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Prediction Model for Optimizing Preparation of SiO2-based Phase Change and Humidity Storage Composites with Uniform Design and Back-propagation Neural Network

  • ZHANG Hao GU Heng-xing HUANG Xin-jie LIU Xiu-yu
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  • School of Civil Engineering and Architecture, Anhui University of Technology MCC Baosteel Technical Service Center Co. Ltd School of Civil Engineering and Architecture, Anhui University of Technology School of Civil Engineering and Architecture, Anhui University of Technology

Received date: 2015-05-07

  Revised date: 2015-06-01

  Online published: 2015-08-20

Abstract

With SiO2 as carrier, fatty acid as phase change material, SiO2-based phase change and humidity storage composites were prepared. The scheme was optimized by uniform design in a combination with BP neural network to optimize preparation of SiO2-based phase change and humidity storage composites. The performance of optimal SiO2-based composites were characterized. The results show that the optimal parameters are solution pH value 3.63, ultrasonic wave power 100 W, molar ratio of deionized water to tetraethyl orthosilicate 9.71, molar ratio of absolute alcohol to tetraethyl orthosilicate 5.18 and molar ratio of fatty acid to tetraethyl orthosilicate 0.51. The optimal equilibrium moisture content under the relative humidity of 97.30% is 0.3057 g/g, cooling time from 30℃ down to 15℃ is 1445 s, and overall performance of phase change and humidity storage is 1.6014. The experimental results and the model prediction are in good agreement (relative error is -1.70%~1.89%). The optimal SiO2-based phase change and humidity storage composites form with fatty acid coated on SiO2 network pore structure, and have the particle size distribution at about 100 nm. The above mentioned results verify the analysis with quadratic regression equation on the results obtained in uniform experimental design.

Cite this article

ZHANG Hao GU Heng-xing HUANG Xin-jie LIU Xiu-yu . Prediction Model for Optimizing Preparation of SiO2-based Phase Change and Humidity Storage Composites with Uniform Design and Back-propagation Neural Network[J]. The Chinese Journal of Process Engineering, 2015 , 15(4) : 548 -554 . DOI: 10.12034/j.issn.1009-606X.215206

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