Process parametric optimization toward augmentation of silica yield using Taguchi technique and artificial neural network approach

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Process parametric optimization toward augmentation of silica yield using Taguchi technique and artificial neural network approach

机译：过程参数优化对使用田口增大硅产量技术和人工神经网络方法

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This study was attempted towards the retrieval of silica from rice husk ash to annihilate the local problems of disposal from the rice milling industries for enhancement of silica purity. Optimization of process factors using the Taguchi technique involved variation in sodium hydroxide concentration (NaOH), alkali impregnation volume per unit weight of the rice husk ash, and reaction time for designing the experimental matrix utilizing L16 orthogonal array at four different levels. The maximum silicaextraction was 98.26% obtained with 4 N of NaOH, 20 ml/g of alkali volume, and treatment time 60 min. The identical experimental data set was also applied to an artificial neural network model (ANN) with the LM algorithm for predicting the feasibility of the extraction process. Both Taguchi and neural networks suggested a high coefficient of determination and a satisfactory correlation between experimental and predicted silica recovery values. The detailed characterization of the synthesized silica powder and residual rice husk ash was executed using field emission scanning electron microscopy (energy-dispersive spectroscopy), Fourier transform infrared spectroscopy, thermogravimetric, Brunauer Emmett Tellet surface area, and particle size analysis. The simultaneous reuse of residual ash and silicate was performed to ensure the best possible reclamation of silica and reusability of rice husk ash. The detailed cost estimation of the synthesized silica powder further suggested the effectiveness of theoptimized process. Thus, a comprehensive approach for enhancement of the silica yield and purity by adopting Taguchi and ANN optimization proved to be useful in this study.

机译：本研究试图对检索从稻壳灰湮灭当地的二氧化硅问题处理的水稻铣削工业硅纯度的增强。使用田口方法优化过程的因素技术涉及氢氧化钠的变化体积浓度(氢氧化钠)、碱浸渍每单位重量的稻壳灰,设计实验的反应时间在四个矩阵利用L16正交数组不同的级别。获得4 N的98.26%氢氧化钠,20 ml / g的碱量,和治疗时间60分钟。相同的实验数据集也被应用一个人工神经网络模型(ANN)LM算法预测的可行性提取的过程。神经网络提出了一个高系数决心和令人满意的相关性实验和预测之间的硅恢复值。合成硅粉和剩余米饭使用场发射壳灰被处决扫描电子显微镜(能量色散红外光谱),傅里叶变换光谱、热重Brunauer艾美特Tellet表面积,和粒度分析。同时残余的火山灰和重用硅酸盐进行,以确保最好的可能的回收硅和可重用性稻壳灰。合成硅粉进一步建议优化过程的有效性。一个全面的增强方法采用田口和硅产量和纯度安优化被证明是有用的研究。

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《Energy, Ecology and Environment》 |2020年第4期|294-312|共19页
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RICE HUSK ASH; Silica fume; Particle size analysisSodium HydroxideLeast mean square algorithmfield emission scanning electron microscopyExtraction processespurityAshes;

机译：analysisSodium HydroxideLeast均方algorithmfield发射扫描电子microscopyExtraction processespurityAshes;

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Process parametric optimization toward augmentation of silica yield using Taguchi technique and artificial neural network approach

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