Papers by Author: Qiang Luo

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Authors: Qiang Luo, Qing Li Ren
Abstract: The three-layer structure back-propagation network model based on the non-linear relationship between the break percentage elongation of the Mg,Al-hydrotalcite/PE nanocomposites and the technological factors was established. And in order to accelerate the converging rate and avoid the local minimum, dimensionality reduction and pre-whitening methods were used. Moreover, the optimum technological process parameters were optimized with genetic algorithm. And the results show that using both the back propagation neural networks and genetic algorithm is very efficient for the prediction of the break percentage elongation of the Mg,Al-hydrotalcite/PE nanocomposite.
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Authors: Qing Li Ren, Qiang Luo, Miao Miao Yang
Abstract: The korshunskite samples were prepared in precipitation by the one-step reaction method at atmospheric pressure. The three-layer structure back-propagation network model based on the non-linear relationship between the amount of the korshunskite whiskers and the technological factors, such as the adding amount of raw materials NaOH, MgCl2, MgO, and reaction temperature, is established. And the results show that the improved back propagation neural networks model is very efficient for predication of the korshunskite whiskers preparation.
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Authors: Qiang Luo, Qing Li Ren
Abstract: A three-layer structure back-propagation network model based on the non-linear relationship between the size of the CaCO3 nanocrystalline and the technological factors, such as reaction time, reaction temperature, raw material adding amount of NaCO3 and CaCl2, was established. Moreover, in order to accelerate the converging rate and avoid the non-converging situation, the momentum terms are introduced. Besides, the variable learning speed is adopted. At the same time, the input variables were pretreated by using the main component analysis firstly. And the results show that the improved back propagation neural networks model is very efficient for predication of the CaCO3 nanocrystalline size.
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Authors: Qiang Luo, Qing Li Ren
Abstract: A three-layer back-propagation neural network model based on the non-linear relationship between the size of the SrTiO3 nanocrystalline and the technology factors, such as reaction time, reaction temperature, raw material adding amount of NaOH and SrCl2, and the rate of TiCl4/Hl, was established. Moreover, in order to accelerate the converging rate and avoid the non-converging situation, the momentum terms are introduced. Besides, the variable learning speed is adopted. At the same time, the input variables were pretreated by using the main component analysis firstly. And the results show that the improved back-propagation neural network model is very efficient for predication of the SrTiO3 nanocrystalline size.
2497
Authors: Qing Li Ren, Qiang Luo, Feng Lei Wang
Abstract: Because of its superior surface properties, nanocalcium carbonate can be applied to the adsorption of heavy metals in wastewater. However, because of the easy aggregation of nanocalcium carbonate, high surface energy and poor dispersibility in water, it is not conducive to the process of adsorption. Therefore, surface modification of nanocalcium carbonate is needed. In this paper, nanocalcium carbonate was prepared by liquid phase method. And the nanocalcium carbonate was surface modified by sodium dodecyl sulfate. The effects of modifier amount, modification temperature, and modification time on the activation and absorbance of nanocalcium carbonate were investigated. And the morphology and particle size of modified nanocalcium carbonate were tested by SEM and XRD patterns.The results show that the dispersion and surface activity of the modified nanocalcium carbonate have been improved remarkably. Moreover, the Cu2+ was adsorbed by sodium dodecyl sulfate modified nanocalcium carbonate and unmodified nanocalcium carbonate under the optimum modification conditions. And the effects of nanocalcium carbonate initial concentration on the adsorption performance were studied. The results show that the adsorption performance of modified calcium carbonate is better than that of the unmodified. Moreover, the adsorption process is studied by adsorption isotherm. By drawing the adsorption isotherms lines and by comparing the fitting result of the experimental data based on the Langmuir model and that of Freundlich model, it is found that the adsorption of Cu2+ by modified nanocalcium carbonate meets the Langmuir model.
830
Authors: Qiang Luo, Qing Li Ren
Abstract: A prediction model for purity of the artificial synthetic hydrotalcite under varied process parameters based on improved artificial back-propagation (BP) neural networks is developed. And the non-linear relationship between the hydrotalcite purity and the raw material adding amount of NaOH, MgCl2 and AlCl3 was established based on BP learning algorithm analysis and convergence improvement. The hydrotalcite purity can be predicted by means of the trained neural net. Thus, by virtue of the prediction model, the future hydrotalcite purity can be evaluated under random complicated raw material amounts. Moreover, the best processing technology is optimized using the genetic algorithm.
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Authors: Qing Li Ren, Qiang Luo, Wei Chen
Abstract: The highly pure acicular nanosize Mg,Al-hydrotalcite was synthesized by the one-step liquid reaction method at atmospheric pressure. The favorable growth unit in the reaction liquid, the requirements of Al3+ entering the Mg2+-(OH-)6 octahedral and forming the growth unit M-(OH-1)6 (M=Mg2+, Al3+) and growth mechanism that the nanosize Mg,Al-hydrotalcite crystal embryo follows are studied according to the test results of XRD, TEM, SEM and IR. Later, that the nanosize Mg,Alhydrotalcite crystal embryo growth follows the gathering growth theory was deeply investigated.
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Authors: Qing Li Ren, Qiang Luo, Yan Hong Hou
Abstract: The optical properties of the Mg (OH)2 crystalline powder samples, which were prepared by us, were investigated by first-principles method. The calculated results show that the static state dielectric function ε1(0) for Mg (OH)2 is 2.8673. The peak value range for the Mg (OH)2 absorption coefficient is mainly in the energy range from 45.521 eV to 66.0213 eV. Moreover, absorption coefficient researches its maximum, which is 1490460cm-1, at the energy of 63.7988eV. Besides, when energy is greater than 66.3901eV, the reflectivity rate is one. And the average static state refractive rate n (0) for Mg (OH)2 is 1.6292. While the maximum peak of energy loss function for Mg (OH)2 is in 20.4755eV.
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