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
In the past few years, support vector machines (SVMs) have been applied to many fields, such as pattern recognition and data mining, etc. However there still exist some problems to be solved. One of them is that the S...In the past few years, support vector machines (SVMs) have been applied to many fields, such as pattern recognition and data mining, etc. However there still exist some problems to be solved. One of them is that the SVM is very sensitive to outliers or noises because of over-fitting problem. In this paper, a fuzzy support vector regression (FSVR) method is presented to deal with this problem. Strategies based on k nearest neighbor (kNN) and support vector data description (SVDD) are adopted to set the fuzzy membership values of data points in FSVR.The proposed FSVR soft sensor models based on kNN and SVDD are employed to predict the concentration of 4-carboxy-benzaldehyde (4-CBA) in purified terephthalic acid (PTA) oxidation process. Simulation results indicate that the proposed method indeed reduces the effect of outliers and yields higher accuracy.展开更多
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National Key Technologies Research and Development Program in the 10th five-year plan，国家杰出青年科学基金
文摘In the past few years, support vector machines (SVMs) have been applied to many fields, such as pattern recognition and data mining, etc. However there still exist some problems to be solved. One of them is that the SVM is very sensitive to outliers or noises because of over-fitting problem. In this paper, a fuzzy support vector regression (FSVR) method is presented to deal with this problem. Strategies based on k nearest neighbor (kNN) and support vector data description (SVDD) are adopted to set the fuzzy membership values of data points in FSVR.The proposed FSVR soft sensor models based on kNN and SVDD are employed to predict the concentration of 4-carboxy-benzaldehyde (4-CBA) in purified terephthalic acid (PTA) oxidation process. Simulation results indicate that the proposed method indeed reduces the effect of outliers and yields higher accuracy.
