Air Dew Point Temperature Research Articles

Solar driven atmospheric water harvesting system with integration of desiccant wheel and desiccant bed: An experimental investigation

Solar-driven atmospheric water harvesting (SDAWH) presents a promising solution to address water scarcity issues, particularly in arid and semi-arid regions where conventional water sources are limited. This paper presents a new SDAWH system, integrated with a desiccant wheel and desiccant bed. The desiccant bed adsorbs water vapour during night and desorbs in a day time. Whereas the desiccant wheel performs adsorption and desorption process simultaneously in day time. Therefore, the combination of these offers the potential to enhance water yield and energy efficiency compared to conventional sorption-based SDAWH systems in semi-arid climate. Further, an evacuated tube solar air heater for regeneration and an air-to-air heat exchanger for condensation of water vapour has been integrated. The desiccant bed releases large amount of water vapour in regeneration air but its desorption gradually decreases with time. On the other hand, the desiccant wheel releases a lesser quantity of water vapour into the regeneration air, but its desorption capacity remains almost constant. The system’s performance has been evaluated in a semi-arid climate. As the system is working in closed cycle, the dew point temperature of regeneration air obtained at the outlet of the desiccant wheel is 39.4 °C, which increases to 42.4 °C at the outlet of the desiccant bed. The total yield of water in a day is 7.6 L, with an energy efficiency of 9 %.

Evaluation of three suction pretreatment systems for the air compressor

Air compressor, as the power source of pneumatic tools, suffers the problems of low efficiency and high failure rate in summer. In this paper, three suction air pretreatment systems are presented, which are characterized by the cold sources: a vapor compression refrigeration unit, a water chilling unit and a combination of water chilling unit with cooling tower. Models for the thermodynamic and economic performance evaluation of the three systems are established and solved. The moisture diffusion coefficient is applied to consider the influence of the moisture condensation and the Marshall and Swift coefficients is introduced to consider the capital change over time. Formula to calculate the efficiency of refrigeration compressor is adopted. Besides, the system total efficiency, percentage of power saving (PPS) and payback period (PBP) are defined as performance indexes. A comparative study with typical meteorological data of hot-dry regions and hot-humid regions is conducted to analyze the superiority of each system under the operation conditions of varied suction air temperature and volume flow. According to the results, optimal spray water flowrate exists for each operation condition and an air–water ratio of 0.8 is recommended. When the suction air temperature decreases, the PBP of each system increases and a sharp increase occurs when the temperature is lower than the air dew point temperature. A suction air temperature of 26 ℃ is suggested. Direct air cooling with an evaporator is preferred when the suction air volume flowrate is smaller than 60 m3/min for hot-humidity regions. The combined system is significantly superior in hot-dry regions. The PBP of the three systems is all longer than 10 months. How to guarantee the usage rate is the key for enterprises to consider the use of suction air pretreatment systems.

Evaluating the Utility of Selected Machine Learning Models for Predicting Stormwater Levels in Small Streams

The consequences of climate change include extreme weather events, such as heavy rainfall. As a result, many places around the world are experiencing an increase in flood risk. The aim of this research was to assess the usefulness of selected machine learning models, including artificial neural networks (ANNs) and eXtreme Gradient Boosting (XGBoost) v2.0.3., for predicting peak stormwater levels in a small stream. The innovation of the research results from the combination of the specificity of small watersheds with machine learning techniques and the use of SHapley Additive exPlanations (SHAP) analysis, which enabled the identification of key factors, such as rainfall depth and meteorological data, significantly affect the accuracy of forecasts. The analysis showed the superiority of ANN models (R2 = 0.803–0.980, RMSE = 1.547–4.596) over XGBoost v2.0.3. (R2 = 0.796–0.951, RMSE = 2.304–4.872) in terms of forecasting effectiveness for the analyzed small stream. In addition, conducting the SHAP analysis allowed for the identification of the most crucial factors influencing forecast accuracy. The key parameters affecting the predictions included rainfall depth, stormwater level, and meteorological data such as air temperature and dew point temperature for the last day. Although the study focused on a specific stream, the methodology can be adapted for other watersheds. The results could significantly contribute to improving real-time flood warning systems, enabling local authorities and emergency management agencies to plan responses to flood threats more accurately and in a timelier manner. Additionally, the use of these models can help protect infrastructure such as roads and bridges by better predicting potential threats and enabling the implementation of appropriate preventive measures. Finally, these results can be used to inform local communities about flood risk and recommended precautions, thereby increasing awareness and preparedness for flash floods.

Performance Analysis of Atmospheric Water Extraction by Refrigerator Cum Air Conditioner

India is blessed with warm and humid climatic conditions in most of its states containing huge amount of water in atmosphere which can fulfill the increasing water demands. Atmospheric water extraction is one of the technologies where fresh water is obtained from the ambient air by condensation. The atmospheric water extracting device is working on vapour compression refrigeration cycle. In this cycle the evaporator inlet temperature is maintained at a temperature lower than the dew point temperature of the atmospheric incoming air. Therefore, the moisture present in the air condenses over the coil and it will be collected. The amount of condensate depends on psychrometric conditions of incoming air. The present work concludes that atmospheric water extracting devices are most effective in hot and humid regions. when the humidity level in atmosphere is being 56%, 8.3ml/min water is extracted and when the humidity level in atmosphere is being 36%, 1.7ml/min water is extracted.

Методика определения температуры точки росы продуктов сгорания природного газа

Currently, there are various methods to determine the dew point temperature of humid air and combustion products of distinct types of fuels. It is especially important when designing deep flue gas cooling systems in boiler plants that do not consider the composition of a particular type of fuel. In this regard, the urgent issue is the development of a simple method to calculate this value when using natural gas as fuel. The authors have used the methods of thermodynamic analysis of processes in humid air and fuel combustion products, and the results of thermal calculations of fuel combustion processes. The authors have conducted the analysis of existing methods to determine the dew point temperature for natural gas combustion products, since it is necessary to prevent condensation of water vapor in chimneys and chimney flue to ensure their operation conditions, especially during deep cooling. A simple method to calculate this value is proposed, and an example of its use is presented. The authors have analyzed the influence of such factors as the moisture content of air and natural gas, the absolute pressure of combustion products and the air flow coefficient per the dew point temperature. It is shown that the final refinement of the given value can be quite significant, and therefore the use of the method that allows considering these patterns is advisable when designing deep flue gas cooling systems. It is established that the proposed method is quite universal and can be used during combustion of natural gas of various compositions and various values of factors characterizing fuel combustion.

Impacts of urban air temperature and humidity on building cooling and heating energy demand in 15 cities of eastern China

Urban climates could significantly impact building energy consumption. This study aimed to investigate the impact of urban-rural disparities in temperature and humidity on building cooling and heating demand in multiple cities. Fixed stations were established using temperature/humidity loggers deployed at 17 pairs of urban and rural sites in 15 cities in eastern China. The study analyzed the differences in air temperature (ΔTa), relative humidity (ΔRH), and dew-point temperature (ΔTdew) between the urban and rural sites. These climatic data were then used as inputs for the energy simulation of a residential building. The following results were revealed: (1) ΔTa was consistently higher at night than during the day. In comparison with the rural reference sites, the RH values at all urban sites were lower; however, some urban sites displayed lower Tdew, while others exhibited higher Tdew. (2) Regarding annual total energy demands, urban buildings exhibited variable changes in energy components compared to rural buildings. Sensible cooling demands increased by 10 %–36 %, while heating demands decreased by 9 %–30 %. Latent cooling demands ranged from a decrease of 12 % to an increase of 17 %. As a result, overall changes in annual total energy demands (cooling + heating) were relatively small, ranging from −2 % to +7 %. (3) Regarding daily total energy demands, a 1 °C increase in ΔTa led to a 12.1 % increase in sensible cooling demand and an 8 % decrease in heating demand. At the same time, a 1 °C variation in ΔTdew corresponded to a 14.6 % change in latent cooling demand. (4) Regarding the daily peak load, modifications in local temperature and humidity in urban areas had a more pronounced impact on daily peak heating loads than on daily peak cooling loads. This study could enhance our understanding of how urban local climates affect building cooling and heating energy demands.

Influence of geographical location and outdoor meteorological parameters on indoor humidity environment in rural residential buildings during the Plum Rains Season in the hot summer and cold winter region.

Plum Rains Season (PRS) has the typical characteristics of outdoor air temperature dramatic changes and high air humidity in the hot summer and cold winter region in China. When the outdoor temperature rises rapidly during PRS, the building envelope surface temperature is probably lower than the indoor air dew point temperature, resulting in moisture condensation. This paper evaluates the influence of geographical location and outdoor meteorological parameters on the indoor humidity environment. The effects of critical parameters such as altitude, average temperature, relative humidity, total precipitation, total precipitation days, atmospheric pressure, and wind speed on the building envelope moisture condensation in nine typical cities in the hot summer and cold winter region were simulated and analyzed. The results show that the Condensation Frequency (CFn) in the western, central, and eastern regions reached the highest in April, May, and June, respectively. Among the nine typical cities, Changsha has the highest Condensation Risk (CR). In addition, the altitude, total precipitation, and atmospheric pressure have little effect on the indoor humidity environment, and it is not directly related to CR; The average temperature and total precipitation days were not related to CR in the western and eastern regions and positively correlated with CR in the central region; The wind speed was positively correlated with CR in the western and central regions and negatively correlated in the eastern region; The relative humidity can affect the indoor humidity environment and moisture condensation on the inner surface of walls, when the relative humidity increases, the CR increases.

Effect of urban heat island mitigation strategies on precipitation and temperature in Montreal, Canada: Case studies

High-resolution numerical weather prediction experiments using the Global Environmental Multiscale (GEM) model at a 250-m horizontal resolution are used to investigate the effect of the urban land-use on 2-m surface air temperature, thermal comfort, and rainfall over the Montreal (Canada) area. We focus on two different events of high temperatures lasting 2–3 days followed by intense rainfall: one is a large-scale synoptic system that crosses Montreal at night and the other is an afternoon squall line. Our model shows an overall good performance in adequately capturing the surface air temperature, dew-point temperature and rainfall during the events, although the precipitation pattern seems to be slightly blocked upwind of the city. Sensitivity experiments with different land use scenarios were conducted. Replacing all urban surfaces by low vegetation showed an increase of human comfort, lowering the heat index during the night between 2° and 6°C. Increasing the albedo of urban surfaces led to an improvement of comfort of up to 1°C during daytime, whereas adding street-level low vegetation had an improvement of comfort throughout the day of up to 0.5°C in the downtown area. With respect to precipitation, significant differences are only seen for the squall line event, for which removing the city modifies the precipitation pattern. For the large-scale synoptic system, the presence of the city does not seem to impact precipitation. These findings offer insight on the effects of urban morphology on the near-surface atmospheric conditions.

Application of vine copulas to estimate dew point temperature

In this study, the accuracy of the copula-based model in the simulation of the dew point temperature in various climates of Iran was investigated, using simulations based on vine copulas such as C-, D-, and R-vine copulas. By examining the various vine copulas and their tree sequences, the best copula and best tree sequence based on AIC, BIC, and log-likelihood were selected. The results show that based on the complete similarity in our case between C-, D- and R-vine copulas, the selected best C-vine copulas fit well the dependence between the minimum and maximum air temperatures and dew point temperature. The simulation results were analyzed using root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE) coefficient, and violin plots. The results show that the copula-based model has high accuracy at all stations. The min (max) RMSE is related to Kerman (Ahvaz) station with RMSE = 0.396 oC (0.617 oC). Also, the min (max) NSE is related to Ahvaz (Urmia) station with NSE = 0.925 (0.955). Also, according to the violin plot, it is possible to appreciate the acceptable certainty of the copula-based model. Due to the diversity of the tree sequences of vine copulas and the use of the rotated states of the internal vine copulas, as well as the possibility of interfering with the effective parameters in high dimensions, the simulation results are reliable and have no restrictions. This model can be used as the best model to estimate dew point temperature due to the full coverage of the range of changes in data.

PM2.5 Concentration Prediction in Six Major Chinese Urban Agglomerations: A Comparative Study of Various Machine Learning Methods Based on Meteorological Data

The escalating issue of air pollution in China’s rapidly developing urban areas has prompted increased attention to the role of meteorological conditions in PM2.5 pollution. This study examines the spatiotemporal distribution of PM2.5 concentrations and their relationship with meteorological factors in six major Chinese urban agglomerations from 2017 to 2020, using daily average data. Statistical and spatial analysis techniques are employed, alongside the construction of eight machine learning models for prediction purposes. The study also compares the feature importance of various meteorological factors impacting PM2.5 concentrations. Results reveal significant regional differences in both average PM2.5 levels and meteorological influences. The Multilayer Perceptron (MLP) model demonstrates the highest prediction accuracy for PM2.5 concentrations. According to the MLP model’s feature importance identification, temperature is the most significant factor affecting PM2.5 concentrations across all urban agglomerations, while wind speed and precipitation have the least impact. Contributions from air pressure and dew point temperature, however, vary among different urban agglomerations. This research considers the impact of urban agglomerations and meteorological conditions on PM2.5 and also offers valuable artificial intelligence-based insights into the key meteorological factors influencing PM2.5 concentrations in diverse regions, thereby informing the development of effective air pollution control policies.

Parametric Studies on a Two-Stage Evaporative Cooler During Tropical Climates in India

Abstract This research study aims to work out the best possible performance parameters for a two-stage direct–indirect evaporative cooler (IDEC) when it is operated in Indian climatic conditions. A numerical simulation model has been developed to estimate temperature, relative humidity, cooling capacity, and saturation efficiency for a direct evaporative cooler (DEC), when it is operated during hot and humid and hot and less humid conditions in Chennai city. Simulation results have been corroborated with those obtained from experiments. Simulation techniques have also been extended for a fin and tube heat exchanger, which acts as IDEC. Both DEC and IDEC are combined and developed into a two-stage evaporative cooler in which performance studies have been carried out. Performance parameters are optimized for all three modes of evaporative cooling. These studies reveal that the DEC system with optimized parameters has a better approach to wet bulb temperature (WBT). Hence, cooling effectiveness of DEC depends on the ambient air WBT of Indian localities. IDEC systems perform better in places where ambient air WBT is less, either hot and less humid or hot and dry climates. Two-stage evaporative cooling is the preferred technique for Chennai weather when compared with direct and indirect cooling methods. In this cooler, spray water temperature approaches the WBT of IDEC cooled air, whereas it is reduced well below ambient air WBT and approaches ambient air dew point temperature. Also, when ambient air is cooled in this new cooler, dry bulb temperature (DBT) of IDEC cooled air can be reduced below ambient air WBT.

Optimization of the performance of M-Cycle indirect evaporative cooling via thermodynamic approach

ABSTRACT For the further energy conservation, the study on the optimizing the M-Cycle indirect evaporative cooling (MIEC) performance is meaningful. With the aim of the optimization, a model of a general MIEC system was established, the thermodynamic entropy production optimization method was used to fully reflect the energy quality and irreversibility. The inlet parameters, supply air ratio, dew point temperature efficiency, unit-cooling capacity and Entropy Production Number were used to analyze and improve the cooling and thermodynamic performance of the general MIEC. A total of 6750 air treatment processes have been studied with the self-programmed FORTRA. It is concluded that when the inlet temperature is high, with the increase in inlet relative humidity, the unit-cooling capacity of the system greatly improves while the Entropy Production Number increases relatively small. When the ambient humidity variation range is large, the irreversible loss of the system can be reduced by coordinating the supply air ratio and dew point temperature efficiency of the MIEC system. Ultimately, the results of this study will provide theoretical reference for the design and operation of the practical engineering of the MIEC.

핀-튜브 열교환기에서 불균형적 착상의 영향에 대한 연구

The aim of this study was to investigate effect of non-uniform frost deposition on fin-tube heat exchangers. Frost deposition phenomena mainly occur in an outdoor unit of a heat pump system because the temperature of the evaporator fin surface is lower than the dew point temperature of the intake air. To minimize performance drop of the evaporator, it is important to consider frost deposition pattern and its impact on the evaporator heat exchanger. As a first step, an efficient simulation model of the fin-tube heat exchanger under frosting condition was introduced and verified with experimental data. Frost non-uniformity was then investigated in terms of two different types of frost non-uniformity: through-plane non-uniformity and in-plane non-uniformity. As a result, it was found that the through-plane non-uniformity was dependent on air intake amount other than air temperature or humidity conditions. It was also found that the heat exchanger with in-plane non-uniformity had more severe degradation of heat transfer rate, although it might relieve the overall air side pressure drop.

Global scaling of precipitation extremes using near-surface air temperature and dew point temperature

Global warming has altered the energy budget and water cycle processes of the land–atmosphere system, which has resulted in significant effects on precipitation extremes. Previous studies have identified a hook structure between near-surface temperature and precipitation extremes, in which extremes increase with temperature rises and decline thereafter. However, the underlying physical mechanisms of this association remain poorly understood. In this study, global-scale responses of precipitation extremes to near-surface air temperature (SAT) and dew point temperature (DPT) were quantified using the ERA5 reanalysis dataset. The results reveal a hook structure between precipitation extremes scaling and temperature, for both SAT and DPT, over many regions worldwide. The peak point temperature (T pp) ranges from 15 °C to 25 °C, increasing as latitude decreased. The association of precipitation extremes with SAT is negative in many areas in the tropics, whereas that with DPT is almost always positive; this suggests that moisture supply is the main factor limiting precipitation at higher surface temperatures. The hook structure and scaling rates incompatible with Clausius–Clapeyron scaling are associated with various factors including precipitation duration, total column water vapour, convective available potential energy, and relative humidity.

Estimation of downwelling surface longwave radiation for all-weather skies from FengYun-4A geostationary satellite data

ABSTRACT Fengyun-4A (FY-4A) is the latest generation of China’s geostationary satellite. The Advanced Geosynchronous Radiation Imager (AGRI) onboard FY-4A can provide high-precision, high-frequency observation data, which makes a new possibility for estimating the downwelling surface longwave radiation (DSLR) with high spatial and temporal resolution. This work presents a new method for estimating DSLRs under all-sky conditions using a genetic algorithm–artificial neural network (GA-ANN) algorithm based on brightness temperature (BT) from the FY-4A AGRI infrared channels and near-surface air temperature and dew point temperature from ERA5 reanalysis data. Based on the verification results of two independent observation sites, it is shown that the bias and RMSE are - 4.31 W/m2 and 35.28 W/m2, respectively. Compared the CERES SYN all-sky DSLR product with the DSLR estimated by the new method, the bias and RMSE are 0.86 W/m2 and 26.87 W/m2, respectively, and the new method has a higher spatial resolution (4 km), which can display more details of spatial variation.

Unfolding unique features of precipitation-temperature scaling across India

Warming of the climate is now unequivocal. However, probable dynamics of changes in the precipitation regimes remains disputable under potential future warming. Theoretically, increasing atmospheric heat translates to increase the atmospheric moisture holding capacity at the Clausius-Clapeyron (C-C) scaling rate in the absence of moisture limitation, which in turn raises the possibilities of more intense precipitation events. Here we investigated the scaling relationship between observed and reanalysis precipitation with surface air temperature and dew point temperature at daily and sub-daily time scales on annual and season-wise basis. To reach a robust conclusion, entire Indian mainland is considered as the study area that spans across a vast climatic characteristics. More influential temperature dependence is found for heavier precipitation events than the lighter ones on annual basis as well as for different seasons. Negative and peak type scaling patterns between the daily surface air temperature (SAT) and precipitation is noticed across the different climatic regimes of the study region. In contrast, positive dependence between dew point temperature (DPT) and precipitation is obtained to be dominant across the study region. Negative sensitivity noticed for the daily surface temperature is mainly the consequence of the local cooling effect due to the occurrence of precipitation and the moisture limitation at higher end of the local temperature. In general, the scaling relation varies from season to season, which tend towards a negative scaling in hotter seasons, but a positive scaling in colder seasons. Extreme precipitation can be better portrayed by sub-daily temperature conditions prior to the start of the storm events. Scaling relationship obtained between the sub-daily precipitation and surface air and dew point temperature exhibits greater consistency in the relationship across the regions. The Western Ghats region is consistently noticed to exhibit a positive scaling relation, whereas rest of the country exhibits mostly a peak type relation. The SAT considered up to 6-h prior to the precipitation might not reflect the actual response of precipitation towards the temperature increase, possibly due to the prevailing cloudy conditions preceding the precipitation event. Findings of this study provides a better understanding related to the anticipated changes in precipitation regimes under future climate with a warming condition across different climatic regimes.

Dairy Cow Thermal Balance Model During Heat Stress: Part 2. Model Assessment

Highlights The thermal balance model body temperature and respiration rate results compared well with published data. Model results were commonly within one standard deviation of reported averages. Research that measures more model inputs, coefficients, and results is needed. The thermal balance model can be used to identify heat stress factors and assess mitigation practices. Abstract. A steady-state process-based lactating cow thermal balance spreadsheet model developed by Nelson and Janni (in press) was compared to mean measured body temperatures, respiration rates, and skin temperatures from two published studies (Gebremedhin et al., 2010; Chen et al., 2015). Model body temperatures were also compared with reticular temperatures from cows standing in unshaded paddocks that were part of a solar shade study (Sharpe et al., 2021). Gebremedhin et al. (2010) reported measured mean rectal temperatures, 39.4 ± 0.5 C and 40.6 ± 0.4 C for hot and dry conditions with and without a solar load; model body temperatures for similar hot and dry conditions were 39.7 C and 40.6 C with and without a solar load, respectively. Model respiration rates were within one standard deviation of measured mean respiration rates (Gebremedhin et al., 2010). The model body temperature for a baseline condition was 39.1°C, which was within 0.1°C of the mean baseline temperature of 39.2 ± 0.6°C (Chen et al., 2015). The model respiration rate was 63 breaths per minute (bpm); much lower than the reported baseline respiration rate of 88 bpm (Chen et al., 2015). Model body temperatures were 0.1°C to 0.7°C lower than the measured mean reticular temperatures of standing cows in non-shaded paddocks with solar loads when ambient temperatures ranged from 24.4°C to 26.5°C. Model results compared well with mean measured parameters from three studies. The model can be used to assess the impact of factors affecting heat exchange (e.g., body mass, milk yield, solar load, air dry-bulb temperature, dew-point temperature, and air velocity) on heat exchange flux, cow respiration rate, and body temperature. Keywords: Body temperature, Dairy, Heat stress, Lactating cow, Respiration rate, Thermal balance model.

Feasibility of condensate water recycle of split air handling unit in Chinese University buildings

Recycling the condensate water of the air conditioner could be explored as an alternative water source to contribute to building the green campus. This paper explored the condensate water production through actual measurement based on a split air handling unit (SAHU) in a university building. Then, the statistical analysis was used to analyze the recycling feasibility and the impact factors of the condensate water production in 31 Chinese provincial capital cities to obtain the recycling potential map of the condensate water generated from a SAHU. Results showed that: (1) In the measurement, the amount of condensate water produced by a single split air conditioner was 1.6 kg from 12:40 to 13:40. Therefore, the daily output of condensate water of the air conditioner with the university operation schedule could reach 52.99 kg during the main air-conditioning season. (2) Among the 31 provincial capital cities in China, the largest condensate water outputs could be found in the Hot Summer and Warm Winter zone and the Hot Summer and Cold Winter zone, with an average monthly output of 1600 kg and 1100 kg, respectively. (3) Regression analysis showed that the dry-bulb temperature and dew point temperature of outdoor air had the highest positive and significant influence on condensate water production. The objective of this study is to provide theoretical guidance for the design and energy conservation evaluation of the feasibility of SAHU condensate water recycling in universities.

A novel air-conditioning system with cascading desiccant wheel and liquid desiccant dehumidifier for low-humidity industrial environments

Numerous industrial workshops require low-humidity environments, including lithium battery production, chip fabrication, some pharmaceutical manufacturing, etc. The desiccant wheel system is the conventional air-conditioning system for maintaining the low-humidity environments. However, only high-humidity outdoor air can be used for desiccant regeneration in some mechanical workshops requiring no mechanical ventilation, which leads to a high regeneration temperature and a poor system energy performance. To enhance the performance, a novel air-conditioning system with cascading desiccant wheel and liquid desiccant dehumidifier for low-humidity industrial workshops is proposed in this study. Solid desiccant and liquid desiccant are innovatively integrated in the proposed system for achieving a supply air dew point temperature below −10 °C. When dehumidifying process air from 5 to 1 g/kg in the basic case, the proposed system shows a competitive COP of 1.6. Under weather conditions in Guangzhou, subtropical China, the proposed system consumes 19.6 % and 58.0 % lower electricity during the summer, compared to the integrated system using outdoor air for regeneration and the desiccant wheel based system, respectively. The proposed system particularly applies to low-humidity industrial workshops in tropical and subtropical regions in summer, where there is a huge air humidity difference between indoors and outdoors. It is anticipated that this study could provide reference for energy saving and carbon emission reduction in low-humidity industrial production process.

Dynamic prediction of the pre-dehumidification of a radiant floor cooling and displacement ventilation system based on computational fluid dynamics and a back-propagation neural network: A case study of an office room

This study was carried out to solve the problem of condensation in radiant floor cooling systems. Computational fluid dynamics simulation and back-propagation neural network prediction were employed to conduct thorough research to predict the effects of the displacement ventilation dehumidification phase in an office building located in Jinan, China. The effects of the air supply temperature ( T as), air supply flow rate ( V as), air supply humidity ratio ( H as), floor temperature ( T floor), initial indoor temperature ( T ini) and relative humidity ( H ini) on the duration and energy consumption of pre-dehumidification were investigated. The big data show the air dew point temperature ( T ad) produced the most significant effect on the pre-dehumidification duration and energy consumption, while T as would cause the least significant effect. With the decrease of T ad, the pre-dehumidification duration and energy consumption were, respectively, decreased by 59.1% and 44.2%. Furthermore, with the variation of V as, the energy consumption exhibited a fluctuating trend. This study provides a novel and effective method to assess the pre-dehumidification control of radiant floor surfaces by considering different initial indoor conditions and air supply parameters.