We investigated the effect of supply air rate and temperature on formaldehyde emission characteristics in an environment chamber.A three-dimensional computational fluid dynamics(CFD) chamber model for simulating forma...We investigated the effect of supply air rate and temperature on formaldehyde emission characteristics in an environment chamber.A three-dimensional computational fluid dynamics(CFD) chamber model for simulating formaldehyde emission in twelve different cases was developed for obtaining formaldehyde concentration by the area-weighted average method.Laboratory experiments were conducted in an environment chamber to validate the simulation results of twelve different cases and the formaldehyde concentration was measured by continuous sampling.The results show that there was good agreement between the model prediction and the experimental values within 4.3 difference for each case.The CFD simulation results varied in the range from 0.21 mg/m3 to 0.94 mg/m3,and the measuring results in the range from 0.17 mg/m3 to 0.87 mg/m3.The variation trend of formaldehyde concentration with supply air rate and temperature variation for CFD simulation and experiment measuring was consistent.With the existence of steady formaldehyde emission sources,formaldehyde concentration generally increased with the increase of temperature,and it decreased with the increase of air supply rate.We also provided some reasonable suggestions to reduce formaldehyde concentration and to improve indoor air quality for newly decorated rooms.展开更多
Catalytic oxidation of formaldehyde (HCHO) is the most efficient way to purify indoor air of HCHO pollutant. This work investigated rare earth La‐doped Pt/TiO2 for low concentration HCHO oxidation at room temperature...Catalytic oxidation of formaldehyde (HCHO) is the most efficient way to purify indoor air of HCHO pollutant. This work investigated rare earth La‐doped Pt/TiO2 for low concentration HCHO oxidation at room temperature. La‐doped Pt/TiO2 had a dramatically promoted catalytic performance for HCHO oxidation. The reasons for the La promotion effect were investigated by N2 adsorption, X‐raydiffraction, CO chemisorption, X‐ray photoelectron spectroscopy, transmission electron microscopy(TEM) and high‐angle annular dark field scanning TEM. The Pt nanoparticle size was reduced to 1.7nm from 2.2 nm after modification by La, which led to higher Pt dispersion, more exposed activesites and enhanced metal‐support interaction. Thus a superior activity for indoor low concentrationHCHO oxidation was obtained. Moreover, the La‐doped TiO2 can be wash‐coated on a cordieritemonolith so that very low amounts of Pt (0.01 wt%) can be used. The catalyst was evaluated in asimulated indoor HCHO elimination environment and displayed high purifying efficiency and stability.It can be potentially used as a commercial catalyst for indoor HCHO elimination.展开更多
Indoor air pollution resulting from volatile organic compounds(VOCs),especially formaldehyde,is a significant health concern needed to predict indoor formaldehyde concentration(Cf)in green intelligent building design....Indoor air pollution resulting from volatile organic compounds(VOCs),especially formaldehyde,is a significant health concern needed to predict indoor formaldehyde concentration(Cf)in green intelligent building design.This study develops a thermal and wet coupling calculation model of porous fabric to account for the migration of formaldehyde molecules in indoor air and cotton,silk,and polyester fabric with heat flux in Harbin,Beijing,Xi’an,Shanghai,Guangzhou,and Kunming,China.The time-by-time indoor dry-bulb temperature(T),relative humidity(RH),and Cf,obtained from verified simulations,were collated and used as input data for the long short-term memory(LSTM)of the deep learning model that predicts indoor multivariate time series Cf from the secondary source effects of indoor fabrics(adsorption and release of formaldehyde).The trained LSTM model can be used to predict multivariate time series Cf at other emission times and locations.The LSTM-based model also predicted Cf with mean absolute percentage error(MAPE),symmetric mean absolute percentage error(SMAPE),mean absolute error(MAE),mean square error(MSE),and root mean square error(RMSE)that fell within 10%,10%,0.5,0.5,and 0.8,respectively.In addition,the characteristics of the input dataset,model parameters,the prediction accuracy of different indoor fabrics,and the uncertainty of the data set are analyzed.The results show that the prediction accuracy of single data set input is higher than that of temperature and humidity input,and the prediction accuracy of LSTM is better than recurrent neural network(RNN).The method’s feasibility was established,and the study provides theoretical support for guiding indoor air pollution control measures and ensuring human health and safety.展开更多
基金Funded by National Science Foundation(No.50778415 and No.50878177)
文摘We investigated the effect of supply air rate and temperature on formaldehyde emission characteristics in an environment chamber.A three-dimensional computational fluid dynamics(CFD) chamber model for simulating formaldehyde emission in twelve different cases was developed for obtaining formaldehyde concentration by the area-weighted average method.Laboratory experiments were conducted in an environment chamber to validate the simulation results of twelve different cases and the formaldehyde concentration was measured by continuous sampling.The results show that there was good agreement between the model prediction and the experimental values within 4.3 difference for each case.The CFD simulation results varied in the range from 0.21 mg/m3 to 0.94 mg/m3,and the measuring results in the range from 0.17 mg/m3 to 0.87 mg/m3.The variation trend of formaldehyde concentration with supply air rate and temperature variation for CFD simulation and experiment measuring was consistent.With the existence of steady formaldehyde emission sources,formaldehyde concentration generally increased with the increase of temperature,and it decreased with the increase of air supply rate.We also provided some reasonable suggestions to reduce formaldehyde concentration and to improve indoor air quality for newly decorated rooms.
基金supported by the National Key Research and Development Program (2016YFC0205900)the National Natural Science Foundation of China (21503106, 21567016)+1 种基金the Education Department of Jiangxi Province (KJLD14005)the Natural Science Foundation of Jiangxi Province (20142BAB213013 and 20151BBE50006)~~
文摘Catalytic oxidation of formaldehyde (HCHO) is the most efficient way to purify indoor air of HCHO pollutant. This work investigated rare earth La‐doped Pt/TiO2 for low concentration HCHO oxidation at room temperature. La‐doped Pt/TiO2 had a dramatically promoted catalytic performance for HCHO oxidation. The reasons for the La promotion effect were investigated by N2 adsorption, X‐raydiffraction, CO chemisorption, X‐ray photoelectron spectroscopy, transmission electron microscopy(TEM) and high‐angle annular dark field scanning TEM. The Pt nanoparticle size was reduced to 1.7nm from 2.2 nm after modification by La, which led to higher Pt dispersion, more exposed activesites and enhanced metal‐support interaction. Thus a superior activity for indoor low concentrationHCHO oxidation was obtained. Moreover, the La‐doped TiO2 can be wash‐coated on a cordieritemonolith so that very low amounts of Pt (0.01 wt%) can be used. The catalyst was evaluated in asimulated indoor HCHO elimination environment and displayed high purifying efficiency and stability.It can be potentially used as a commercial catalyst for indoor HCHO elimination.
基金This work was supported by the National Natural Science Foundation of China(52278129)the Key Scientific and Technological Innovation Team of Shaanxi Province(2023-CX-TD-29)Xiaohu Yang greatly acknowledged the support by the K.C.Wong Education Foundation.
文摘Indoor air pollution resulting from volatile organic compounds(VOCs),especially formaldehyde,is a significant health concern needed to predict indoor formaldehyde concentration(Cf)in green intelligent building design.This study develops a thermal and wet coupling calculation model of porous fabric to account for the migration of formaldehyde molecules in indoor air and cotton,silk,and polyester fabric with heat flux in Harbin,Beijing,Xi’an,Shanghai,Guangzhou,and Kunming,China.The time-by-time indoor dry-bulb temperature(T),relative humidity(RH),and Cf,obtained from verified simulations,were collated and used as input data for the long short-term memory(LSTM)of the deep learning model that predicts indoor multivariate time series Cf from the secondary source effects of indoor fabrics(adsorption and release of formaldehyde).The trained LSTM model can be used to predict multivariate time series Cf at other emission times and locations.The LSTM-based model also predicted Cf with mean absolute percentage error(MAPE),symmetric mean absolute percentage error(SMAPE),mean absolute error(MAE),mean square error(MSE),and root mean square error(RMSE)that fell within 10%,10%,0.5,0.5,and 0.8,respectively.In addition,the characteristics of the input dataset,model parameters,the prediction accuracy of different indoor fabrics,and the uncertainty of the data set are analyzed.The results show that the prediction accuracy of single data set input is higher than that of temperature and humidity input,and the prediction accuracy of LSTM is better than recurrent neural network(RNN).The method’s feasibility was established,and the study provides theoretical support for guiding indoor air pollution control measures and ensuring human health and safety.
