BC) serving as a three-dimensional nanostructure, resulted in the formation of Au–TiO2/BC NCs through mechanical compositing, a confirmation obtained through various characterization techniques including XRD, SEM, SEM-EDS, TEM, TEM-EDS, BET, XPS, FT-IR, Spectrofluorometer, Raman, and UV–vis spectroscopy. The combination of Au–TiO2 nanoparticles with BC showcased homogeneous coverage over BC nanostructures, attributed to BC's unique properties, particularly its surface hydroxyl groups that prevent TiO2 agglomeration, along with uniform distribution of Au nanoparticles on TiO2 layers. Photocatalytic activity, assessed through methylene blue (MB) degradation under both UV and direct sunlight irradiation, demonstrated significantly improved efficiency with AuNPs, achieving approximately 90% degradation in the Au–TiO2/BC NCs under UV irradiation. The synthesized NCs exhibited stability with a slight performance reduction after cyclic tests, indicating potential for wastewater treatment applications under diverse light conditions. The integration of Au NPs onto TiO2 surfaces amplified light-induced reactions, addressing critical challenges in environmental remediation, particularly in the degradation of persistent environmental pollutants. This study offers valuable insights, positioning Au–TiO2/BC NCs as promising candidates for sustainable environmental remediation, emphasizing reusability, enhanced photocatalytic performance, and a straightforward synthesis process under both UV and direct sunlight irradiation.

Theoretical and run-test investigation on a 50 MW beam-down concentrating solar power plant in China

The beam-down concentrating solar power plant has the advantages of high concentrating ratio, low installation and maintenance requirements, and low pump consumption. To conduct further theoretical and run-test evaluations of the optical and thermal performance of the beam-down concentrating solar power system, the Yumen Xinneng 50 MW beam-down power plant is analyzed in this research. The variation of the heliostat field efficiency, hyperboloid mirror flux density, receiver flux density, and annual power generation under various operating parameters is discussed. The run-test results from June 12th indicated an average outlet temperature of molten salt reached 559°C between 13:07 and 13:56 under an average direct normal irradiation of 739.7 W/m2. The corresponding inlet temperature increased from 302.3°C to 307.6°C due to the rewarming effect caused by the increase in hot molten salt. Regarding the simulation results, the recommended design parameters for the vertex ratio of the hyperboloid mirror are between 0.775 and 0.8 to mitigate the contradiction between initial investment and optical efficiency. The maximum values of cosine efficiency, shading and blocking efficiency, shading efficiency of the hyperboloid mirror, and attenuation efficiency for the heliostat field are 85.56%, 99.96%, 99.35%, and 97.46% on June 1st for the maximum solar elevation angle of 70.72°. Consequently, the maximum values of the average hyperboloid mirror flux density and receiver flux density are 11.2 kW/m2 and 399.1 kW/m2 on June 1st. The maximum power generation for a 7-day period is 4674.22 MWe in June with a duration time of 100 hours. This paper can further expand research on the comprehensive performance of beam-down power plants.

Experimental Study of Cleaning Techniques for improving Performance of PV Modules

The present study concentrates effects of soiling on the performance of three monocrystalline Solar panels (a reference panel, a nano coated and a self-cleaned). The panels were installed at a tilt angle of 33° facing south on the roof of Mechanical Engineering Department, Mirpur University of Science and Technology Mirpur Azad Jammu and Kashmir (AJ&K), Pakistan for a period of five weeks. The performance parameters such as solar irradiance, dust density, power output and the cell temperature of the panels were measured on weekly basis. The outcomes of the investigation demonstrate that power output of the reference, nano-coated and self-cleaning solar panels was observed to be reduced to 12%, 10%, and 8% respectively. The rapid fluctuation in the direct normal irradiation (DNI) is noticed for the whole experimental duration. Moreover, the temperature of reference, nano-coated and self-cleaning solar panels are observed to be decreased 13%, 11% and 12% respectively. The dust density increases for the whole experimental period.

Typical solar extinction year at Plataforma Solar de Almería (Spain). Application to thermoelectric solar tower plants

The atmospheric extinction of solar radiation reflected by heliostats are recognized as an important factor of radiative losses in Concentrating Solar Power technologies in general and especially in thermoelectric solar tower plants. These types of plants are getting larger (≥100 MWe), and consequently the distances between the heliostats and the receiver very often exceed 1 km and radiative losses due to solar extinction on this path can represent a high percentage. The aerosols and water vapor along this route scatter and absorb solar radiation, preventing a percentage of it from reaching the solar receiver. For this reason, in the process of choosing a location for the design and construction of these plants, the radiative losses due to extinction in that place should be known in advance. Until now, Typical Meteorological Years have been available for the location chosen in the plant design stage, mainly considering Direct Normal Irradiance but not solar extinction. Unfortunately, ground-based measurement of solar extinction has never been properly considered because it was not known how to measure or estimate it adequately. The Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT, Spain) has developed a reliable solar extinction measurement system which has been recording accurate horizontal extinction values at Plataforma Solar de Almería (PSA) since 2017. Based on this unique in the world solar extinction database of more than five years, a Typical Solar Extinction Year has been obtained for the first time to rigorously validate extinction models that then allow knowing the extinction in any area of interest in the world for solar tower power plants and to choose the most convenient one. It has been found that the annual average extinction at PSA for the measurement distance (742 m) is 6 %, with a standard deviation of 2 % and a median of 6 %. This average annual extinction value corresponds to a horizontal extinction coefficient of (0.083 ± 0.029) km−1 and a Visual Range of 47 km (-12 km, +23 km). Frequent haze events have been observed at PSA mainly caused by Saharan dust, which can be considered one more symptom of the current increased desertification and climate change.