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Optimization of Compositional and Technological Parameters for Phosphate Graphite Sand

机译：磷酸盐石墨砂成分和工艺参数的优化

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摘要

In present work, the compression strength and tensile strength of phosphate graphite sand with compositional and technological parameters (phosphoric acid, Al2O3, drying temperature, and drying time) were experimentally investigated. An L9 (34) orthogonal array was employed to analyze the effect of these four parameters on the compression strength and tensile strength, respectively. In addition, the radial basis function artificial neural network (RBFANN) was used to establish the models for compression strength and tensile strength, respectively. Moreover, the simulation and prediction results by the RBFANN and linear and non-linear regressions are compared. The results are as follows: the optimum scheme for phosphate graphite sand designed by us is phosphoric acid 24%, Al2O3 30%, drying temperature 400 °C, and drying time 60 min. The ascending sequence of the effect of four factors on both compression strength and tensile strength of phosphate graphite sand is drying time, drying temperature, Al2O3, and phosphoric acid. In addition, the prediction and simulation results show that RBFANN outperforms Taguchi approach for modeling.
机译：在目前的工作中，实验研究了磷酸石墨砂的抗压强度和拉伸强度，其组成和工艺参数（磷酸，Al2 O3 ，干燥温度和干燥时间）均得到了研究。利用L9 （34 ）正交阵列分别分析了这四个参数对抗压强度和抗拉强度的影响。另外，使用径向基函数人工神经网络（RBFANN）分别建立抗压强度和抗拉强度模型。此外，比较了RBFANN的模拟和预测结果以及线性和非线性回归。结果表明：我们设计的磷酸石墨砂的最佳方案是磷酸24％，Al2 O3 30％，干燥温度400°C，干燥时间60 min。影响磷酸盐石墨砂抗压强度和抗拉强度的四个因素的升序依次为干燥时间，干燥温度，Al2 O3 和磷酸。此外，预测和仿真结果表明，RBFANN在建模方面优于Taguchi方法。

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    An-hui Cai; Hua Chen; Jing-ying Tan; Min Chen; Xiao-song Li; Yong Zhou; Wei-ke An;

  • 作者单位

    Department of Mechanical and Electrical Engineering Hunan Institute of Science and Technology YueYang 414000 P.R. China;

    Department of Mechanical and Electrical Engineering Hunan Institute of Science and Technology YueYang 414000 P.R. China;

    Department of Mechanical and Electrical Engineering Hunan Institute of Science and Technology YueYang 414000 P.R. China;

    Department of Physical and Electronic Engineering Hunan Institute of Science and Technology YueYang 414000 P.R. China;

    Department of Mechanical and Electrical Engineering Hunan Institute of Science and Technology YueYang 414000 P.R. China;

    Department of Mechanical and Electrical Engineering Hunan Institute of Science and Technology YueYang 414000 P.R. China;

    Department of Mechanical and Electrical Engineering Hunan Institute of Science and Technology YueYang 414000 P.R. China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    artificial neural network; optimization; orthogonal design; phosphate graphite sand;

    机译：人工神经网络;优化;正交设计;磷酸盐石墨砂;

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