Dew Point Research Articles

Concerted Influence of H2O and CO2: Moisture Exposure of Sulfide Solid Electrolyte Li4SnS4.

Although moisture-induced deterioration mechanisms in sulfide solid electrolytes to enhance atmospheric stability have been investigated, the additional impact of CO2 exposure remains unclear. This study investigated the generation of H2S from Li4SnS4 under H2O and CO2 exposure. Li4SnS4 was exposed to Ar gas at a dew point of 0 °C with and without 500 ppm of CO2, and its ion conductive properties were evaluated. Although the lithium-ion conductivity of Li4SnS4 decreased regardless of the presence of CO2, the amount of H2S generated with CO2 was five times higher. To elucidate the underlying mechanism, X-ray diffraction and Raman spectroscopy were used. Without CO2, hydrate Li4SnS4·4H2O formation markedly increased, whereas, with CO2, it increased a little. The difference revealed distinct deterioration mechanisms leading to a decrease in lithium-ion conductivity: without CO2, adsorbed H2O and Li4SnS4·4H2O contributed to the decrease, while with CO2, a weak acid dissociation reaction could reduce the thermodynamic stability of the moisture-exposed Li4SnS4 surface including Li4SnS4·4H2O and adsorbed H2O, promoting H2S release and carbonate formation. This was supported by the recovery of lithium-ion conductivity after vacuum heating. The concerted influence of H2O and CO2 provides valuable insights into the fundamental deterioration mechanisms in sulfide solid electrolytes that could be applied in battery manufacturing processes.

Influence of Meteorological Parameters on Indoor Radon Concentration Levels in the Aksu School

The radon concentration activity in buildings is influenced by various factors, including meteorological elements like temperature, pressure, and precipitation, which are recognized as significant influencers. The fluctuations of indoor radon in premises are related to seasonal change. This study aimed to understand better the effects of environmental parameters on indoor radon concentration levels in the Aksu school. Indoor and outdoor temperature differentials heavily influence diurnal indoor radon patterns. The analysis indicates that the correlation between indoor radon and outdoor temperature, dew point, and air humidity is weak and negligible for atmospheric pressure, wind speed, and precipitation, as determined by the obtained values of R2 and the Chaddock scale. The multiple regression model is characterized by the correlation coefficient rxy = 0.605, which corresponds to a close relationship on the Chaddock scale.

Effect of salt concentration on osmotic potential in drying soils—Measurement and models

AbstractThe water potential in drying soils, comprising both matric potential and osmotic potential components, can be measured using the dew point method (DPM). By combining DPM data with retention curve data acquired from techniques such as the suction plate method or the simplified evaporation method (SEM), it becomes possible to determine the soil water retention curve across the entire moisture spectrum. However, as the latter methods only determine the matric potential, the osmotic potential component in DPM data must either be negligible or known so that osmotic and matric potential components can be separated. This study aims to critically analyse common approaches for calculating the osmotic potential. To achieve this, we measured the water retention properties of a silt loam, a sandy loam and a sand across the entire moisture range by combining SEM and DPM. By using almost salt‐free soil material, we characterized reference water retention curves with negligible osmotic potential components. The impact of salt on water potential was analysed by conditioning soils with MgCl2 solutions of different concentrations, drying them, and measuring the water potential at different water contents using the DPM. The resulting water potentials were compared to the reference potentials and differences were interpreted as the osmotic potential component. The DPM‐measured water potentials in drying soils can be significantly affected by osmotic potential, especially at higher matric potentials (low suctions). Two models accounting for ideal and one model accounting for non‐ideal electrolyte behaviour were used to compare osmotic potential predictions with measurements. At low to medium salt concentrations, all models performed fairly well. At high concentrations, only the model accounting for non‐ideal behaviour predicted the osmotic potential satisfactorily, whereas at very high concentrations, all models underestimated the impact of osmotic potential on water potential. This suggests that the surface properties of the soil matrix, such as the specific surface area and surface charges, may lead to a decrease in osmotic potential beyond what is expected in pure solutions.

Prediction of carbon dioxide emissions from Atlantic Canadian potato fields using advanced hybridized machine learning algorithms – Nexus of field data and modelling

In this study, three novel machine learning algorithms of additive regression-random forest (AR-RF), Iterative Classifier Optimizer (ICO-AR-RF), and multi-scheme (MS-RF) were explored for carbon dioxide (CO2) flux rate prediction from three agricultural fields. To build the dataset, 401 samples were collected from two fields in Prince Edward Island (PEI) and 122 samples from the New Brunswick (NB), Canada. In addition, soil moisture (SM), temperature (ST), and electrical conductivity (EC), alongside eight climatic variables including wind speed (WS), solar radiation (SR), relative humidity (RH), precipitation (PCP), air temperature (AT), dew point (DP), vapour pressure difference (VPD) and reference evapotranspiration (ETo) were also collected. Greedy stepwise (GS) approach was implemented for feature selection. Finally, different qualitative (scatter plot, line graph, Taylor diagram, box plot, and Rug plot), and quantitative (uncertainty analysis, root mean square error (RMSE), percent of BIAS (PBIAS), Nash Sutcliff efficiency (NSE) and RMSE-observations standard deviation ratio (RSR)) techniques were used for model evaluation and comparison. Results of feature selection approaches revealed that DP, AT, SM, and ST are the four most effective variables at CO2 prediction in PEI, while AT, RH, DP, and ST are the most effective in the NB study area. For optimum input scenario, the GS algorithm was applied, and results showed that a combination of DP, AT, ST, SM, and ETo was the best for the PEI study area, while for NB, all input variables should be involved. Our analysis, for prediction of CO2 fluxes, confirmed that the ICO-AR-RF model performed the best at both PEI (RMSE=0.70, NSE=0.76, PBIAS=-5.11, RSR=0.48) and NB (RMSE=0.74, NSE=0.75, PBIAS=3.23, RSR=0.50), followed by MS-RF and AR-RF. Uncertainty analysis showed that CO2 prediction is more sensitive to input scenario selection than models in both study areas. Results revealed that climatic variables are more effective in CO2 prediction than soil characteristics and the developed hybrid model ICO-AR-RF can be a promising tool for decision-makers and beneficial for stakeholders.

Forecasting Nighttime Frost-induced Black Ice Formation on Bridges Using Atmospheric Data

Background Faced with the tragedies caused by black ice in winter, especially on bridges, it is imperative to forecast black ice for preventive maintenance and to notify drivers approaching the dangerous spots. Methods In this study, three different machine learning algorithms—Deep Neural Network (DNN), Random Forest (RF), and Support Vector Machine (SVM)—were employed to predict nighttime black ice induced by frost on three bridges in Korea. Input data consisted of atmospheric data (air temperature, relative humidity, dew point, and differences between air temperature and relative humidity over two consecutive days) provided by the weather agency. Results To assess the employed models, reference data were generated based on the physical principle that ice forms when the pavement temperature is lower than the dew point temperature and negative. The pavement temperature was obtained using an infrared surface temperature sensor mounted on a maintenance patrol vehicle. Consequently, DNN and RF showed higher performance with an accuracy of 95%, followed by SVM with an accuracy of 92.5%. Conclusion Due to the use of easily obtainable atmospheric data, the findings of this study can be practically applied to preventive maintenance and driver information, thereby enhancing traffic safety.

Comparative analysis of machine learning models for predicting PM2.5 concentrations using meteorological and chemical indicators

Air pollution significantly impacts human health, causing numerous premature deaths, particularly with the rise in PM2.5 concentrations. Therefore, comparing different machine learning (ML) models for predicting PM2.5 concentration is crucial. This research focuses on six ML models: Linear Regression (LR), Regression Tree (RT), Support Vector Machine (SVM), Ensemble Regression (ERT), Gaussian Process Regression (GPR), and Artificial Neural Networks (ANN). Trained on six years of data (July 2015–December 2021) with optimized hyperparameters, the models consider eight meteorological and chemical indicators as PM2.5 predictors, including temperature, relative humidity, air pressure, O3, SO2, NO2, dew point, and wind speed. Model efficiency is assessed using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Correlation Coefficient (R), and Coefficient of Determination (R2) values. The models achieve R2 and RMSE values as follows: LR (0.72, 13.52), RT (0.8, 12.156), SVM (0.82, 10.28), ERT (0.81, 11.87), GPR (0.94, 7.65), and ANN (0.99, 2.36). These metrics indicate the superior performance of ANN, with its R2 value approaching 1 and the lowest RMSE compared to other models. The results highlight the effectiveness of ANN, particularly the model with three hidden layers, in predicting PM2.5 concentration. Utilizing ML models for this purpose is crucial for understanding and mitigating the impacts on human health and the environment, with ANN emerging as a promising tool for various investigations.

Numerical Modelling of Phase Transformations of Water Droplets for Efficient Heat Recovery from Biofuel Flue Gas

The phase transformations of water droplets in the humid air flow for water injection into the flue gas in biofuel combustion technology under typical boundary conditions have been simulated. The numerical study was performed in two stages. The first one defines the influence of radiation on the thermal state of the water droplets and on the cycle of phase transformation modes. It is shown that the influence of radiation in a flue gas flow of 150℃→200℃ temperature on the thermal state of quantitatively injected water droplets is not significant, but it induces qualitative changes in the temperature field and influences phase transformations cycle of the droplets. In the second step, the cycling of the droplet phase transformation modes for water injection into the exhaust flue gas prior to a condensing economiser under typical boundary conditions was numerically investigated. It was justified that for efficient cooling and humidification of the flue gas before the condensing economiser it is necessary to inject water heated above the dew-point temperature by dispersing it into fine droplets.

High potency of application on an open direct-contact thermal storage using humid air

To reuse low-temperature wasted heat as a thermal resource for high temperature, a direct-contact adsorption thermal storage was focused using humid air and zeolite 13X particles as the working fluid and adsorbent, respectively. Only a few previous studies have chosen the working fluid in gaseous form because it is unavailable for latent heat in generating heat sources. Recovering waste heat in humid air to generate hotter steam is unique and becomes an originality of our present work. The time required to regenerate zeolite particles and the maximum temperature of the generated steam were investigated assuming a warm-up device for a vehicle. The time required to regenerate zeolite was investigated by changing the dew point, temperature, and superficial velocity of the inlet humid air. It was mainly affected by the temperature of the inlet air. The absorbent was regenerated within 30 min when the humid air preheated to 200 °C was supplied. On the other hand, the maximum steam temperature was investigated by changing the superficial velocity and temperature of saturated inlet humid air. As one of the significant and novel finding in this work, the steam of >200 °C was obtained as a high-temperature heat source even with saturated humid air unavailable latent heat. Moreover, as theoretical knowledge, it was revealed that the maximum temperature of the heat source can be estimated by the relationship between the heat balance on the packed bed and adsorption equilibrium.

Comprehensive analysis of polysulfone membrane humidifier in hydrogen fuel cell vehicles: Experimental and theoretical approaches

This study performed a comprehensive experimental and theoretical analysis of a shell-and-tube type gas-to-gas humidifier module in a 95 kW class hydrogen fuel cell vehicle. The humidifier module features a polysulfone hollow fiber membrane characterized by its hydrophilic and non-fluorinated properties. Experimental investigations were conducted to understand the impacts of the inlet temperature, relative humidity, and temperature differences on heat and mass transfer rates, and the approach dew point temperature (ADT). The results showed that the ADT increased from 12.42 to 15.89 °C with a temperature difference of 15 °C between the shell and tube inlet sides, as the flow rate increased from 1000 to 3400 L/min, with the highest performance observed at lower flow rates. Simultaneously, a detailed theoretical model was developed, incorporating a new dual-mode sorption approach that accounts for temperature dependency within the solution-diffusion mechanism across the membrane. The developed model was in good agreement with the experimental results, with a relative error of less than 5 %. Furthermore, the dynamic model accurately predicts the response of the humidifier module to step changes in feed flow rates, simulating load variations in the fuel cell vehicle. This provides valuable insights into the humidifier's adaptability and responsiveness, which are crucial for optimizing its performance under various operating conditions.

A universal triangle method for evapotranspiration estimation with MODIS products and routine meteorological observations: Algorithm development and global validation

The surface temperature (Ts) and vegetation index (VI) triangle method is one of the most famous and widely applied methods for regional evapotranspiration (ET) estimation. However, no robust validation has been performed yet to evaluate its accuracy on a global scale, making its accuracy unclear when compared with other global ET models. The main reason behind it is that most traditional triangle models depend on the Ts-VI feature space within a certain spatial domain to parameterize their boundary conditions. The domain dependency makes it impossible to derive universal boundaries for continental or global application. Under this background, this study presented a universal triangle method for ET estimation with Moderate Resolution Imaging Spectroradiometer (MODIS) products and routine meteorological observations. The solution for boundary conditions was performed pixel by pixel without the consideration of spatial domain based configuration. Specifically, the dry boundary was solved theoretically based on the surface energy balance equation, and the wet boundary was substituted by wet-bulb temperature estimated from dew point temperature and air temperature. The validation against ground observations collected from 33 flux towers worldwide shows that this universal triangle method achieved evaporative fraction (EF) estimation with the correlation coefficient of 0.65, the mean absolute error of 0.17, the root-mean-square-error of 0.20, and the bias of 0.13. These four metrics of daily ET estimation were 0.83, 0.71 mm/d, 0.90 mm/d, and 0.41 mm/d, respectively. This analysis is the first comprehensive validation of the triangle method on a global scale. The comparison with early work suggests that our universal triangle method on a global scale has reached a comparable accuracy to traditional triangle models at regional scale. However, the universal triangle method has the capacity to monitor ET continuously pixel by pixel without domain dependency. This is just what most traditional triangle methods do not possess.

Modeling of moisture content during baking with different approaches for effective diffusivity and evaluation of quality variables in baked potato slices.

Baking is a healthier alternative to frying, since texture, color, smell, and flavor are developed, without adding oil. The objective was to estimate the moisture content in potato slices, during baking using Fick's law of diffusion to model internal moisture transport and to assess the impact on quality attributes. Moisture transport kinetics were examined at three baking temperatures of 120, 130, and 140°C. Fick's law was employed to estimate average moisture content using different methods: considering both a constant (method of slopes by subperiods, MSS; and method of successive approximations, MSA) and a variable (represented as a quadratic function of time, QFT) behavior of effective diffusivity (De). Three quality variables were analyzed: water activity (aw, dew point hygrometry), total color difference (∆E, colorimetry), and fracturability (F, universal testing machine). The diffusivity estimated with the time-varying De method provided a more realistic description of moisture migration during baking. The aw, ∆E, and F for baked potato slices ranged from 0.234 to 0.276, 17.9 to 24.6, and 5.20 to 5.49 N, respectively. These attributes imply improved stability and extended shelf life, showing typical colors and texture changes for baked snacks. These changes are linked to variations in diffusivity, influenced by the size and quantity of micropores within the food structure. This study could allow an accurate prediction of mass transfer by considering variable De, facilitating the optimization of baking conditions. PRACTICAL APPLICATION: The analysis of the moisture content using Fick's law, considering a time-varying diffusivity, enables the optimization of the baking process for foods. This helps minimize the occurrence of defective products.

Environmental conditions in equine indoor arenas: A descriptive study

Indoor arenas do not always include mechanical ventilation or stirring fans and occupancy by horses and humans can be sporadic and inconsistent, which creates a challenging space for understanding and predicting variations in temperature, moisture, and airflow. To understand the interior environment within indoor arenas, monitoring was conducted at 15 facilities within 200 kilometres of Lexington, KY. Environmental monitoring of dry bulb temperature, relative humidity, dew point temperature, air speeds, and solar radiation took place over 7 days in the winter and summer to examine temporal variability. Environmental data was collected every 5 minutes using the HOBO RX3000 Remote Monitoring Station with the HOBOnet Temp/RH Sensor, HOBOnet Solar Radiation (Silicon Pyranometer) Sensor, and HOBOnet Ultrasonic Wind Speed and Direction Sensor. Clear seasonal differences and diurnal patterns were evident in all environmental conditions, but the relative humidity. The relative humidity and dew point temperatures indicated moisture could be an issue in many of the indoor arenas. High relative humidity and excess moisture can negatively impact horse and human health as well as the lifespan of the facility. Similar results to previous spatial variability indoor arena characterizations were observed during the environmental monitoring with air speeds being below the threshold for still air in livestock facilities (0.51 m s-1). Sensor technology and implementation provides a better understanding of the interior environment and how indoor arena design can impact it.

Dew points for hydrogen-rich (hydrogen + propane) and (hydrogen + n-butane) mixtures determined with a microwave re-entrant cavity resonator

Dew-point measurements were performed for hydrogen-rich binary mixtures with hydrocarbon mole fractions of 0.09995 C3H8, 0.1499 C3H8, 0.0513 n-C4H10, and 0.09888 n-C4H10 using a modified microwave re-entrant cavity apparatus. Isochoric measurements were conducted in the temperature range of (256 to 286) K with pressures up to 6.8 MPa and 2.0 MPa for mixtures containing propane and n-butane, respectively. The combined expanded uncertainties (k = 2) in dew-point temperature and pressure were estimated to be between (0.35 and 2.0) K and (0.0011 and 0.030) MPa. The agreement with predictions of the GERG-2008 equation of state is within 5.5%. However, better agreement with predicted values was achieved using a cubic equation recently developed in-house. Moreover, experiments with short- and long-term exposure of pressure sensors to hydrogen have shown that pressure measurement, and thus the uncertainty, can be affected; further investigation on this matter is therefore inevitable to improve hydrogen thermophysical property data.

Optimizing phase equilibrium predictions for the liquefaction of supercritical water gasification products: Enhancing energy storage solutions through advanced thermodynamic modeling

Supercritical water gasification products typically consist of a vapor mixture consisting of H2 and CO2 with high pressure. Liquefaction of supercritical water gasification products not only allows the vapor mixture to be reduced in volume for easy transportation, but also allows liquid CO2 to be obtained from it, and also serves as a form of energy storage. Under the existing thermodynamic model, the prediction of the liquefaction dew point under high pressure is easily diverged and the accuracy is not enough. In this paper, we obtain the value of CO2+H2 binary interaction coefficient under high pressure kij=0.146 by the fitting method and, which improves the convergence of the predicted phase equilibrium data at high pressure and obtains the results with higher accuracy. In the prediction of dew point of supercritical water gasification products, the fluctuation of H2 content has little effect on the liquefaction pressure, and more significant is the effect of the liquefaction temperature, and it is more economical to carry out the liquefaction work within the range of −30 °C to −5 °C. At the same time, in the liquefaction products lower than −10 °C, the liquid CO2 with the mole fraction of more than 0.9 can be obtained.

A MODEL FOR FORECASTING DEW POINT PRESSURE IN NIGER DELTA CONDENSATE RESERVOIRS USING CONSTANT VOLUME DEPLETION TESTS DATA

The dew point pressure plays a critical role in developing gas condensate reservoirs. It is a crucial factor used in fluid characterisation, performance estimation of gas reservoirs, and design of production systems. However, the composition of gas condensate fluid varies from one location to another, and thus, empirical correlations have been developed to determine the dew point pressure without conducting routine tests. As a result, correlations that are solely useful in the region where they were developed and analysed are developed. This work developed an empirical correlation using data from gas condensate wells in the Niger Delta using multiple linear regression. The model was developed using the Analytical Tools and techniques in Microsoft Excel. To develop the model, we utilised 63 data sets from the Constant Volume Depletion (CVD) experiment, which involved gas condensates from resources in the Niger Delta. The model comprises an empirical correlation that estimates the dew point pressure for gas condensate reservoirs. It uses compositional information of the fluid and reservoir temperature as input parameters. The correlation is derived through multiple regression analyses. Comparing the prediction accuracy of formulas based on the developed model and other conventional methods indicated that this model is more accurate than other statistical methods in predicting DPP within the Niger Delta region. This model can produce more accurate results than other estimation methods when working under limited field information and time constraints. The correlation developed in this process has a coefficient of determination of R2=0.869 and an Average Absolute Relative Error (AARE) of -2.0767%, along with a root mean square of 195279, indicating a high level of reliability in the proposed correlation.

High-Performance Humidity Sensor Capable for Monitoring Low-Moisture Levels in Energy Industries

Humidity sensors are extensively used in industrial processing and environmental control. There are two categories of humidity sensors: High-humidity (relative humidity) measurement and Low-humidity (moisture) measurement. The proper units for low-humidity measurement are dew point and parts per million in volume (ppmv) or parts per billion in volume (ppbv). It is very important to monitor moisture levels in natural gas during processing and transportation in pipelines. It is of critical importance to monitor moisture levels in lithium-ion battery manufacturing. In these cases, the moisture levels are limited to be less than -50°C dew point or 40 ppmv. The conventional relative humidity sensors cannot be used because of their low sensitivity. The dew point sensors on the market are polymer-based and gamma-phase-alumina based. The polymer-based sensors have low sensitivity, which can only accurately detect moisture levels of -60°C dew point and higher. The gamma-phase alumina sensors suffer from long-term drift due to phase change.We present novel high-performance humidity sensors based on porous alpha-phase alumina and silicon dioxide nanostructures, which are capable of detecting moisture levels as low as -100°C dew point or 13 ppbv with excellent long-term stability. The porous alpha-phase alumina films were produced by anodic-spark-deposition on aluminum substrates in which electric sparks were produced due to high-voltage anodization (>120V). Local high-temperature (about 2200°C) at the points of sparks converted the amorphous alumina into alpha-phase alumina (Sapphire). It provides an extremely stable structure for humidity sensors. Hydrophilic silicon dioxide films deposited by atomic layer deposition was used as sensing materials. The sensors’ sensitivity, temperature coefficient, response speed, and long-term stability were studied in details. The sensors have great promise for monitoring moisture levels in natural gas industry and lithium-ion battery manufacturing.

A Slow Failure Particle Swarm Optimization Long Short-Term Memory for Significant Wave Height Prediction

Significant wave height (SWH) prediction is crucial for marine safety and navigation. A slow failure particle swarm optimization for long short-term memory (SFPSO-LSTM) is proposed to enhance SWH prediction accuracy. This study utilizes data from four locations within the EAR5 dataset, covering 1 January to 31 May 2023, including variables like wind components, dewpoint temperature, sea level pressure, and sea surface temperature. These variables predict SWH at 1-h, 3-h, 6-h, and 12-h intervals. SFPSO optimizes the LSTM training process. Evaluated with R2, MAE, RMSE, and MAPE, SFPSO-LSTM outperformed the control group in 13 out of 16 experiments. Specifically, the model achieved an optimal RMSE of 0.059, a reduction of 0.009, an R2 increase to 0.991, an MAE of 0.045, and an MAPE of 0.032. Our results demonstrate that SFPSO-LSTM provides reliable and accurate SWH predictions, underscoring its potential for practical applications in marine and atmospheric sciences.

Identification of dust events in the greater Phoenix area

Dust events are common in Arizona, USA, causing significant impacts on air quality and human health. Several previous studies have compiled an analysis of the dust event climatology for the state of Arizona, yet very few used multiple Meteorological Aerodrome Reports (METARs) to determine their occurrence, seasonality, cause, and duration. In this project, we identified dust events based on measurements taken from nine different METAR Automatic Surface Observation Systems (ASOS) from the greater Phoenix area in Arizona. A total of 531 dust events were identified from 2005 to 2021. Each dust event was examined for the meteorological disturbance (synoptic and convective) that caused it. Dust events were analyzed for different meteorological characteristics (temperature, dew point, relative humidity, wind speed, wind gust, and visibility), temporal patterns, and the impacts of different climatological indices and drought conditions. Each dust event was categorized based on its intensity as either a blowing dust event or a dust storm based on the lowest visibility recorded. Separation based on cause and severity was then performed to further analyze differences in the aforementioned meteorological characteristics and parameters during dust events. Notable seasonality, causality, and temporal trends consistent with the occurrence of the summer monsoon season were found, with differences in meteorological parameters when analyzed by cause and intensity consistent with previous studies. Observations of Particulate Matter (PM) concentrations during dust and non-dust times reveal the impact of these dust events on PM10 and PM2.5 concentrations.

Correlation of Fine Particulates and Meteorological Parameters in Indoor Public Schools Environment within Benin City, Edo State, Nigeria

This study focused on the correlation of fine particulates and meteorological parameters in indoor public school environments, in Benin City, Edo State, Nigeria. Thirty public schools (17 primary and 13 secondary) were selected for the study. The PM2.5 samples were taken 1.5 – 2.0 meters within the human breathing zone. The sampling was done using an Apex 21 S Casella regular pump with a conical inhalable sampling (CIS) head for eight hours at a flow rate of 3.5L per minute (LPM). The 37-diameter quartz filters were processed in a muffle furnace for four hours at 450 – 500 oC before sampling and weighing afterward. The results obtained showed variations in the meteorological parameters with a range of temperature from 28.52 – 29.82°C. The relative humidity ranged from 84.22 – 427.78%; pressure was 1006.70 - 5052.87 mmHg; wind speed was 4.13 – 1660.88 Km/h and dew point was 24.7 – 3293.56%. The correlation coefficient showed a positive relationship exists in meteorological parameters between primary and secondary schools’ sources of enrichment. The PM2.5 mass concentration had the range of 135 – 785.31 µg/m3 which were above the stationary limits of 25 µg/m3 and 35 µg/m3 stipulated by the National Ambient Air Quality Standards and World Health Organization, respectively. These call for greater attention to alleviate all negative outcomes of environmental health issues.

Numerical simulations and an experimental study for optimal design of a 1500 [formula omitted] water-tube condensing boiler

The present study is focused on investigating the condensation process and numerical simulation for optimal design of a 1500 kW water-tube condensing boiler. The specific geometry of the heat exchanger of this boiler involves compact arrangement of spiral coils to enhance heat transfer efficiency and achieve high capacity. Experimental validation is conducted on a 580 kW condensing boiler. Numerical simulations of the flow for gas and water sides of the boiler are performed in parallel using the ANSYS-FLUENT software with the k-omega (SST) turbulence model. Twelve models with different combustion chamber dimensions, coil numbers and revolutions are considered. Important design criteria include flue gas temperature, combustion chamber pressure and water pressure drop in the coils. The results indicate that increasing effective heating surface area and reducing coils gap decrease flue gas temperature to the dew point. Increasing coil revolutions has a greater impact on flue gas temperature and boiler weight compared to increasing coil numbers. With the same number of coils, by adding an extra revolution the weight is increased approximately 17 % and the effective heating surface area about 21 %. On the other hand, for the same number of revolutions, each additional spiral coil increases both of the weight and the effective heating surface area approximately 4 %. Moreover, with an increase in the number of the coils and a decrease in the number of revolutions, the pressure within the combustion chamber is decreased. By reducing the gap size, the flue gas temperature is reduced approximately 2 °C and the combustion chamber pressure is increased about 18 %. Base on the design criteria, the model six with 27 spiral coils and 7–8 revolutions, is identified as the optimal design. This progress and capability allows the design and manufacture of high-capacity boilers for residential sector as well as for industrial applications.