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Hao L.,On-Line Monitoring | Hao L.,Tianjin University of Science and Technology | Hao L.,Chiba University | Guan S.,Chiba University | And 4 more authors.
Applied Surface Science | Year: 2017

In this work, the process of mechanical coating followed by molten KNO3 treatment is given to prepare visible-light-responsive K+-doped TiO2. X-ray diffraction (XRD), scanning electron spectroscopy (SEM), Energy dispersive spectrometer (EDS) and X-ray photoelectron spectroscopy (XPS) were conducted to characterize these TiO2 coatings. The results showed that K+-doped anatase TiO2/Ti composite coatings formed after molten KNO3 treatment at elevated temperatures. Meanwhile, their photocatalytic degradation of methylene blue (MB) and the antibacterial activity against Escherichia coli (E. coli) was also studied. The visible-light-responsive photocatalytic activity of the coatings in MB degradation increased with increase of K+ ions when holding temperature was raised from 673 to 773 K. An excellent antibacterial activity of the K+-doped TiO2/Ti coatings against E. coli was also obtained even in absence of light. The antibacterial activity in dark should attribute to the release of K+ ions from the coatings. The photocatalytic activity under visible-light irradiation should result from the absorption spectrum extension due to the doping of K+ ions into the lattice of TiO2. © 2017 Elsevier B.V.


Hao L.,Tianjin University of Science and Technology | Hao L.,On-Line Monitoring | Guan S.,Chiba University | Lu Y.,Chiba University | And 3 more authors.
Surface and Coatings Technology | Year: 2016

Mechanical coating followed by thermal oxidation in the atmosphere was used to prepare TiO2/SnO2 composite coatings. The coatings were characterized by X-ray diffraction (XRD), scanning electronic spectroscopy (SEM), energy dispersive spectrometer (EDS), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM) and so on. Results show that continuous rutile TiO2 coatings decorated with discrete SnO2 particles were formed during thermal oxidation at the temperature range of 873-1073 K. Through the temperature range, a small quantity of SnO was formed. However, tiny amount of Ti2O3 was formed only when oxidation temperature was 1073 K. The surface topography evolution of rutile TiO2 crystals during the thermal oxidation and relevant mechanism was also examined. Equiaxed, rod, columnar and needle TiO2 crystals with nanoscale were formed at temperatures from 873-1073 K. The topography evolution indicates that the diffusion of oxygen species into the formed rutile TiO2 layer was predominant at 873 K. It favored the growth of equiaxed crystals in the tangent plane rather than radial direction of Al2O3 ball substrate at the early stage of the thermal oxidation. However, the diffusion of Ti cations became prevailing compared with oxygen species at 1073 K. The reaction of Ti and oxygen species occurred at the external surface of the formed TiO2 layer where rutile TiO2 needles were formed. During thermal oxidation at 973 K, both the Ti cations and oxygen species were involved in the diffusion process and clusters of paired/parallel rutile TiO2 nanorods were formed. The addition of metallic Sn remarkably affected the surface topography and the grain size of rutile TiO2 crystals through regulating the diffusion of Ti cations and changing the local oxygen concentration surrounding Ti coatings. A proper additive amount refined the grains of rutile TiO2. © 2016 Elsevier B.V.


Tian W.,Tianjin University of Science and Technology | Tian W.,On-Line Monitoring | Yang S.,Tianjin University of Science and Technology | Li Z.,Tianjin University of Science and Technology | And 4 more authors.
Energy and Buildings | Year: 2016

Bayesian computation has received increasing attention in calibrating building energy models due to its flexibility and accuracy. However, there has been little research on how to determine informative energy data in Bayesian calibration in building energy models. Therefore, this study aims to determine and choose informative energy data using correlation analysis and hierarchical clustering method. A case study of retail building is used to demonstrate the proposed methods to infer four unknown input parameters using EnergyPlus program. The results indicate that the different combinations of energy data can provide various levels of accuracy in estimating unknown input variables in model calibration. This suggests that Bayesian computation is suitable for inferring the parameters when there are missing energy data that can be treated as uninformative output data. The proposed method can be also used to find the redundant information on energy data in order to improve computational efficiency in Bayesian calibration. © 2016 Elsevier B.V.


Wang J.,Tianjin University of Science and Technology | Wang J.,On-Line Monitoring
Recent Patents on Mechanical Engineering | Year: 2016

Background: In recent years, as microcellular plastics have been used in different economic and life fields, microcellular injection molding machine has been paid more and more attention and been developed into various structures. Objective: The purpose of this article is to provide an overview of the supercritical fluid injecting system, mixing, and nucleating equipment of the microcellular injection molding, and explain how the technologies influence the product's quality and application. Methods: The paper reviews various patents and research developments about supercritical fluid injecting system, mixing, and nucleating equipment of microcellular injection molding machine, and analyses their effects on cell diameter, weight reduction, and product appearance. Results: Current and future developments of the injection apparatus, nozzle, and mold apparatus of microcellular injection molding are finally provided to improve the microcellular injection molded product's quality. Conclusion: The considerable attention has been paid on the microcellular injection technology. The recent patents and technologies have provided help to apply microcellular injection molding in a wider range, however how to inject physical agent into resin melt, mix them, and form fine cell structure is still difficult. For further enhancement, more devices should be invented in order to solve or simplify the above difficulties. © 2016 Bentham Science Publishers.


Wei L.,Tianjin University of Science and Technology | Wei L.,On-Line Monitoring | Tian W.,Tianjin University of Science and Technology | Tian W.,On-Line Monitoring | And 5 more authors.
Procedia Engineering | Year: 2015

There has been an increasing interest in applying machine learning methods in urban energy assessment. This research implemented six statistical learning methods in estimating domestic gas and electricity using both physical and socio-economic explanatory variables in London. The input variables include dwelling types, household tenure, household composition, council tax band, population age groups, etc. Six machine learning methods are two linear approaches (full linear and Lasso) and four non-parametric methods (MARS multivariate adaptive regression spline, SVM support vector machine, bagging MARS, and boosting). The results indicate that all the four non-parametric models outperform two linear models. The SVM models perform the best among these models for both gas and electricity. The bagging MARS performs only a little worse than the SVM for gas use prediction. The Lasso model has similar predictive capability to the full linear model in this case. © 2015 The Authors. Published by Elsevier Ltd.


Gao X.,Tianjin University of Science and Technology | Wang J.,Tianjin University of Science and Technology | Wang S.,Tianjin University of Science and Technology | Li Z.,Tianjin University of Science and Technology | Li Z.,On-Line Monitoring
Drying Technology | Year: 2016

Modeling of particulate or thin-layer drying of materials is necessary to understand the fundamental transport mechanism and a prerequisite to successfully simulate or scale up the whole process for optimization or control of the operating conditions. Simple models with a reasonable physical meaning are effective for engineering purposes. Thin-layer drying of green peas was carried out in a fluidized bed with a newly developed slotted gas distributor. Based on the reaction engineering approach, a drying model of green peas was well established, in which relative activation energy (ΔEv/ΔEv,b) was correlated with reduced moisture content (X − Xb) at a drying air temperature of 80°C. The drying kinetics of green peas was discussed in terms of activation energy. In addition, activation energy based on a simplified material surface temperature profile was recalculated to evaluate the temperature sensitivity to the model establishment. © 2016, Copyright © Taylor & Francis Group, LLC.


Yang S.,Tianjin University of Science and Technology | Tian W.,Tianjin University of Science and Technology | Tian W.,On-Line Monitoring | Cubi E.,University of Calgary | And 3 more authors.
Procedia Engineering | Year: 2016

Sensitivity analysis is an important tool in building energy assessment to determine the key factors influencing energy use or carbon emissions for buildings. This research is focused on comparing the characteristics of four global sensitivity analysis: SRC (standardized regression coefficient), Morris design, extended FAST (Fourier Amplitude Sensitivity Test) and TGP (treed Gaussian process) method. A retail building located at Harbin (China) is used as a case study to demonstrate the advantages and drawbacks for these four methods. The results indicate that the TGP method (one of meta-modelling approaches) is the best choice in terms of both accuracy and computationally cost. Note that the TGP method needs more time to calculate the sensitivity index although it needs only moderate time for running building energy models. At least two fundamentally different methods for sensitivity analysis are recommended to be performed to provide more robust results in building energy assessment. © 2016 The Authors. Published by Elsevier Ltd.


Li Z.,Tianjin University of Science and Technology | Li Z.,On-Line Monitoring | Gao X.,Tianjin University of Science and Technology | Wu L.,Nagoya University | And 2 more authors.
Journal of Porous Materials | Year: 2016

Low-cost activated carbons were prepared from poplar wood by chemical activation with KOH as a chemical activating agent. The thermal behavior of KOH-impregnated poplar wood was analyzed in detail via the Thermogravimetric analysis (TG, DTG and DSC). Moreover, the effects of impregnation ratio, activation temperature and activation time on the porous structure were investigated. Results showed that KOH has catalytic effect which could promote the pyrolysis of poplar wood at lower activation temperature. The activated carbons from poplar wood with KOH activation could be formed at 550–700 °C. The specific surface area and pore volume of activated carbon increased with increasing the impregnation ratio, activation temperature and activation time. Moreover, influence of increasing impregnation ratio on the porosity of activated carbon is sensitive to activation time. The maximum specific surface area (1551 m2/g) activated carbon with uniform micropores distribution was obtained at activation temperature of 700 °C, impregnation ratio of 3/1 and activation time of 30 min. Carbon skeleton could break seriously when activation temperature is higher than 750 °C. © 2016 Springer Science+Business Media New York


Tian W.,Tianjin University of Science and Technology | Tian W.,On-Line Monitoring | Choudhary R.,University of Cambridge | Augenbroe G.,Georgia Institute of Technology | Lee S.H.,Lawrence Berkeley National Laboratory
Building and Environment | Year: 2015

Statistical energy modelling & analysis of building stock is becoming mainstream in the context of city or district scale analysis of energy saving measures. A common aspect in such analyses is that there is generally a set of key explanatory variables - or the main inputs - that are statistically related to a quantity of interest (end-use energy or CO2). In the context of energy use in buildings, it is not uncommon that the explanatory variables may be correlated. However, there has been little discussion about the correlated variables in building stock research. This paper uses a set of campus buildings as a demonstrative case study to investigate the application of variable importance and meta-model construction in the case of correlated inputs when quantifying energy demand of a building stock. The variable importance analysis can identify key factors that explain energy consumption of a building stock. To this end, it is necessary to apply methods suitable for correlated inputs because the observational data (inputs) of buildings are usually correlated. For constructing statistical energy meta-models, two types of regression models are used: linear and non-parametric models. The results indicate that the linear models perform well compared to the complicated non-parametric models in this case. In addition, a simple transformation of the response, commonly used in linear regression, can improve predictive performance of both the linear and non-parametric models. © 2015 Elsevier Ltd.


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