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Hui Huang
Kunming Metallurgy College

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Article
Published: 27 November 2019 in Journal of Thermal Analysis and Calorimetry
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Accurately predicting the thermal conductivity of nanofluids under various thermodynamic conditions is of great importance to promote the industrial application of nanofluids. Unfortunately, the accuracy and applicability of the current theoretical or empirical models cannot meet the demand, due to the inherent complex of nanofluids. In this study, an intelligent model, named radial basis function artificial neural network (RBF–ANNs), is developed to predict the thermal conductivity of nanofluids under various conditions. Five parameters including nanoparticle volume concentration, temperature, nanoparticle diameter, thermal conductivity of nanoparticle and thermal conductivity of base fluid are selected as the input variables. A total of 1444 experimental data samples are collected to optimize the structure of model. The RBF model is compared with six theoretical models and three intelligent models through statistical and graphical analyses. Also, trend analysis and sensitivity analysis are conducted to evaluate the influencing mechanism of nanoparticle concentration, temperature, nanoparticle size, thermal conductivities of base fluids and nanoparticle on the thermal conductivity of nanofluids. Meanwhile, the quality of the experimental data is evaluated by means of leverage algorithm. Results indicate the superiority of the RBF, especially when the data size is large. The overall correlation coefficient (R2), average absolute relative deviation (AARD%) and root-mean-squared error of the developed model are 0.9931, 2.715 and 0.0316, respectively. Among the five input parameters, the volume fraction of nanoparticles has the greatest impact on the thermal conductivity of nanofluid. The results of outlier detection demonstrate that the proposed RBF model and data samples are statistically valid.

ACS Style

Songyuan Zhang; Zhong Ge; Xingxiang Fan; Hui Huang; Xiaobo Long. Prediction method of thermal conductivity of nanofluids based on radial basis function. Journal of Thermal Analysis and Calorimetry 2019, 141, 859 -880.

AMA Style

Songyuan Zhang, Zhong Ge, Xingxiang Fan, Hui Huang, Xiaobo Long. Prediction method of thermal conductivity of nanofluids based on radial basis function. Journal of Thermal Analysis and Calorimetry. 2019; 141 (2):859-880.

Chicago/Turabian Style

Songyuan Zhang; Zhong Ge; Xingxiang Fan; Hui Huang; Xiaobo Long. 2019. "Prediction method of thermal conductivity of nanofluids based on radial basis function." Journal of Thermal Analysis and Calorimetry 141, no. 2: 859-880.

Journal article
Published: 14 March 2016 in Metals
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The recovery of vanadium from sulfuric and hydrofluoric mixed acid solutions generated by the direct leaching of black shale was investigated using solvent extraction and precipitation methods. The process consisted of reduction, solvent extraction, and stripping, followed by precipitation and calcination to yield vanadium pentoxide. The influence of various operating parameters on the extraction and recovery of vanadium was studied. Vanadium (IV) was selectively extracted using a mixture of 10% (v/v) di(2-ethylhexyl)phosphoric acid and 5% (v/v) tri-n-butylphosphate in sulfonated kerosene. Using six extraction and five stripping stages, the extraction efficiency for vanadium was 96.7% and the stripping efficiency was 99.7%. V2O5 with a purity of 99.52% was obtained by oxidation of the loaded strip solution and precipitation of ammonium polyvanadate at pH 1.8 to 2.2, followed by calcination of the dried precipitate at 550 °C for 2 h. It was concluded that the combination of solvent extraction and precipitation is an efficient method for the recovery of vanadium from a multi-element leach solution generated from black shale.

ACS Style

Xingbin Li; Chang Wei; Zhigan Deng; Cunxiong Li; Gang Fan; Minting Li; Hui Huang. Recovery of Vanadium from H2SO4-HF Acidic Leaching Solution of Black Shale by Solvent Extraction and Precipitation. Metals 2016, 6, 63 .

AMA Style

Xingbin Li, Chang Wei, Zhigan Deng, Cunxiong Li, Gang Fan, Minting Li, Hui Huang. Recovery of Vanadium from H2SO4-HF Acidic Leaching Solution of Black Shale by Solvent Extraction and Precipitation. Metals. 2016; 6 (3):63.

Chicago/Turabian Style

Xingbin Li; Chang Wei; Zhigan Deng; Cunxiong Li; Gang Fan; Minting Li; Hui Huang. 2016. "Recovery of Vanadium from H2SO4-HF Acidic Leaching Solution of Black Shale by Solvent Extraction and Precipitation." Metals 6, no. 3: 63.