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22 pages, 5604 KiB  
Article
Solar Energy Forecasting Framework Using Prophet Based Machine Learning Model: An Opportunity to Explore Solar Energy Potential in Muscat Oman
by Mazhar Baloch, Mohamed Shaik Honnurvali, Adnan Kabbani, Touqeer Ahmed, Sohaib Tahir Chauhdary and Muhammad Salman Saeed
Energies 2025, 18(1), 205; https://doi.org/10.3390/en18010205 - 6 Jan 2025
Viewed by 469
Abstract
The unpredictable nature of renewable energy sources, such as wind and solar, makes them unreliable sources of energy for the power system. Nevertheless, with the advancement in the field of artificial intelligence (AI), one can predict the availability of solar and wind energy [...] Read more.
The unpredictable nature of renewable energy sources, such as wind and solar, makes them unreliable sources of energy for the power system. Nevertheless, with the advancement in the field of artificial intelligence (AI), one can predict the availability of solar and wind energy in the short, medium, and long term with fairly high accuracy. As such, this research work aims to develop a machine-learning-based framework for forecasting global horizontal irradiance (GHI) for Muscat, Oman. The proposed framework includes a data preprocessing stage, where the missing entries in the acquired data are imputed using the mean value imputation method. Afterward, data scaling is carried out to avoid the overfitting/underfitting of the model. Features such as the GHI cloudy sky index, the GHI clear sky index, global normal irradiance (GNI) for a cloudy sky, GNI for a clear sky, direct normal irradiance (DNI) for a cloudy sky, and DNI for a clear sky are extracted. After analyzing the correlation between the abovementioned features, model training and the testing procedure are initiated. In this research, different models, named Linear Regression (LR), Support Vector Machine (SVR), KNN Regressor, Decision Forest Regressor, XGBoost Regressor, Neural Network (NN), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Random Forest Regressor, Categorical Boosting (CatBoost), Deep Autoregressive (DeepAR), and Facebook Prophet, are trained and tested under both identical features and a training–testing ratio. The model evaluation metrics used in this study include the mean absolute error (MAE), the root mean squared error (RMSE), R2, and mean bias deviation (MBD). Based on the outcomes of this study, it is concluded that the Facebook Prophet model outperforms all of the other utilized conventional machine learning models, with MAE, RMSE, and R2 values of 9.876, 18.762, and 0.991 for the cloudy conditions and 11.613, 19.951 and 0.988 for the clean weather conditions, respectively. The mentioned error values are the lowest among all of the studied models, which makes Facebook Prophet the most accurate solar irradiance forecasting model for Muscat, Oman. Full article
(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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Figure 1
<p>Research flow chart for solar irradiance prediction.</p>
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<p>Hourly solar irradiance of selected site after removing missing value entries.</p>
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<p>Distribution of ghi_clear_sky values.</p>
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<p>Distribution of cloudy sky global horizontal irradiance values.</p>
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<p>Monthly variability of solar irradiance parameters: comparing cloudy and clear sky conditions.</p>
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<p>Correlation of solar irradiance features with hour.</p>
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<p>Correlation heatmap of solar irradiance parameters.</p>
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<p>Actual vs. predicted GHI_Clear_Sky using Prophet model.</p>
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<p>Actual vs. predicted GHI_Cloudy_Sky using Prophet model.</p>
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<p>Residual distribution comparison for GHI_Cloudy_Sky predictions: Prophet, Random Forest, and XGBoost models.</p>
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<p>Cumulative error plot for GHI_Cloudy_Sky predictions: Prophet, Random Forest, and XGBoost models.</p>
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<p>Cumulative error plot for GHI_Clear_Sky predictions: Prophet, Random Forest, and XGBoost models.</p>
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15 pages, 1722 KiB  
Article
A Machine Learning-Based Model for Preoperative Assessment and Malignancy Prediction in Patients with Atypia of Undetermined Significance Thyroid Nodules
by Gilseong Moon, Jae Hyun Park, Taesic Lee and Jong Ho Yoon
J. Clin. Med. 2024, 13(24), 7769; https://doi.org/10.3390/jcm13247769 - 19 Dec 2024
Viewed by 394
Abstract
Objectives: The aim of this study was to investigate the preoperative clinical and hematologic variables, including the neutrophil-to-lymphocyte ratio (NLR), that can be used to predict malignancy in patients with atypia of undetermined significance (AUS) thyroid nodules; we further aimed to develop a [...] Read more.
Objectives: The aim of this study was to investigate the preoperative clinical and hematologic variables, including the neutrophil-to-lymphocyte ratio (NLR), that can be used to predict malignancy in patients with atypia of undetermined significance (AUS) thyroid nodules; we further aimed to develop a machine learning-based prediction model. Methods: We enrolled 280 patients who underwent surgery for AUS nodules at the Wonju Severance Christian Hospital between 2018 and 2022. A logistic regression-based model was trained and tested using cross-validation, with the performance evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC). Results: Among the 280 patients, 116 (41.4%) were confirmed to have thyroid malignancies. Independent predictors of malignancy included age, tumor size, and the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) classification, particularly in patients under 55 years of age. The addition of NLR to these predictors significantly improved the malignancy prediction accuracy in this subgroup. Conclusions: Incorporating NLR into preoperative assessments provides a cost-effective, accessible tool for refining surgical decision making in younger patients with AUS nodules. Full article
(This article belongs to the Special Issue Endocrine Malignancies: Current Surgical Therapeutic Approaches)
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<p>Patient inclusion and exclusion criteria. AUS, atypia of undetermined significance; PTC, papillary thyroid carcinoma.</p>
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<p>Comparison of statistical significance across study subgroups for laboratory data. The degree of significance (<span class="html-italic">p</span>-values) of DNI, NLR, and PLR was evaluated using logistic regression analysis, including the binomial distribution of thyroid cancer and laboratory indices as dependent and independent variables, respectively. The logistic model was applied to the entire patient cohort, the older patient (≥55 years) group, and the younger (&lt;55 years) patient group, separately. The x-axis denotes the subgroup analysis according to different age groups. The y-axis indicates a negative log-transformed <span class="html-italic">p</span>-value evaluated using logistic regression. DNI, delta neutrophil index; NLR, neutrophil–lymphocyte ratio; PLR, platelet–lymphocyte ratio.</p>
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<p>Comparison of performance of four thyroid cancer prediction models (&lt;55 years old). Four classification models were established according to four different combinations of clinical biomarkers. The “base” model includes age, sex, primary tumor size, and K-TIRADS classification as the input variables. The “DNI” model integrates DNI alongside the variables in the base model. In the “NLR” model, NLR is included along with the base model variables. Similarly, the “PLR” model incorporates PLR together with the base model variables. An iteration of four-fold cross-validation was conducted 250 times on an original dataset obtained from the WSCH, yielding 1000 sample training datasets. Logistic regression was implemented to establish the classification model for the binomial distribution of thyroid malignancy status (thyroid cancer vs. benign nodule). The logistic model was iteratively run for the 1000 random training datasets, yielding 1000 performance values. The 1000 performance levels were obtained from the 1000 matched sampling testing datasets. A boxplot depicts the distribution of the 1000 performance measures. In Panel (<b>A</b>), different colors are used to visually distinguish between the four models (Base, DNI, NLR, PLR). These colors do not carry any specific quantitative meaning. In Panel (<b>B</b>), the colors in the matrix represent the magnitude of <span class="html-italic">p</span>-values, with deeper shades of red indicating smaller <span class="html-italic">p</span>-values. Gray cells represent <span class="html-italic">p</span>-values &gt;0.05, indicating no statistical significance. Exact <span class="html-italic">p</span>-values are displayed within each cell for clarity. K-TIRADS, Korean Thyroid Imaging Reporting and Data System; DNI, delta neutrophil index; NLR, neutrophil–lymphocyte ratio; PLR, platelet–lymphocyte ratio; WSCH, Wonju Severance Christian Hospital.</p>
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<p>Comparison of performance of four thyroid cancer prediction models (≥55 years old). Four classification models were devised utilizing different combinations of clinical biomarkers. The “base” model was constructed with age, sex, primary tumor size, and K-TIRADS classification as the input variables. The “DNI” model featured DNI in conjunction with the base model variables. The “NLR” model included NLR along with the base model variables, while the “PLR” model incorporated PLR together with the base model variables. An iterative process of four-fold cross-validation was conducted 250 times on an initial dataset sourced from the WSCH, resulting in the generation of 1000 training datasets through sampling. Logistic regression was employed to construct a classification model for the binomial distribution of thyroid malignancy status (thyroid cancer vs. benign nodule). The logistic model underwent iterative executions across the 1000 random training datasets, producing 1000 performance values. These performance levels were derived from 1000 corresponding testing datasets obtained through matched sampling. The distribution of the 1000 performance measures is visually represented in a boxplot. In Panel (<b>A</b>), different colors are used to visually distinguish between the four models (Base, DNI, NLR, PLR). These colors do not carry any specific quantitative meaning. In Panel (<b>B</b>), the colors in the matrix represent the magnitude of <span class="html-italic">p</span>-values, with deeper shades of red indicating smaller <span class="html-italic">p</span>-values. Gray cells represent <span class="html-italic">p</span>-values &gt;0.05, indicating no statistical significance. Exact <span class="html-italic">p</span>-values are displayed within each cell for clarity. K-TIRADS, Korean Thyroid Imaging Reporting and Data System; DNI, delta neutrophil index; NLR, neutrophil–lymphocyte ratio; PLR, platelet–lymphocyte ratio; WSCH, Wonju Severance Christian Hospital.</p>
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<p>Final malignancy prediction model. (<b>A</b>) Iteration of 4-fold cross-validation was applied 250 times to an original dataset obtained from the WSCH, yielding 1000 sample training datasets. Multivariate logistic regression was iteratively used for the 1000 random training sets to generate the binomial distribution of thyroid malignancy status (thyroid cancer vs. benign nodule), yielding 1000 beta coefficients per variable. (<b>B</b>) The equation for the thyroid cancer prediction model was established by computing the mean value of the 1000 parameters for each predictor. (<b>C</b>) AUROC was used to evaluate the classification performance for the dichotomous status of thyroid cancer condition. AUROC was measured using the 1000 testing datasets matched with the 1000 training sets obtained from the 250 × 4 CV. (<b>D</b>) The LR-based thyroid cancer index provides predictive values ranging from 0 to 1. Then, we selected the cut-off of the thyroid cancer index showing the maximum value of the F1 score. We employed 250 × 4 CV for the selection of the optimal predictive value of thyroid cancer, yielding 1000 cut-off values of the thyroid cancer index. The average of the 1000 cut-offs of the LR indices was identified as the optimal threshold for predicting thyroid cancer from AUS. K-TIRADS, Korean Thyroid Imaging Reporting and Data System; NLR, neutrophil–lymphocyte ratio; AUC, area under the curve; WSCH, Wonju Severance Christian Hospital; AUROC, area under the ROC curve; LR, logistic regression; CV, cross-validation; AUS, atypia of undetermined significance.</p>
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20 pages, 4395 KiB  
Article
Effect of Solar Irradiation Inter-Annual Variability on PV and CSP Power Plants Production Capacity: Portugal Case-Study
by Ailton M. Tavares, Ricardo Conceição, Francisco M. Lopes and Hugo G. Silva
Energies 2024, 17(21), 5490; https://doi.org/10.3390/en17215490 - 2 Nov 2024
Viewed by 756
Abstract
The sizing of solar energy power plants is usually made using typical meteorological years, which disregards the inter-annual variability of the solar resource. Nevertheless, such variability is crucial for the bankability of these projects because it impacts on the production goals set at [...] Read more.
The sizing of solar energy power plants is usually made using typical meteorological years, which disregards the inter-annual variability of the solar resource. Nevertheless, such variability is crucial for the bankability of these projects because it impacts on the production goals set at the time of the supply agreement. For that reason, this study aims to fill the gap in the existing literature and analyse the impact that solar resource variability has on solar power plant production as applied to the case of Portugal (southern Europe). To that end, 17 years (2003–2019) of meteorological data from a network of 90 ground stations hosted by the Portuguese Meteorological Service is examined. Annual capacity factor regarding photovoltaic (PV) and concentrating solar power (CSP) plants is computed using the System Advisor Model, used here for solar power performance simulations. In terms of results, while a long-term trend for increase in annual irradiation is found for Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI), 0.4148 and 3.2711 kWh/m2/year, respectively, consistent with a solar brightening period, no corresponding trend is found for PV or CSP production. The latter is attributed to the long-term upward trend of 0.0231 °C/year in annual average ambient temperature, which contributes to PV and CSP efficiency reduction. Spatial analysis of inter-annual relative variability for GHI and DNI shows a reduction in variability from the north to the south of the country, as well as for the respective power plant productions. Particularly, for PV, inter-annual variability ranges between 2.45% and 12.07% in Faro and Santarém, respectively, while higher values are generally found for CSP, 3.71% in Faro and 16.04% in São Pedro de Moel. These results are a contribution to future instalments of PV and CSP systems in southern Portugal, a region with very favourable conditions for solar energy harvesting, due to the combination of high production capacity and low inter-annual variability. Full article
(This article belongs to the Special Issue Renewable Energy System Technologies: 2nd Edition)
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<p>Temporal evolution of DNI (blue dots) and GHI (yellow dots) annual irradiation from 2003 to 2019 at four reference IPMA stations: Lisbon, Beja, Sines, and Faro. Dashed lines are the trends, inside plots show the data inset.</p>
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<p>Temporal evolution of DNI (blue dots) and GHI (yellow dots) annual irradiation from 2003 to 2019 at four reference IPMA stations: Lisbon, Beja, Sines, and Faro. Dashed lines are the trends, inside plots show the data inset.</p>
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<p>Temporal evolution of the capacity factor of CSP (blue dots) and PV (yellow dots) plants from 2003 to 2019 at four IPMA stations: Lisbon, Beja, Sines, and Faro. Dashed lines are the trends, inside plots shows the data inset.</p>
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<p>Temporal evolution of the capacity factor of CSP (blue dots) and PV (yellow dots) plants from 2003 to 2019 at four IPMA stations: Lisbon, Beja, Sines, and Faro. Dashed lines are the trends, inside plots shows the data inset.</p>
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<p>Boxplots with the temporal evolution at 90 IPMA stations over a 17-year period, from 2003 to 2019 of: (<b>a</b>) GHI irradiation, (<b>b</b>) DNI irradiation, (<b>c</b>) CF for PV plant, (<b>d</b>) CF for CSP plant, and (<b>e</b>) ambient temperature, Ta. The median is represented by a red line in each boxplot, the red crosses are outliers, and the black straight line shows the trend of the median.</p>
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<p>Boxplots with the temporal evolution at 90 IPMA stations over a 17-year period, from 2003 to 2019 of: (<b>a</b>) GHI irradiation, (<b>b</b>) DNI irradiation, (<b>c</b>) CF for PV plant, (<b>d</b>) CF for CSP plant, and (<b>e</b>) ambient temperature, Ta. The median is represented by a red line in each boxplot, the red crosses are outliers, and the black straight line shows the trend of the median.</p>
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<p>Boxplots with the temporal evolution at 90 IPMA stations over a 17-year period, from 2003 to 2019 of: (<b>a</b>) GHI irradiation, (<b>b</b>) DNI irradiation, (<b>c</b>) CF for PV plant, (<b>d</b>) CF for CSP plant, and (<b>e</b>) ambient temperature, Ta. The median is represented by a red line in each boxplot, the red crosses are outliers, and the black straight line shows the trend of the median.</p>
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<p>Correlation between annual irradiation and CF of the solar power plants. The CF<sub>PV</sub> is correlated with the GHI irradiation (blue dots) and the CF<sub>CSP</sub> is correlated with the DNI irradiation (yellow dots). The trends are shown by solid lines.</p>
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<p>Comparison of variability in annual irradiation: (<b>a</b>) GHI, (<b>b</b>) DNI.</p>
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<p>Comparison of capacity factor variability for mainland Portugal: (<b>a</b>) PV, (<b>b</b>) CSP.</p>
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27 pages, 8454 KiB  
Article
Comparative Techno-Economic Analysis of Parabolic Trough and Linear Fresnel Collectors with Evacuated and Non-Evacuated Receiver Tubes in Different Geographical Regions
by Mehdi Shokrnia, Mattia Cagnoli, Roberto Grena, Antonio D’Angelo, Michela Lanchi and Roberto Zanino
Processes 2024, 12(11), 2376; https://doi.org/10.3390/pr12112376 - 29 Oct 2024
Viewed by 1138
Abstract
In the context of Concentrated Solar Power (CSP) technology, this paper presents a comparison between the Parabolic Trough Collector (PTC) and the Linear Fresnel Collector (LFC), considering both evacuated and non-evacuated receiver tubes. The comparison was carried out in terms of the Levelized [...] Read more.
In the context of Concentrated Solar Power (CSP) technology, this paper presents a comparison between the Parabolic Trough Collector (PTC) and the Linear Fresnel Collector (LFC), considering both evacuated and non-evacuated receiver tubes. The comparison was carried out in terms of the Levelized Cost of Electricity (LCOE) considering a reference year and four locations in the world, characterized by different levels of direct normal irradiation (DNI) from 2183 kWh/m2/year to 3409 kWh/m2/year. The LCOE depends on economic parameters and on the net energy generated by a plant on an annual basis. The latter was determined by a steady-state 1D model that solved the energy balance along the receiver axis. This model required computing the incident solar power and heat losses. While the solar power was calculated by an optical ray-tracing model, heat losses were computed by a lumped-parameter model developed along the radial direction of the tube. Since the LFC adopted a secondary concentrator, no conventional correlation was applicable for the convective heat transfer from the glass cover to the environment. Therefore, a 2D steady-state CFD model was also developed to investigate this phenomenon. The results showed that the PTC could generate a higher net annual energy compared to the LFC due to a better optical performance ensured by the parabolic solar collector. Nevertheless, the difference between the PTC and the LFC was lower in the non-evacuated tubes because of lower heat losses from the LFC receiver tube. The economic analysis revealed that the PTC with the evacuated tube also achieved the lowest LCOE, since the higher cost with respect to both the LFC system and the non-evacuated PTC was compensated by the higher net energy yield. However, the non-evacuated LFC demonstrated a slightly lower LCOE compared to the non-evacuated PTC since the lower capital cost of the non-evacuated LFC outweighed its lower net annual energy yield. Finally, a sensitivity analysis was conducted to assess the impact on the LCOE of the annual optical efficiency and of the economic parameters. This study introduces key technical parameters in LFC technology requiring improvement to achieve the level of productivity of the PTC from a techno-economic viewpoint, and consequently, to fill the gap between the two technologies. Full article
(This article belongs to the Special Issue Heat and Mass Transfer Phenomena in Energy Systems)
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<p>Scheme of the cross section of the receiver unit. This configuration was the same for both of the PTC and LFC technologies, except for the secondary concentrator, which only exists in the case of the LFC technology.</p>
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<p>Emissivity as a function of the temperature for the two coatings implemented for the evacuated and non-evacuated tubes.</p>
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<p>Global view of the methodology for the techno-economic analysis highlighting the optical analysis (<a href="#sec4-processes-12-02376" class="html-sec">Section 4</a>), thermal analysis (<a href="#sec5-processes-12-02376" class="html-sec">Section 5</a>) and economic analysis (<a href="#sec6-processes-12-02376" class="html-sec">Section 6</a>).</p>
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<p>The solar system simulated in Tonatiuh considering the Sun in the east and the Sun’s altitude being equal to 40°, with a reduced number of simulated photons for visualization purposes.</p>
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<p>Incidence angle modifier components for the LFC and PTC technologies.</p>
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<p>The scheme of the 1D model discretized along the receiver axis with the boundary conditions applied.</p>
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<p>Heat fluxes along the radial direction considered in the lumped-parameter model, from Ref. [<a href="#B16-processes-12-02376" class="html-bibr">16</a>].</p>
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<p>Experimental setup for obtaining the heat loss from the absorber tube demonstrating the schematic locations of the thermocouples.</p>
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<p>Comparison between the experimental data and the computed results by the lumped-parameter model for the heat loss.</p>
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<p>Comparison between the CFD model and the two correlations for natural convection around a cylinder with error bars (green dotted line for Churchill–Chu and black dotted line for Morgan) and computed Nusselt numbers for natural convection around the Fresnel collector.</p>
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<p>Comparison between the CFD model and the Zhukauskas correlation for the circular cylinder in cross flow with the error bar and computed Nusselt numbers for forced convection around the Fresnel collector.</p>
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<p>Meteorological data including the DNI, wind speed and ambient temperature for the reference location (Karas, Namibia) and the reference day (13 November).</p>
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<p>Thermal performance of the different configurations considering the reference day (13 November) in terms of the (<b>a</b>) receiver outlet temperature, (<b>b</b>) mass flow rate, (<b>c</b>) mean heat loss and (<b>d</b>) thermal efficiency.</p>
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<p>Reference meteorological data including DNI, wind speed and ambient temperature for the different locations under investigation.</p>
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<p>Net monthly energy yields for the reference locations during the year considering the different configurations.</p>
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<p>The net annual energy yields for the reference locations considering different configurations.</p>
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<p>The LCOEs for the reference locations considering different configurations.</p>
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<p>LCOE variation using different HTFs operating at low, medium and high temperatures for various configurations.</p>
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<p>Sensitivity analysis representing the impact of the capital cost on equalizing the LCOE for the LFC and the PTC.</p>
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11 pages, 1296 KiB  
Article
Dynamics of Neutrophil-to-Lymphocyte Ratio (NLR), Lymphocyte-to-Monocyte Ratio (LMR), and Platelet-to-Lymphocyte Ratio (PLR) in Patients with Deep Neck Infection
by Jeong-Mi Kim, Huu Hoang and Jeong-Seok Choi
J. Clin. Med. 2024, 13(20), 6105; https://doi.org/10.3390/jcm13206105 - 13 Oct 2024
Viewed by 850
Abstract
Background: Inflammatory biomarkers, including the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR), have been utilized as prognostic factors in various diseases. This study aims to evaluate changes in the NLR, PLR, and LMR in patients diagnosed with a deep neck [...] Read more.
Background: Inflammatory biomarkers, including the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR), have been utilized as prognostic factors in various diseases. This study aims to evaluate changes in the NLR, PLR, and LMR in patients diagnosed with a deep neck infections (DNI) to identify useful prognostic markers. Methods: This single-center, retrospective cohort study utilized data from the electronic medical records of patients admitted to the ENT department of a tertiary university hospital between January 2000 and August 2024. Patients diagnosed with a DNI during the study period were enrolled. Preoperative and postoperative inflammatory markers were measured in all patients, and NLR, LMR, and PLR values were calculated and analyzed. Results: The post-treatment NLR was significantly lower than the pre-treatment NLR. Similarly, the post-treatment LMR was significantly higher and the post-treatment PLR was significantly lower compared to pre-treatment values. Patients admitted to the ICU had higher inflammatory markers than those in general wards. Additionally, patients with elevated inflammatory markers had longer hospital stays. Inflammatory markers were also higher in older patients and those who underwent surgical treatment. Conclusions: Significant changes in the NLR, LMR, and PLR in patients diagnosed with a DNI can serve as useful prognostic markers. These findings suggest that monitoring these markers may help to assess and improve the inflammatory status of patients, highlighting their potential role in guiding treatment. Full article
(This article belongs to the Section Immunology)
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<p>A comparison of NLR, PLR, and LMR values according to (<b>a</b>) gender, (<b>b</b>) age and (<b>c</b>) diabetes (DB), and pre-and post-treatment periods.</p>
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<p>A comparison of NLR, PLR and LMR values according to (<b>a</b>) surgery status, (<b>b</b>) ICU admission, and (<b>c</b>) length of hospital stay, and pre-and post-treatment periods.</p>
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20 pages, 6458 KiB  
Article
Multi-Utility Solar Thermal Systems: Harnessing Parabolic Trough Concentrator Using SAM Software for Diverse Industrial and Residential Applications
by Soufyane Naaim, Badr Ouhammou, Mohammed Aggour, Brahim Daouchi, El Mahdi El Mers and Miriam Mihi
Energies 2024, 17(15), 3685; https://doi.org/10.3390/en17153685 - 26 Jul 2024
Viewed by 971
Abstract
This study investigates the technical and economic feasibility of a 20 MW parabolic trough solar thermal power plant (PTSTPP) located in Kenitra, Morocco, characterized by an annual average direct normal irradiance (DNI) exceeding 5.3 [...] Read more.
This study investigates the technical and economic feasibility of a 20 MW parabolic trough solar thermal power plant (PTSTPP) located in Kenitra, Morocco, characterized by an annual average direct normal irradiance (DNI) exceeding 5.3 kWh/m2/day. Utilizing System Advisor Model (SAM) 2012.12.02 software, the plant is designed with Therminol VP-1 as the heat transfer fluid (HTF) throughout the solar field, coupled with a dry cooling system to reduce water consumption. The proposed thermal energy storage (TES) system employs HITEC solar salt as the storage medium, allowing for six full load hours of thermal energy storage. With a solar multiple (SM) of 2, the simulated plant demonstrates the capability to generate an annual electricity output of 50.51 GWh. The economic viability of the plant is further assessed, revealing a Levelized Cost of Electricity (LCOE) of 0.1717 $/kWh and a capacity factor (CF) of 32%. This comprehensive analysis provides valuable insights into the performance, economic viability, and sustainability of a parabolic trough solar power plant in the specific climatic conditions of Kenitra, Morocco. Full article
(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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<p>Kenitra weather measurements acquired from the Meteonorm Database.</p>
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<p>The components and operation of a parabolic trough solar thermal system (SAM 2012.12.02).</p>
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<p>Solar field arrangement [<a href="#B26-energies-17-03685" class="html-bibr">26</a>].</p>
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<p>Annual beam normal irradiation heat map (<math display="inline"><semantics> <mrow> <mi mathvariant="normal">W</mi> <mo>/</mo> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> </semantics></math>).</p>
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<p>Monthly energy generation of the parabolic trough concentrator (PTC).</p>
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<p>Hourly data of thermal power input, total electric power to grid, gross electrical power output to power cycle, and field thermal power incident.</p>
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<p>Dry bulb temperature and solar incidence angle.</p>
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<p>Power and thermal losses in a parabolic trough system in July.</p>
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<p>Power and thermal losses in a parabolic trough system in January.</p>
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<p>The effect of solar multiple on annual energy production and LCOE for Kenitra.</p>
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12 pages, 770 KiB  
Article
Sequential Impact of Diabetes Mellitus on Deep Neck Infections: Comparison of the Clinical Characteristics of Patients with and without Diabetes Mellitus
by Ting-I Liao, Chia-Ying Ho, Shy-Chyi Chin, Yu-Chien Wang, Kai-Chieh Chan and Shih-Lung Chen
Healthcare 2024, 12(14), 1383; https://doi.org/10.3390/healthcare12141383 - 10 Jul 2024
Cited by 1 | Viewed by 1184
Abstract
Background: Deep neck infections (DNIs) can compromise the airway and are associated with high morbidity and mortality rates. Diabetes mellitus (DM) is a metabolic disorder characterized by chronic hyperglycemia that is associated with several comorbidities. We compared the clinical characteristics of DNI patients [...] Read more.
Background: Deep neck infections (DNIs) can compromise the airway and are associated with high morbidity and mortality rates. Diabetes mellitus (DM) is a metabolic disorder characterized by chronic hyperglycemia that is associated with several comorbidities. We compared the clinical characteristics of DNI patients with and without DM. Methods: This study recorded the relevant clinical variables of 383 patients with DNIs between November 2016 and September 2022; of those patients, 147 (38.38%) had DM. The clinical factors between DNI patients with and without DM were assessed. Results: Patients with DM were older (p < 0.001), had higher white blood cell counts (p = 0.029) and C-reactive protein levels (CRP, p < 0.001), had a greater number of deep neck spaces (p = 0.002) compared to patients without DM, and had longer hospital stays (p < 0.001). Klebsiella pneumoniae was cultured more frequently from patients with DM than those without DM (p = 0.002). A higher CRP level (OR = 1.0094, 95% CI: 1.0047–1.0142, p < 0.001) was a significant independent risk factor for DM patients with prolonged hospitalization. The lengths of hospital stays in patients with poorly controlled DM were longer than those with well-controlled DM (p = 0.027). Conclusions: DNI disease severity and outcomes were worse in patients with DM than those without DM. Antibiotics effective against Klebsiella pneumoniae should be used for DNI patients with DM. DNI patients with DM and high CRP levels had more prolonged hospitalizations. Appropriate blood glucose control is essential for DNI patients with DM. Full article
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<p>(<b>A</b>) Axial view and (<b>B</b>) coronal view of a DNI patient with DM. (<b>A</b>) indicates that the infection spread from the parapharyngeal space to the parotid space and the submandibular space, encompassing infection across multiple deep neck spaces. (<b>B</b>) depicts infection in the coronal plane. Arrowhead: epiglottis; Double dotted arrow: involved spaces of DNI; P: parapharyngeal space; Pa: parotid space; S: submandibular space. (300 × 300 dpi).</p>
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<p>The coronal view showed extensive infection in the retropharyngeal spaces with poor DM control. Arrow: compromised and deviated airway; Asterisk; multiloculated deep neck abscess; Double dotted arrow: involved spaces of DNI. (300 × 300 dpi).</p>
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14 pages, 4416 KiB  
Article
Measuring DNI with a New Radiometer Based on an Optical Fiber and Photodiode
by Alejandro Carballar, Roberto Rodríguez-Garrido, Manuel Jerez, Jonathan Vera and Joaquín Granado
Sensors 2024, 24(11), 3674; https://doi.org/10.3390/s24113674 - 6 Jun 2024
Viewed by 2715
Abstract
A new cost-effective radiometer has been designed, built, and tested to measure direct normal solar irradiance (DNI). The proposed instrument for solar irradiance measurement is based on an optical fiber as the light beam collector, a semiconductor photodiode to measure the optical power, [...] Read more.
A new cost-effective radiometer has been designed, built, and tested to measure direct normal solar irradiance (DNI). The proposed instrument for solar irradiance measurement is based on an optical fiber as the light beam collector, a semiconductor photodiode to measure the optical power, and a calibration algorithm to convert the optical power into solar irradiance. The proposed radiometer offers the advantage of separating the measurement point, where the optical fiber collects the solar irradiation, from the place where the optical power is measured. A calibration factor is mandatory because the semiconductor photodiode is only spectrally responsive to a limited part of the spectral irradiance. Experimental tests have been conducted under different conditions to evaluate the performance of the proposed device. The measurements confirm that the proposed instrument performs similarly to the expensive high-accuracy pyrheliometer used as a reference. Full article
(This article belongs to the Special Issue Recent Advance of Optical Measurement Based on Sensors)
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<p>Block diagram for the proposed radiometer. The system is composed of an optical fiber (OF), a semiconductor photodiode (SC-PD), an optical power meter (OPM), and a module for data acquisition (DAQ).</p>
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<p>Scheme of the multimode optical fiber’s end tip exposed to the solar radiation.</p>
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<p>Calibration process: <span class="html-italic">P</span> (λ<sub>i</sub>) is the estimated optical power measurement, <span class="html-italic">A</span><sub>eff</sub> is the fiber-optic effective area, <span class="html-italic">Φ<sub>raw</sub></span> (λ<sub>i</sub>) is the raw solar irradiance, <span class="html-italic">CF</span> (<span class="html-italic">λ</span><sub>i</sub>) is the correction factor, and <span class="html-italic">B<sub>n</sub></span> is the resulting DNI measurement.</p>
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<p>Solar spectral irradiance [<a href="#B28-sensors-24-03674" class="html-bibr">28</a>] (blue line), and responsivity response for the silicon photodiode Thorlabs S140C (red line) as a function of wavelength.</p>
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<p>Experimental assembly for the proof of concept of the proposed instrument: (<b>a</b>) optical fiber and commercial pyrheliometer installed in a sun tracker; and (<b>b</b>) silicon photodiodes and an optical power meter, plus a computer for data acquisition.</p>
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<p>DNI measurements obtained by the proposed radiometer (red line) and the commercial pyrheliometer (black line) for two sunny days, with: (<b>a</b>) Thorlabs FG050LGA multimode fiber; and (<b>b</b>) Thorlabs FP200URT multimode fiber. The green line represents the raw solar irradiance before applying the correction factor.</p>
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<p>DNI measurements obtained by the commercial pyrheliometer (black line) and the proposed radiometer (red line) with a Thorlabs FG105LVA multimode fiber: (<b>a</b>) DNI measurements for a sunny day; absolute (<b>b</b>) and relative (<b>c</b>) deviations between the DNI measurements provided by the proposed instrument and the commercial pyrheliometer. Green line in (<b>a</b>) represents the raw solar irradiance before applying the correction factor.</p>
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<p>Variation of the correction factor depending on the selected reference wavelength in the OPM.</p>
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<p>DNI measurements obtained by the commercial pyrheliometer (black line) and the proposed radiometer (red line) on a cloudy day, with: (<b>a</b>) Thorlabs FG050LGA multimode fiber; and (<b>b</b>) Thorlabs FG105LCA multimode fiber. The green line represents the raw solar irradiance before applying the correction factor.</p>
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<p>DNI measurements obtained by the commercial pyrheliometer (black line) and the proposed radiometer (red line) with the Thorlabs FG105LVA multimode fiber: (<b>a</b>) DNI measurements for a cloudy day; absolute (<b>b</b>) and relative (<b>c</b>) deviations between the DNI measurements provided by the proposed instrument and the commercial pyrheliometer.</p>
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<p>DNI measurements obtained by the commercial pyrheliometer (black line) and the proposed radiometer (red line) for a rainy interval with the Thorlabs FG105LCA multimode fiber.</p>
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19 pages, 6940 KiB  
Article
Evaluation of Two Satellite Surface Solar Radiation Products in the Urban Region in Beijing, China
by Lin Xu and Yuna Mao
Remote Sens. 2024, 16(11), 2030; https://doi.org/10.3390/rs16112030 - 5 Jun 2024
Viewed by 841
Abstract
Surface solar radiation, as a primary energy source, plays a pivotal role in governing land–atmosphere interactions, thereby influencing radiative, hydrological, and land surface dynamics. Ground-based instrumentation and satellite-based observations represent two fundamental methodologies for acquiring solar radiation information. While ground-based measurements are often [...] Read more.
Surface solar radiation, as a primary energy source, plays a pivotal role in governing land–atmosphere interactions, thereby influencing radiative, hydrological, and land surface dynamics. Ground-based instrumentation and satellite-based observations represent two fundamental methodologies for acquiring solar radiation information. While ground-based measurements are often limited in availability, high-temporal- and spatial-resolution, gridded satellite-retrieved solar radiation products have been extensively utilized in solar radiation-related studies, despite their inherent uncertainties in accuracy. In this study, we conducted an evaluation of the accuracy of two high-resolution satellite products, namely Himawari-8 (H8) and Moderate Resolution Imaging Spectroradiometer (MODIS), utilizing data from a newly established solar radiation observation system at the Beijing Normal University (BNU) station in Beijing since 2017. The newly acquired measurements facilitated the generation of a firsthand solar radiation dataset comprising three components: Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), and Diffuse Horizontal Irradiance (DHI). Rigorous quality control procedures were applied to the raw minute-level observation data, including tests for missing data, the determination of possible physical limits, the identification of solar tracker malfunctions, and comparison tests (GHI should be equivalent to the sum of DHI and the vertical component of the DNI). Subsequently, accurate minute-level solar radiation observations were obtained spanning from 1 January 2020 to 22 March 2022. The evaluation of H8 and MODIS satellite products against ground-based GHI observations revealed strong correlations with R-squared (R2) values of 0.89 and 0.81, respectively. However, both satellite products exhibited a tendency to overestimate solar radiation, with H8 overestimating by approximately 21.05% and MODIS products by 7.11%. Additionally, solar zenith angles emerged as a factor influencing the accuracy of satellite products. This dataset serves as crucial support for investigations of surface solar radiation variation mechanisms, future energy utilization prospects, environmental conservation efforts, and related studies in urban areas such as Beijing. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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Graphical abstract

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<p>(<b>a</b>) Location of the newly established BNU site; (<b>b</b>) the instruments.</p>
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<p>Scatter plots of minute-level GHI and the sum of DNI multiplied by the cosine of solar zenith angle (<span class="html-italic">μ</span>) plus DHI (DNI·<span class="html-italic">μ</span> + DHI) data, in both raw and quality-controlled (QC) forms, from 2020 to 2022.</p>
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<p>Hourly observations included in each date with available data from 2020 to 2022.</p>
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<p>Time series of daily solar radiation components from 1 January 2020, to 22 March 2022. (<b>a</b>) GHI, (<b>b</b>) DNI, (<b>c</b>) DHI.</p>
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<p>Scatter plot of hourly H8, MCD18, and observational GHI Data. The red solid line is the 1:1 line.</p>
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<p>Scatter plot of daily H8, MCD18, and observational GHI data. The red solid line is the 1:1 line.</p>
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<p>Hourly GHI distribution of observational data, H8 and MCD18 for each month.</p>
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<p>Boxplots of the difference between observed GHI and MCD18, H8 and under different SZA intervals. In each boxplot, the bottom of the lower tail represents the minimum value and the top of the upper tail represents the maximum. The lower line of the box represents the 25th percentile, the upper box represents the 75th percentile, and the middle line in the box represents the median.</p>
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<p>Boxplots of the relative difference between observed GHI and MCD18, H8 and under different SZA intervals. The relative difference (%) was calculated by dividing the absolute difference in <a href="#remotesensing-16-02030-f009" class="html-fig">Figure 9</a> by the mean observed GHI for each SZA category. In each boxplot, the bottom of the lower tail represents the minimum value and the top of the upper tail represents the maximum. The lower line of the box represents the 25th percentile, the upper box represents the 75th percentile, and the middle line in the box represents the median.</p>
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29 pages, 4449 KiB  
Article
Techno-Economic Assessment of Molten Salt-Based Concentrated Solar Power: Case Study of Linear Fresnel Reflector with a Fossil Fuel Backup under Saudi Arabia’s Climate Conditions
by Ahmed Aljudaya, Stavros Michailos, Derek B. Ingham, Kevin J. Hughes, Lin Ma and Mohamed Pourkashanian
Energies 2024, 17(11), 2719; https://doi.org/10.3390/en17112719 - 3 Jun 2024
Viewed by 1287
Abstract
Concentrated solar power (CSP) has gained traction for generating electricity at high capacity and meeting base-load energy demands in the energy mix market in a cost-effective manner. The linear Fresnel reflector (LFR) is valued for its cost-effectiveness, reduced capital and operational expenses, and [...] Read more.
Concentrated solar power (CSP) has gained traction for generating electricity at high capacity and meeting base-load energy demands in the energy mix market in a cost-effective manner. The linear Fresnel reflector (LFR) is valued for its cost-effectiveness, reduced capital and operational expenses, and limited land impact compared to alternatives such as the parabolic trough collector (PTC). To this end, the aim of this study is to optimize the operational parameters, such as the solar multiple (SM), thermal energy storage (TES), and fossil fuel (FF) backup system, in LFR power plants using molten salt as a heat transfer fluid (HTF). A 50 MW LFR power plant in Duba, Saudi Arabia, serves as a case study, with a Direct Normal Irradiance (DNI) above 2500 kWh/m2. About 600 SM-TES configurations are analyzed with the aim of minimizing the levelized cost of electricity (LCOE). The analysis shows that a solar-only plant can achieve a low LCOE of 11.92 ¢/kWh with a capacity factor (CF) up to 36%, generating around 131 GWh/y. By utilizing a TES system, the SM of 3.5 and a 15 h duration TES provides the optimum integration by increasing the annual energy generation (AEG) to 337 GWh, lowering the LCOE to 9.24 ¢/kWh, and boosting the CF to 86%. The techno-economic optimization reveals the superiority of the LFR with substantial TES over solar-only systems, exhibiting a 300% increase in annual energy output and a 20% reduction in LCOE. Additionally, employing the FF backup system at 64% of the turbine’s rated capacity boosts AEG by 17%, accompanied by a 5% LCOE reduction. However, this enhancement comes with a trade-off, involving burning a substantial amount of natural gas (503,429 MMBtu), leading to greenhouse gas emissions totaling 14,185 tonnes CO₂ eq. This comprehensive analysis is a first-of-a-kind study and provides insights into the optimal designs of LFR power plants and addresses thermal, economic, and environmental considerations of utilizing molten salt with a large TES system as well as employing natural gas backup. The outcomes of the research address a wide audience including academics, operators, and policy makers. Full article
(This article belongs to the Collection Renewable Energy and Energy Storage Systems)
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<p>The direct normal irradiance for different regions in Saudi Arabia (Reproduced with permission from [<a href="#B43-energies-17-02719" class="html-bibr">43</a>]).</p>
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<p>A schematic diagram for a linear Fresnel reflector power plant with thermal energy storage and a fossil fuel auxiliary boiler.</p>
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<p>Comparison of the monthly energy production of solar-only LFR power plant and 18 h of thermal energy storage.</p>
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<p>Variation of the levelized cost of electricity as a function of thermal energy storage.</p>
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<p>Variation of the levelized cost of electricity in terms of the solar multiple.</p>
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<p>3D variation of the levelized cost of electricity with different ranges of the thermal energy storage duration and the solar multiple.</p>
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<p>Variation of the annual energy generation in terms of the duration of the thermal energy storage.</p>
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<p>Variation of the annual energy generation in terms of the size of the solar multiple.</p>
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<p>3D variation of the annual energy generation with different ranges of the thermal energy storage duration and the solar multiple.</p>
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<p>Variation of the capacity factor in terms of the duration of the thermal energy storage.</p>
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<p>Variation of the capacity factor in terms of the duration of the solar multiple.</p>
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<p>3D variation of the capacity factor with different ranges of the thermal energy storage duration and the solar multiple.</p>
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<p>LFR’s solar field and energy storage thermal performance profile for a typical day in Duba.</p>
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<p>Effects of different ranges of the fossil fuel fraction on the annual techno-economic performance.</p>
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<p>Annual thermal performance profile of the solar field, thermal energy storage, and the fossil fuel backup system.</p>
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38 pages, 4207 KiB  
Article
New Decomposition Models for Hourly Direct Normal Irradiance Estimations for Southern Africa
by Francisca Muriel Daniel-Durandt and Arnold Johan Rix
Solar 2024, 4(2), 269-306; https://doi.org/10.3390/solar4020013 - 14 May 2024
Cited by 1 | Viewed by 1074
Abstract
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 [...] Read more.
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. Full article
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<p>The irradiance relationships between GHI, DNI, DHI, and <math display="inline"><semantics> <msub> <mi>θ</mi> <mi>Z</mi> </msub> </semantics></math>.</p>
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<p>Validation sites of discussed decomposition models.</p>
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<p>Decomposition model development.</p>
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<p>Clusters within the Southern African context. (<b>a</b>) SAURAN [<a href="#B41-solar-04-00013" class="html-bibr">41</a>]; (<b>b</b>) GHI across South Africa [<a href="#B50-solar-04-00013" class="html-bibr">50</a>].</p>
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<p>Distribution of data within clusters.</p>
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<p>Cluster 1 coefficients in <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>K</mi> <mi>n</mi> </msub> <mo>=</mo> <mi>a</mi> <mo>+</mo> <mi>b</mi> <mo>·</mo> <mi>A</mi> <mi>M</mi> <mo>+</mo> <mi>c</mi> <mo>·</mo> <mi>A</mi> <msup> <mi>M</mi> <mn>2</mn> </msup> </mrow> </semantics></math>. (<b>a</b>) <span class="html-italic">a</span> coefficient; (<b>b</b>) <span class="html-italic">b</span> coefficient; (<b>c</b>) <span class="html-italic">c</span> coefficient.</p>
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<p>Cluster 2 coefficients in <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>K</mi> <mi>n</mi> </msub> <mo>=</mo> <mi>a</mi> <mo>+</mo> <mi>b</mi> <mo>·</mo> <mi>A</mi> <mi>M</mi> <mo>+</mo> <mi>c</mi> <mo>·</mo> <mi>A</mi> <msup> <mi>M</mi> <mn>2</mn> </msup> </mrow> </semantics></math>. (<b>a</b>) <span class="html-italic">a</span> coefficient; (<b>b</b>) <span class="html-italic">b</span> coefficient; (<b>c</b>) <span class="html-italic">c</span> coefficient.</p>
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<p>Cluster 2 coefficients in <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>K</mi> <mi>n</mi> </msub> <mo>=</mo> <mi>a</mi> <mo>+</mo> <mi>b</mi> <mo>·</mo> <mi>A</mi> <mi>M</mi> <mo>+</mo> <mi>c</mi> <mo>·</mo> <mi>A</mi> <msup> <mi>M</mi> <mn>2</mn> </msup> </mrow> </semantics></math>. (<b>a</b>) <span class="html-italic">a</span> coefficient; (<b>b</b>) <span class="html-italic">b</span> coefficient; (<b>c</b>) <span class="html-italic">c</span> coefficient.</p>
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<p>Cluster 3 coefficients in <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>K</mi> <mi>n</mi> </msub> <mo>=</mo> <mi>a</mi> <mo>+</mo> <mi>b</mi> <mo>·</mo> <mi>A</mi> <mi>M</mi> <mo>+</mo> <mi>c</mi> <mo>·</mo> <mi>A</mi> <msup> <mi>M</mi> <mn>2</mn> </msup> </mrow> </semantics></math>. (<b>a</b>) <span class="html-italic">a</span> coefficient; (<b>b</b>) <span class="html-italic">b</span> coefficient; (<b>c</b>) <span class="html-italic">c</span> coefficient.</p>
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<p>Cluster 3 coefficients in <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>K</mi> <mi>n</mi> </msub> <mo>=</mo> <mi>a</mi> <mo>+</mo> <mi>b</mi> <mo>·</mo> <mi>A</mi> <mi>M</mi> <mo>+</mo> <mi>c</mi> <mo>·</mo> <mi>A</mi> <msup> <mi>M</mi> <mn>2</mn> </msup> </mrow> </semantics></math>. (<b>a</b>) <span class="html-italic">a</span> coefficient; (<b>b</b>) <span class="html-italic">b</span> coefficient; (<b>c</b>) <span class="html-italic">c</span> coefficient.</p>
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<p>Cluster 4 coefficients in <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>K</mi> <mi>n</mi> </msub> <mo>=</mo> <mi>a</mi> <mo>+</mo> <mi>b</mi> <mo>·</mo> <mi>A</mi> <mi>M</mi> <mo>+</mo> <mi>c</mi> <mo>·</mo> <mi>A</mi> <msup> <mi>M</mi> <mn>2</mn> </msup> </mrow> </semantics></math>. (<b>a</b>) <span class="html-italic">a</span> coefficient; (<b>b</b>) <span class="html-italic">b</span> coefficient; (<b>c</b>) <span class="html-italic">c</span> coefficient.</p>
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<p>Regional model coefficients in <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>K</mi> <mi>n</mi> </msub> <mo>=</mo> <mi>a</mi> <mo>+</mo> <mi>b</mi> <mo>·</mo> <mi>A</mi> <mi>M</mi> <mo>+</mo> <mi>c</mi> <mo>·</mo> <mi>A</mi> <msup> <mi>M</mi> <mn>2</mn> </msup> </mrow> </semantics></math>. (<b>a</b>) <span class="html-italic">a</span> coefficient; (<b>b</b>) <span class="html-italic">b</span> coefficient; (<b>c</b>) <span class="html-italic">c</span> coefficient.</p>
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<p>Regional model coefficients in <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>K</mi> <mi>n</mi> </msub> <mo>=</mo> <mi>a</mi> <mo>+</mo> <mi>b</mi> <mo>·</mo> <mi>A</mi> <mi>M</mi> <mo>+</mo> <mi>c</mi> <mo>·</mo> <mi>A</mi> <msup> <mi>M</mi> <mn>2</mn> </msup> </mrow> </semantics></math>. (<b>a</b>) <span class="html-italic">a</span> coefficient; (<b>b</b>) <span class="html-italic">b</span> coefficient; (<b>c</b>) <span class="html-italic">c</span> coefficient.</p>
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<p>Hourly test results of decomposition model development for CSIR.</p>
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<p>Hourly test results of decomposition model development for CUT.</p>
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<p>Hourly test results of decomposition model development for FRH.</p>
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<p>Hourly test results of decomposition model development for GRT.</p>
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<p>Hourly test results of decomposition model development for HLO.</p>
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<p>Hourly test results of decomposition model development for ILA.</p>
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<p>Hourly test results of decomposition model development for KZH.</p>
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<p>Hourly test results of decomposition model development for KZW.</p>
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<p>Hourly test results of decomposition model development for MIN.</p>
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<p>Hourly test results of decomposition model development for NMU.</p>
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<p>Hourly test results of decomposition model development for NUST.</p>
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<p>Hourly test results of decomposition model development for RVD.</p>
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<p>Hourly test results of decomposition model development for SUN.</p>
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<p>Hourly test results of decomposition model development for UBG.</p>
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<p>Hourly test results of decomposition model development for UFS.</p>
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<p>Hourly test results of decomposition model development for UNV.</p>
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<p>Hourly test results of decomposition model development for UNZ.</p>
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<p>Hourly test results of decomposition model development for UPR.</p>
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<p>Hourly test results of decomposition model development for VAN.</p>
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10 pages, 686 KiB  
Article
Associations between Peritonsillar Abscess and Deep Neck Infection in Chronic Periodontitis Patients: Two Nested Case—Control Studies Using a National Health Screening Cohort
by So Young Kim, Il Hwan Park, Chun Sung Byun, Hyo Geun Choi, Mi Jung Kwon, Ji Hee Kim, Joo-Hee Kim and Chang Wan Kim
J. Clin. Med. 2024, 13(8), 2166; https://doi.org/10.3390/jcm13082166 - 9 Apr 2024
Cited by 1 | Viewed by 1074
Abstract
Background/Introduction: Odontogenic infection is one of the main etiologies of deep neck infection (DNI). However, the relationship between chronic periodontitis (CP) and the incidence of DNI has not been examined. This study aimed to evaluate the incidence of DNI and peritonsillar abscess (PTA) [...] Read more.
Background/Introduction: Odontogenic infection is one of the main etiologies of deep neck infection (DNI). However, the relationship between chronic periodontitis (CP) and the incidence of DNI has not been examined. This study aimed to evaluate the incidence of DNI and peritonsillar abscess (PTA) after CP. Methods: The Korean National Health Insurance Service-National Sample Cohort 2002–2019 was used. In Study I, 4585 PTA patients were matched with 19,340 control I participants. A previous history of CP for 1 year was collected, and the odds ratios (ORs) of CP for PTA were analyzed using conditional logistic regression. In Study II, 46,293 DNI patients and 185,172 control II participants were matched. A previous history of CP for 1 year was collected, and conditional logistic regression was conducted for the ORs of CP for DNI. Secondary analyses were conducted in demographic, socioeconomic, and comorbidity subgroups. Results: In Study I, a history of CP was not related to the incidence of PTA (adjusted OR = 1.28, 95% confidence interval [CI] = 0.91–1.81). In Study II, the incidence of DNI was greater in participants with a history of CP (adjusted OR = 1.55, 95% CI = 1.41–1.71). The relationship between CP history and DNI was greater in groups with young, male, low-income, and rural residents. Conclusions: A prior history of CP was associated with a high incidence of DNI in the general population of Korea. Patients with CP need to be managed for the potential risk of DNI. Full article
(This article belongs to the Special Issue Clinical Challenges and Advances in Periodontology and Oral Surgery)
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<p>(<b>a</b>) A schematic illustration of the participant selection process that was used in the present study. Of a total of 1,137,861 participants, 4585 participants with peritonsillar abscess were matched with 18,340 control participants for age, sex, income, and region of residence. (<b>b</b>) A schematic illustration of the participant selection process that was used in the present study. Of a total of 1137,861 participants, 46,293 participants with deep neck infection were matched with 185,172 control participants for age, sex, income, and region of residence.</p>
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<p>(<b>a</b>) A schematic illustration of the participant selection process that was used in the present study. Of a total of 1,137,861 participants, 4585 participants with peritonsillar abscess were matched with 18,340 control participants for age, sex, income, and region of residence. (<b>b</b>) A schematic illustration of the participant selection process that was used in the present study. Of a total of 1137,861 participants, 46,293 participants with deep neck infection were matched with 185,172 control participants for age, sex, income, and region of residence.</p>
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37 pages, 3349 KiB  
Review
Potential Drug–Nutrient Interactions of 45 Vitamins, Minerals, Trace Elements, and Associated Dietary Compounds with Acetylsalicylic Acid and Warfarin—A Review of the Literature
by David Renaud, Alexander Höller and Miriam Michel
Nutrients 2024, 16(7), 950; https://doi.org/10.3390/nu16070950 - 26 Mar 2024
Cited by 2 | Viewed by 5776
Abstract
In cardiology, acetylsalicylic acid (ASA) and warfarin are among the most commonly used prophylactic therapies against thromboembolic events. Drug–drug interactions are generally well-known. Less known are the drug–nutrient interactions (DNIs), impeding drug absorption and altering micronutritional status. ASA and warfarin might influence the [...] Read more.
In cardiology, acetylsalicylic acid (ASA) and warfarin are among the most commonly used prophylactic therapies against thromboembolic events. Drug–drug interactions are generally well-known. Less known are the drug–nutrient interactions (DNIs), impeding drug absorption and altering micronutritional status. ASA and warfarin might influence the micronutritional status of patients through different mechanisms such as binding or modification of binding properties of ligands, absorption, transport, cellular use or concentration, or excretion. Our article reviews the drug–nutrient interactions that alter micronutritional status. Some of these mechanisms could be investigated with the aim to potentiate the drug effects. DNIs are seen occasionally in ASA and warfarin and could be managed through simple strategies such as risk stratification of DNIs on an individual patient basis; micronutritional status assessment as part of the medical history; extensive use of the drug–interaction probability scale to reference little-known interactions, and application of a personal, predictive, and preventive medical model using omics. Full article
(This article belongs to the Section Micronutrients and Human Health)
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<p>Bidirectional relationship of DNIs. Reproduced from Karadima et al. [<a href="#B9-nutrients-16-00950" class="html-bibr">9</a>].</p>
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<p>Selected DNIs with ASA. Created with biorender.com, accessed on 20 March 2024.</p>
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<p>Selected DNIs with warfarin. Created with biorender.com, accessed on 20 March 2024.</p>
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<p>Drug Interaction Probability Scale. taken from Horn et al. [<a href="#B392-nutrients-16-00950" class="html-bibr">392</a>].</p>
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19 pages, 4442 KiB  
Article
Analysis of the Solar Pyrolysis of a Walnut Shell: Insights into the Thermal Behavior of Biomaterials
by Arturo Aspiazu-Méndez, Nidia Aracely Cisneros-Cárdenas, Carlos Pérez-Rábago, Aurora M. Pat-Espadas, Fabio Manzini-Poli and Claudio A. Estrada
Energies 2024, 17(6), 1435; https://doi.org/10.3390/en17061435 - 16 Mar 2024
Cited by 2 | Viewed by 1052
Abstract
The state of Sonora, Mexico, stands as one of the leading producers of pecan nuts in the country, which are commercialized without shells, leaving behind this unused residue. Additionally, this region has abundant solar resources, as shown by its high levels of direct [...] Read more.
The state of Sonora, Mexico, stands as one of the leading producers of pecan nuts in the country, which are commercialized without shells, leaving behind this unused residue. Additionally, this region has abundant solar resources, as shown by its high levels of direct normal irradiance (DNI). This study contributes to research efforts aimed at achieving a synergy between concentrated solar energy technology and biomass pyrolysis processes, with the idea of using the advantages of organic waste to reduce greenhouse gas emissions and avoiding the combustion of conventional pyrolysis through the concentration of solar thermal energy. The objective of this study is to pioneer a new experimental analysis methodology in research on solar pyrolysis reactors. The two main features of this new methodology are, firstly, the comparison of temperature profiles during the heating of inert and reactive materials and, secondly, the analysis of heating rates. This facilitated a better interpretation of the observed phenomenon. The methodology encompasses two different thermal experiments: (A) the pyrolysis of pecan shells and (B) the heating–cooling process of the biochar produced in experiment (A). Additionally, an experiment involving the heating of volcanic stone is presented, which reveals the temperature profiles of an inert material and serves as a comparative reference with experiment (B). In this experimental study, 50 g of pecan shells were subjected to pyrolysis within a cylindrical stainless-steel reactor with a volume of 156 cm3, heated by concentrated radiation from a solar simulator. Three different heat fluxes were applied (234, 482, and 725 W), resulting in maximum reaction temperatures of 382, 498, and 674 °C, respectively. Pyrolysis gas analyses (H2, CO, CO2, and CH4) and characterization of the obtained biochar were conducted. The analysis of heating rates, both for biochar heating and biomass pyrolysis, facilitated the identification, differentiation, and interpretation of processes such as moisture evaporation, tar production endpoint, cellulosic material pyrolysis, and lignin degradation. This analysis proved to be a valuable tool as it revealed heating and cooling patterns that were not previously identified. The potential implications of this tool would be associated with improvements in the design and operation protocols of solar reactors. Full article
(This article belongs to the Special Issue Advances in Bioenergy and Waste-to-Energy Technologies)
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<p>Scheme of the experimental setup for biomass pyrolysis in a stainless-steel reactor with a solar simulator.</p>
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<p>TGA of walnut shell. Heating rate: 10 °C/min.</p>
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<p>Temperature profiles during the heating of inert material (volcanic stone) and biochar: (<b>a</b>,<b>c</b>). Heating rate profiles during the heating of inert material (volcanic stone) and biochar: (<b>b</b>,<b>d</b>). Comparison between profiles of volcanic stone and biochar: (<b>e</b>,<b>f</b>). Labels: V = volcanic stone; Cr = biochar; power: L = low 234 W; M = medium 482 W; and H = high 725 W.</p>
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<p>Temperature and heating rate analysis. Labels: T5 = internal thermocouple; CA = case A. CB = case B; L = low (234 W). M = medium (482 W). H = high (725 W); V = valley; Conv = convergence; S = segment. Dashed lines = onset and termination periods of heating. (<b>a</b>,<b>c</b>,<b>e</b>) correspond to the temperature column. (<b>b</b>,<b>d</b>,<b>f</b>) correspond to the heating rate column.</p>
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<p>Temperature and heating rate analysis of gas outlet (T6) and tar trap (T7); CA = case A; CB = case B; L = low (234 W); M = medium (482 W); H = high (725 W). CV = coincidence with valley; CM = coincidence moisture drying; dashed lines = onset and termination periods of heating. (<b>a</b>,<b>c</b>,<b>e</b>) correspond to the temperature column. (<b>b</b>,<b>d</b>,<b>f</b>) correspond to the heating rate column.</p>
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<p>Syngas composition CA = case A; CB = case B; L = low (234 W); M = medium (482 W); H = high (725 W); CV = coincidence with valley; dashed lines = onset and termination periods of heating (<b>a</b>,<b>c</b>,<b>e</b>) correspond to the walnut shell pyrolysis column. (<b>b</b>,<b>d</b>,<b>f</b>) correspond to the biochar heating column.</p>
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<p>TGA compared to biochar percentage produced. Y = product yield; L = low (234 W); M = medium (482 W); H = high (725 W); heating rate = 10 °C/min.</p>
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19 pages, 6308 KiB  
Article
Suitability Index for the Placement of Solar Plants Based on Inequality Measurements and on Satellite Images
by Estrella Trincado and Jose María Vindel
Remote Sens. 2024, 16(6), 1039; https://doi.org/10.3390/rs16061039 - 15 Mar 2024
Viewed by 888
Abstract
The selection of a certain location for the placement of a solar facility depends on the solar resource availability, which is generally assessed though exceedance probabilities. However, the choice of the specific exceedance probability is arbitrary and the assessment will be different depending [...] Read more.
The selection of a certain location for the placement of a solar facility depends on the solar resource availability, which is generally assessed though exceedance probabilities. However, the choice of the specific exceedance probability is arbitrary and the assessment will be different depending on the choice taken. Furthermore, exceedance probabilities do not reflect seasonal variability, which affects radiation availability. Therefore, in this work we present a new index, the suitability index based on Theil (SIT), which allows us to assess with a single value the degree of suitability of a site for installing a solar plant. Obtained from the Theil index, it considers the availability of the resource and its seasonal variability, based as it is on the proportion of the given radiation in each month. As we will see, the new index is clearly more sensitive to the amount of radiation expressed in terms of the 50th percentile than to the variability, as given by the interquartile range. This is a quality to be pondered since scarcity of radiation will always be a greater disadvantage for a solar installation than high variability. The results obtained in the study, grounded in the application of satellite images, show that the index adequately reflects the radiation characteristics in the study area. The territory is broken into areas associated with such characteristics through a cluster analysis, so that geographical and economic elements can be considered when choosing the final location for a solar installation. Furthermore, the new index may include the effects of energy storage during the months in which a certain demand is exceeded. Full article
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<p>Physical map of the Iberian Peninsula. Source: Nations Online Project. <a href="https://www.nationsonline.org/oneworld/map/Iberian-Peninsula-topographic-map.htm" target="_blank">https://www.nationsonline.org/oneworld/map/Iberian-Peninsula-topographic-map.htm</a> (accessed on 2 February 2024).</p>
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<p>Radiation percentiles associated with the exceedance probabilities of: (<b>a</b>) 90%; (<b>b</b>) 50%.</p>
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<p>Interquartile range.</p>
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<p>Average versus variance of the DNI (in W/m<sup>2</sup>) throughout the study territory.</p>
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<p>Map of the suitability index based on Theil (SIT).</p>
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<p>Optimal number of clusters: (<b>a</b>) Davis–Bouldin method; (<b>b</b>) silhouette method.</p>
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<p>Division of the territory into clusters with similar behavior to the SIT.</p>
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<p>Monthly evolution of the DNI in the five sites.</p>
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<p>SIT for different demands after storage: (<b>a</b>) 300 W/m<sup>2</sup>; (<b>b</b>) 350 W/m<sup>2</sup>; (<b>c</b>) 400 W/m<sup>2</sup>; (<b>d</b>) 450 W/m<sup>2</sup>.</p>
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