Dew Point Research Articles

Study of red vine phenotypic plasticity across central-southern Italy sites: an integrated analysis of the transcriptome and weather indices through WGCNA

The grapevine (Vitis spp., family Vitaceae) is characterized by marked phenotypic plasticity. Its ability to withstand specific environmental conditions depends on the activation of highly coordinated responses resulting from interactions among genotypes (G) and environmental factors (E). In this study, the transcriptomes of commercially ripe berries of the Cabernet Sauvignon and Aglianico genotypes grown in open fields at three different sites in central-southern Italy (Campania, Molise and Sicily) were analyzed with RNA sequencing. These transcriptomic data were integrated with a comprehensive set of weather course indices through weighted gene co-expression network analysis (WGCNA). A total of 11,887 differentially expressed genes (DEGs) were retrieved, most of which were associated with the Aglianico genotype. The plants from the Sicilian site presented the greatest number of DEGs for both genotypes. Most of the weather course data (daily maximum air temperature, relative humidity, air pressure, dew point, and hours of sun radiation) were significantly correlated with the “lightcyan1” module, confirming WGCNA as a powerful method for identifying genes of high biological interest. Within this module, the gene encoding the ACA10 cation transporter was highly expressed in plants of both genotypes from Campania, where the lowest anthocyanin content was recorded. The transcriptome was also correlated with quality traits, such as total soluble solids and polyphenol content. This approach could lead to the identification of a transcriptomic profile that may specifically identify a genotype and its growing site and to the discovery of hub genes that might function as markers of wine quality.

A novel structural equation modelling-based framework for identifying hydrometeorological multi-factor interaction

Study regionThe Hanjiang River Basin, China. Study focusIn this study, we proposed a new integrated framework based on structural equation models to identify hydrometeorological multi-factor interaction relationship. The spatiotemporal distribution and interaction relationship of hydrological, meteorological, and vegetation indicators in the Hanjiang River Basin were analyzed based on the national long-series monitoring data. New hydrological insights for the regionThe findings reveal that: (1) The rainfall is the maximum in the lower reaches (968.87 mm), and the runoff depth fluctuated the most in the lower reaches (1181.70 mm to 2934.88 mm). (2) Dew point temperature is an important influencing factor of rainfall in the upper and middle reaches, and relative humidity is an important influencing factor of rainfall in the lower reaches. High vegetation cover is an important influencing factor for runoff depth. (3) Rainfall and runoff depth are significantly negatively affected by meteorological characteristics, with effect values ranging from −0.24 to −0.68. However, runoff depth is positively affected by rainfall, relative humidity, vegetation characteristics, and canopy interception, with effect values ranging from 0.02 to 0.58. The size and mode of influence have significant regional differences.

Activated Carbon Fiber Felt Composites for the Direct Air Capture of Carbon Dioxide.

Negative emissions technologies to mitigate climate change require innovative solutions for the direct air capture (DAC) of CO2 from the atmosphere. K2CO3 readily reacts with CO2 to form KHCO3; however, bulk K2CO3 suffers from very slow sorption kinetics. By incorporating K2CO3 into activated carbon (AC) fiber felts, the sorption kinetics were significantly improved by increasing the surface area of K2CO3 in contact with air. The AC-K2CO3 fiber composite felts are flexible, cheap, easy to manufacture, chemically stable, and show excellent DAC capacity and (de)sorption rates, with stable performance up to ten cycles. Cyclic testing was demonstrated with 4 h sorption and 0.5 h desorption intervals. The best composite felts collected an average of 478 μmol of CO2 per gram of composite during 4 h of exposure to ambient air (19 % relative humidity) that had a CO2 concentration of 400-450 ppm after regeneration at 125 °C in an air furnace. An increase in the dew point temperature from 0 to 12 °C decreased sorption performance of the composite felts by 40 %.

Failure Analysis of Burst Tube Caused by Corrosion Thinning of the Tube Wall of Boiler Economizer

Abstract A boiler economizer is in continuous operation during several burst pipes. Burst tubes are located near the elbow below the weld, and the weld on both sides of the tube wall is thinning seriously. There is a flue gas formation vortex in the furnace chamber; the outer wall of the tubes is thicker ash, and fins and tubes are corroded to varying degrees. This study employed meticulous macroscopic observation, precise chemical composition analysis, detailed metallography, advanced scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS) to determine the causes of the coal economizer tube burst and tube wall thinning, ensuring the accuracy of the findings. The chemical composition, microstructure, and grain size of the base material met the requirements of the relevant standards. The heat-affected zone and weld seam show the Widmannstatten structure, a superheated tissue caused by excessive heat input during welding. The outer wall of the pipe corrosion is more serious, resulting in serious wall thinning, and ultimately, the pipe bursts, unable to withstand the pressure inside the pipe. It was eventually determined that the cause of the pipe burst was that the operating temperature of the economizer was close to the dew point, and the flue gas contained sulfur dioxide, sulfur trioxide, and hydrogen chloride that met water vapor at that temperature to form acid. Acid on the outer wall of the pipe was caused by flue gas dew point corrosion, seriously causing the pipe wall to thin. When the pipe wall is thinned to a certain extent due to corrosion, it cannot withstand the pressure inside the pipe, and rupture will occur.

Monitoring of Greenhouse Gas Emission Drivers in Atlantic Canadian Potato Production: A Robust Explainable Intelligent Glass-Box

In this research, a novel explainable multi-level ensemble learning framework has been developed to accurately monitor the greenhouse gas (GHG) emission drivers of the Maritime potato crop system i.e., Carbon dioxide (CO2), nitrous oxide (N2O), and water vapour (H2O). For this purpose, alongside the GHG emission drivers, the hydro-meteorological and soil properties information was collected from three Canadian sites, two in Prince Edward Island (PEI) and one in New Brunswick. This advanced framework includes a transparent multi-level pre-processing module and a Runge-Kutta optimizer (RUN), integrated with an eXplainable gradient-boosted decision Tree (GBDT) machine learning (ML) technique. The preprocessing scheme meticulously selects the most effective input combinations from the hydro-meteorological and soil properties datasets using hybridization of Boruta-GBDT for feature selection, Best Subset Least Absolute Shrinkage and Selection Operator (BSLR), and Weighted Aggregated Sum Product Assessment (WASPAS). The optimal combinations were then analyzed using the GBDT-RUN and compared against two algorithms: LightGBM coupled with RUN optimizer (LightGBM-RUN) and classical GBDT. The explainability of the primary model was enhanced using SHapley Additive exPlanations (SHAP). Model validation employed various metrics, such as the correlation coefficient (R), squared deviation (SquD), and a range of sophisticated statistical graphics. Results demonstrated that the GBDT-RUN model exhibited superior performance in monitoring GHG emissions (CO2|R=0.8431, SquD=17.1759, WASPAS=1.88E-07; N2O|R=0.8431, SquD=17.1759, WASPAS=1.88E-07; H2O| R=0.8431, SquD=17.1759, WASPAS=1.88E-07), outperforming both LightGBM-RUN and classical GBDT. Furthermore, the explainability analysis identified dew point and soil temperature as the most influential factors in the CO2, N2O, and H2O emissions scenarios.

Исследование характеристик комбинированных устройств глубокого охлаждения дымовых газов на водогрейных котлах системы ЖКХ

One of the ways to increase the efficiency of hot water boilers of the housing and communal services system is the deep cooling of combustion products to temperatures below the dew point temperature. Currently, there are a number of studies on the use of condensation economizers and air heaters of various types to solve this problem. The use of such devices makes it possible to reduce the temperature of combustion products and, thus, increase the heat utilization coefficient, reduce fuel consumption. The use of combined flue gas cooling devices seems particularly promising, but the parameters of their operation have not yet been studied. In this regard, the development and study of the parameters of such devices are relevant. Using the techniques to design heat exchangers, a set of thermal calculations has been performed to study the characteristics of a combined deep cooling system for combustion products after a hot water boiler of the housing and communal services system, represented by an air heater and a condensation economizer. It is considered that one part of the flue gases after the air heater is sent to the economizer, and the other part goes through the bypass, bypassing the economizer, and then meets the combustion products coming out of it. The distribution of the combustion product flow before the economizer is based on the need to ensure the temperature of the mixed flow after the economizer at 70 °C. For the most common hot water boiler in the housing and communal services system of the TVG-8M type, the authors have been established the dependence of the heat transfer coefficients, the surface area of the heat exchangers and other parameters of the deep cooling system of the exhaust gases on the set value of the temperature of the combustion products after the air heater. It has been established that the proposed scheme of a combined deep cooling system for combustion products makes it possible to improve the technical and economic performance of the boiler by reducing fuel consumption. It is proved that the smoke temperature after the air heater for this type of the boiler should not be very low (not lower than 115–130 °C), since it dramatically increases the heat flow with a decrease in the average logarithmic temperature difference and leads to an increase not only in the temperature of the heated air, but also in the surface area of the air heater.

Application of the VDI 2221 method in the design of an air-to-water converter device

Indonesia's drought and water crisis problems have grown significantly in importance in recent years, especially in light of the country's fast population expansion, urbanization, and growing awareness of the effects of climate change. By 2040, there may be a clean water crisis in every region along Java's north coast, from Banten to Surabaya, according to projections made by the Indonesian Institute of Sciences (LIPI). The water crisis could be a major worldwide concern and will be a highly complicated issue. One approach to resolving the water shortage is to design an air-to-water converter device. The purpose of this research is to design a device that can efficiently and effectively convert air into water through condensation processes. The design will be based on the VDI 2221 Method. The rationale behind selecting the VDI 2221 Method is that it is a methodical approach to design that can help a designer generate and guide multiple design options. The primary mechanism of this apparatus is condensation, which turns air into dew points. The author utilized Solidworks 2020 to create the air-to-water conversion device. Through the stages of VDI 2221, the finest design outcomes were obtained from this design. The air-to-water converter mechanism in this design is constructed by version 2, which was chosen as the best option. Based on ASTM A36 material, theoretical calculations on the frame produced a safety factor of 1.45 and a maximum stress of 1.72414 × 108 N/m2. Based on the turbine shaft calculation, the maximum stress of 1.07235 × 108 N/m2 and the safety factor of 2.57379 were obtained using Aluminum 6061 material

Spatiotemporal wind speed forecasting using conditional local convolution and multidimensional meteorology features

Wind speed prediction is crucial for precisely wind power forecasting and reduced maintenance costs. Highland regions, which possess a considerable wind potential, present complex meteorological conditions, making wind speed prediction challenging. Traditional weather forecasting relies on complex statistical methods and extensive prior knowledge. While recent deep learning models have improved prediction accuracy, they often assume uniform influence weight structure, limiting model effectiveness. This study introduces an enhanced Conditional Local Convolution Recurrent Network (CLCRN) model to improve spatiotemporal wind speed forecasting using multidimensional meteorological inputs such as temperature, pressure, and dew point, alongside wind components. This model addresses uniform influence model weight issue by redesigning convolution kernels to better capture local meteorological features and integrating multiple influencing factors. Our model consistently achieves lower Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values across various prediction intervals (3, 6, 9, and 12 h) compared to other models, supported by the meteorological station data from 2019 to 2021. Furthermore, the spatial distribution of the local convolution weights aligns with local wind velocity patterns in Inner Mongolia, enhancing model interpretability. These results demonstrate potential for practical applications in renewable energy planning and wind dynamics simulation.

Optimizing Precipitation Forecasting and Agricultural Water Resource Allocation Using the Gaussian-Stacked-LSTM Model

Our study investigates the use of machine learning models for daily precipitation prediction using data from 56 meteorological stations in Jilin Province, China. We evaluate Stacked Long Short-Term Memory (LSTM), Transformer, and Support Vector Regression (SVR) models, with Stacked-LSTM showing the best performance in terms of accuracy and stability, as measured by the Root Mean Square Error (RMSE). To improve robustness, Gaussian noise was introduced, particularly enhancing predictions for zero-precipitation days. Key predictors identified through variable attribution analysis include temperature, dew point, prior precipitation, and air pressure. Additionally, we demonstrate the practical benefits of precipitation forecasts in optimizing water resource allocation. A prediction-based strategy outperforms equal distribution in managing resources efficiently, as shown in a case study using 2022 Beidahu data. Overall, our research advances precipitation forecasting through deep learning and offers valuable insights for water resource management.

Removal of moisture from air by cooling method

Moisture removal from air is a critical aspect of climate control in various industrial and residential applications. The cooling method, which relies on the condensation of water vapor by lowering air temperature below its dew point, is one of the most commonly used techniques for dehumidification. This study investigates the principles, effectiveness, and limitations of the cooling method in different environmental conditions. By examining the impact of factors such as ambient temperature, humidity levels, and cooling system design, this research aims to identify strategies for optimizing energy efficiency in dehumidification processes. Experimental results highlight the correlation between cooling temperature and condensation rates, emphasizing the need for advanced cooling technologies to reduce energy consumption. The findings contribute to the development of more sustainable and cost-effective dehumidification systems, offering valuable insights for enhancing HVAC performance and reducing environmental impact.

Influence of Atmospheric Gas Species on an Argyrodite-Type Sulfide Solid Electrolyte During Moisture Exposure.

Sulfide solid electrolytes have potential in practical all-solid-state batteries owing to their high formability and ionic conductivity. However, sulfide solid electrolytes are limited by the generation of toxic hydrogen sulfide and conductivity deterioration upon moisture exposure. Although numerous studies have investigated the hydrolysis degradation induced by "moisture," the influence of "atmospheric gases" during moisture exposure has not been extensively investigated despite the importance for practical fabrication. Therefore, in this study, we investigated the impact of atmospheric gases during moisture exposure on an argyrodite-type Li6PS5Cl via electrochemical impedance spectroscopy, X-ray diffraction, X-ray absorption spectroscopy, and X-ray photoelectron spectroscopy. The electrolyte powder was exposed to various atmospheric gases, namely Ar, Ar + 500 ppm CO2, O2, and O2 + 500 ppm CO2, with moisture at a dew point of -20 °C, and H2S gas generation was monitored. As a result, the amount of H2S gas did not depend on the atmospheric gases. However, the atmospheric gases had a significant effect on the decrease in conductivity. Spectroscopic analyses revealed that CO2 facilitates the formation of carbonates and that O2 promotes the formation of phosphates and sulfonates. The formation of these compounds leads to surface degradation, which further decreases the conductivity.

Dew Point Characteristics at Synoptic Stations in Northern Benin

The dew point temperature is a very important parameter for hydro and agro-climatological research. This work studied the temporal variability of dew point temperature in Northern Benin. The dew point data comes from three synoptic stations in Northern Benin and covers the period from January 1980 to February 2019. After, correction and homogenization of the data, statistical methods are employed to analyze its structure, distribution, and temporal variability. The average of the dataset is 16.607°C with a standard deviation of 6.871 and a coefficient of variation of 0.414. The distribution is negatively skewed without extreme values, with an interquartile range of 10.284. The analysis by days of month indicates an irregular change in values at the beginning of month. The days of week show a minimum at the beginning and a maximum at the end of week. The analysis of dew point temperature data by months of the year shows a bell-shaped distribution with a plateau covering the months from May to September. The average dew point temperature has been increasing over the years. The data also describe a non-stationary periodic series approved by statistical tests. The absence of an inflection point in the data and in the trend means that the distribution is evolving and cyclical but not regular.

Numerical and experimental study on corrosion mechanism of the water-oil two-phase flow in the atmospheric tower top system

The atmospheric tower is the main component of the crude oil refining units and is a critical area of concern due to the risk of corrosion failure. Research on the corrosion mechanism and risk protection of atmospheric tower top systems plays a vital role in the safe operation of refineries. In this paper, the water-oil two phase process flow of the atmospheric tower top system was simulated by Aspen software. A corresponding flow corrosion simulation experiment was designed according to the main characteristic parameters of the simulated stream. The experimental results under varying temperature and impact angle conditions were analyzed by using microanalysis technology and CFD simulation methods. The process and mechanism of corrosion failure of tower top system under ammonia salt and dew point corrosion environment are revealed in physicochemical aspects. The results show that the corrosion rate was highest at the dew point temperature, but as the temperature decreases, the corrosion products adsorbed and accumulated on the surface are more difficult to remove from the surface, which slows down the corrosion rate. When the impact angle increased from 0° to 60°, the increment of the surface fluid impact force and turbulence intensity makes the corrosion products easier to be stripped off, which enhances the mass transfer efficiency between the corrosive medium and the surface to accelerate the corrosion rate. Different phase states and flow modes affected the morphology of corrosion pits on the surface. The depth and size of the pits in only water phase corrosion were larger than those in the two-phase flow corrosion, Additionally, as the impact angle increased from 0°, the morphology of pits changes from a relatively flat pattern to a more undulating shape. The research results reveal the characteristics of the main corrosion types occurring in the atmospheric tower top system of the oil refining industry, and provide a reference for corrosion risk protection methods.

Study on the Impact of Porous Media on Condensate Gas Depletion: A Case Study of Reservoir in Xihu Sag.

During the depletion and pressure reduction process in condensate gas reservoirs, the precipitation of condensate oil transforms the single-phase gas flow into a two-phase gas-liquid flow, significantly reducing the permeability. Currently, microscopic studies of the phase behavior of condensate gas in porous media mainly focus on observing and describing the occurrence of condensate oil, lacking quantitative calculations and direct observations of condensate oil throughout the entire depletion cycle. This paper uses a microvisualization method to simulate the depletion process of condensate gas reservoirs. Condensate gases with oil contents of 175.3 and 505.5 g/cm3 were prepared by mixing methane, ethane, hexane, and decane in specific proportions. Pore structures were extracted from thin sections of real core casts, and microfluidic chips with a minimum pore diameter of 20 μm and an areal porosity of 20.75% were fabricated by using a chemical wet etching method. Subsequently, microfluidic condensate gas depletion experiments were conducted with chip images recorded during the depletion process. Grayscale analysis of the depletion images was performed using ImageJ software to quantitatively calculate condensate oil saturation and recovery rates, analyzing the effects of different condensate oil contents on condensate gas depletion, and comparing the differences between depletion in porous media and in a PVT cell. The conclusions drawn are as follows: the dew points of high and low in the porous media are 3.15% and 1.85% higher than those in the PVT cell, respectively. In the early stages of depletion, condensate oil saturation in porous media is higher than that in the PVT cell, while in the middle to late stages, condensate oil saturation in porous media is lower than that in the PVT cell. The condensate oil recovery rate in porous media is significantly higher than the depletion recovery rate in the PVT cell. Condensate oil tends to precipitate and disperse at blind ends and corners, while it easily forms patches in mainstream large pores.

The Evaluation of Global and Regional Applications of Model for Prediction Across Scales-Atmosphere (MPAS) Against Weather Research Forecast (WRF) Model over California for a Winter (2013 DISCOVER-AQ) and Summer (2016 CABOTS) Episode

The Model for Prediction Across Scales-Atmosphere (MPAS) was used to simulate meteorological conditions for a two-week winter episode during 10–23 January 2013, and a two-week summer episode during 18–31 July 2016, using both as a global model and a regional model with a focus on California. The results of both global and regional applications of MPAS were compared against the surface and upper air rawinsonde observations while the variations of characteristic meteorological variables and modeling errors were evaluated in space, time, and statistical sense. The results of the Advanced Weather Research and Forecast (WRF-ARW, hereafter WRF) model simulations for the same episodes were also used to evaluate the results of both applications of MPAS. The temporal analyses performed at surface stations indicate that both global and regional applications of MPAS and WRF model predict the diurnal evolution of characteristic meteorological parameters reasonably well in both winter and summer episodes studied here. The average diurnal bias in predicting 2 m temperature by MPAS and WRF are about the same with a maximum of 2 °C in winter and 1 °C in summer while that of 2 m mixing ratio is within 1 g/kg for all three modeling applications. The rawinsonde profiles of temperature, dew point temperature, and wind direction agree reasonably well with observations while wind speed is underestimated by all three applications. The comparisons of the spatial distribution of anomaly correlation and mean bias errors calculated from each model results for 2 m temperature, 2 m water vapor mixing ratio, 10 m wind speed and wind direction indicate that all three models have similar magnitudes of agreement with observations as well as errors away from observations throughout California.

Hydrate formation and its influence on natural gas pipeline: Simulation study

Natural gas transportation is plagued with the twin problem of hydrate and corrosion that occurs when a hydrate prone gas is conveyed in such a manner that the pipeline operating conditions fall within hydrate formation region of the hydrate phase envelop. This study simultaneously studied this twin problem, establishing their interdependent using gas sample from Niger Delta that was meticulously simulated and analysed for hydrate formation possibilities with the aid of Unism software. CO2 corrosion was simulated using NORSOK M-506 standard model in matlab. Major factors considered are the relationship between corrosion rate and temperature, corrosion rate and PH, corrosion-temperature relationship for varying CO2 mole percent, and PH values. The result from this study established that both type I and type II hydrates could form at the operating conditions of 5OC and 60 bar. Rate of corrosion decreases and increases with increase in PH and temperature respectively to a certain temperature of approximately 78 OC, then a dip in rate of corrosion. Corrosion-temperature relationship for varying PH and CO2 mole percent shows a decrease in corrosion rate with an in increase in PH, and an increase with increase in CO2 mole percent, with a rise as high as 5,7 mm/year at a 3 mole percent. This value and trend portray a bad omen for the affected pipeline. This study recommends that natural gas to be transported by pipeline should be sweetened and processed to remove H2S, CO2 and mercaptans if present and also to satisfy maximum dew point specification.

Associations between environmental factors and running performance: An observational study of the Berlin Marathon.

Extensive research has delved into the impact of environmental circumstances on the pacing and performance of professional marathon runners. However, the effects of environmental conditions on the pacing strategies employed by marathon participants in general remain relatively unexplored. This study aimed to examine the potential associations between various environmental factors, encompassing temperature, barometric pressure, humidity, precipitation, sunshine, cloud cover, wind speed, and dew point, and the pacing behavior of men and women. The retrospective analysis involved a comprehensive dataset comprising records from a total of 668,509 runners (520,521 men and 147,988 women) who participated in the 'Berlin Marathon' events between the years 1999 and 2019. Through correlations, Ordinary Least Squares (OLS) regression, and machine learning (ML) methods, we investigated the relationships between adjusted average temperature values, barometric pressure, humidity, precipitation, sunshine, cloud cover, wind speed, and dew point, and their impact on race times and paces. This analysis was conducted across distinct performance groups, segmented by 30-minute intervals, for race durations between 2 hours and 30 minutes to 6 hours. The results revealed a noteworthy negative correlation between rising temperatures and declining humidity throughout the day and the running speed of marathon participants in the 'Berlin Marathon.' This effect was more pronounced among men than women. The average pace for the full race showed positive correlations with temperature and minutes of sunshine for both men and women. However, it is important to note that the predictive capacity of our model, utilizing weather variables as predictors, was limited, accounting for only 10% of the variance in race pace. The susceptibility to temperature and humidity fluctuations exhibited a discernible increase as the marathon progressed. While weather conditions exerted discernible influences on running speeds and outcomes, they did not emerge as significant predictors of pacing.

Linear Discriminant Analysis for Predicting Net Blotch Severity in Spring Barley with Meteorological Data in Finland

Predictive information on plant diseases could help to reduce and optimize the usage of pesticides in agriculture. This research presents classification procedures with linear discriminant analysis to predict three possible severity levels of net blotch in spring barley in Finland. The weather data utilized for classification included mathematical transformations, namely features of outdoor temperature and air humidity with calculated dew point temperature values. Historical field observations of net blotch density were utilized as a target class for the identification of classifiers. The performance of classifiers was analyzed in sliding data windows of two weeks with selected, cumulative, summed feature values. According to classification results from 36 yearly data sets, the prediction of net blotch occurrence in spring barley in Finland can be considered as a linearly separable classification task. Furthermore, this can be achieved with linear discriminant analysis by combining the output probabilities of separate binary classifiers identified for each severity level of net blotch disease. In this case, perfect classification with a resolution of three different net blotch severity levels was achieved during the first 50 days from the beginning of the growing season. This strongly suggests that real-time classification based on a few weather variables measured on a daily basis can be applied to estimate the severity of net blotch in advance. This allows application of the principles of integrated pest management (IPM) and usage of pesticides only when there is a proven need.

Dynamical and rheological analysis of gas condensate reservoir characteristics and multiphase flow behavior of a well

This manuscript proposes dynamical and rheological analysis of gas condensate reservoir characteristics and multiphase flow behavior of a well located at the lower Indus basin gas condensate reservoir in Sindh, Pakistan. The main objective is to analyze the compositional variation impacts on the liquid dropout, flow behavior and well productivity. For the sake of data analysis, constant composition expansion and constant volume depletion capabilities have been tested via pressure, volume and temperature cell under measurement of dew point and liquid dropout flow. By invoking CMG: Win prop simulator on the basis of equation of state namely Peng Robenson and Soave Redlish Kwong equations, phase diagram and well stream physical parameters have been analyzed. Our result suggested crucial significance for the behavior of flow and well productivity that liquid generation changes with the physical properties of flowing stream namely vapor viscosity, compressibility factor and vapor density due to pressure depletion showing decreasing trend. Mole percent of lighter components C1 mole percent increases from 54.567 to 62.153. While mole percent of heavier components C18+ decreases from 3.453 to 1.513 with 45% liquid dropout. In brevity, maximum well productivity has been observed in composition-1 which is 5.005 MMscf/day at pressure of 3131 psig.

Performance of advanced-deep learning algorithm for modeling and prediction of rain height over some selected locations in the tropics and sub-tropics zone for radio propagation applications

As demand for high-frequency broadband communication services keeps rising, rain-induced attenuation remains the predominant threat to radiowave propagation. Accurate prediction of attenuation requires continuous measurement and monitoring of rain-induced meteorological parameters, specifically rain rate and rain height, due to their spatio-temporal variations. Rain height is an upper dataset mostly computed from Zero- degree Isotherm Heights (ZDIH) measured by radar. This research proposes a novel approach for predicting rain height from earth surface data such as surface temperature, pressure, total cloud cover, dew point temperature, surface solar radiation, water vapor amount in the air, and humidity. This research investigates the relationship between meteorological surface data and rain height. Subsequently, six machine learning models were employed for predicting rain height using the ten years surface data as input variables. The models were applied to six sub-tropical (Polokwane, Pretoria. and Cape Town) and tropical (Sokoto, Akure, and Port Harcourt (PH)) stations in South Africa and Nigeria, respectively. Analysis of the results shows that the Gradient Boosting Algorithm (GBA) performed best with determination coefficients greater than 0.80 and RMSE less than 350 in all three stations in South Africa. However, all the models failed to produce good result for the Nigeria stations. The Random Forest model has the fairest performance metrics with r2 of 0.40, 0.46 and 0.46 in Sokoto, Akure and PH. respectively. GBA is recommended for predicting rain height in South Africa. The research outcome would assist radio engineers in improving the prediction of rain-induced attenuation and determining appropriate fade mitigation techniques.