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首页> 外文期刊>South african journal of chemical engineering >Modeling and optimization of galena dissolution in a binary solution of nitric acid and ferric chloride using artificial neural network coupled with genetic algorithm and response surface methodology
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Modeling and optimization of galena dissolution in a binary solution of nitric acid and ferric chloride using artificial neural network coupled with genetic algorithm and response surface methodology

机译：人工神经网络与遗传算法和响应面法耦合的硝酸和氯化铁二元溶液中Galena溶解的建模与优化

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In this research, the optimization of lead recovery from galena with a binary solution of nitric acid and ferric chloride using response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) was carried out. The galena mineral was examined for mineralogical properties with X-ray diffraction spectroscopic system while the elemental composition was analyzed with X-ray fluorescence (XRF). The results revealed that the galena mineral exists as lead sulphide (PbS). The central composite design was employed for RSM modeling while back propagation (BP) coupled with the Levenberg-Marquardt (LM) algorithm was used to construct a feed-forward neural network (FFNN). The leaching temperature, acid concentration, stirring rate, leaching time and ferric chloride concentration were chosen as input factors, while the percentage yield of lead was the response. The multilayer perceptron with design of 5-9-1 gave the best performance. Comparison of the RSM and ANN model indicated satisfactory prediction of the response, with AAD of 0.887% and 0.377%, R2 of 0.9910 and 0.9989, and RMSE of 0.815 and 0.290, respectively. The process parameters were optimized via GA and RSM optimization tools. An optimum lead yield of 90.33% was obtained with a leaching temperature of 80.2°C, HNO3 concentration of 3.55 M, stirring rate of 498.88 rpm, leaching time of 86.91 minutes and ferric chloride concentration of 0.35 M using RSM while a yield of 87.11% was achieved via GA.
机译：在本研究中，进行了使用响应表面方法（RSM），人工神经网络（ANN）和遗传算法（GA）的耐硝酸和氯化物二进制溶液的Galena优化Galena的优化。在用X射线荧光（XRF）分析元素组合物的同时，检查了矿物学性质的矿物学性质的矿物学性质。结果表明，伽利热纳矿物存在于硫化铅（PBS）中。中央复合设计用于RSM建模，同时使用Levenberg-Marquardt（LM）算法耦合的回波（BP）来构建前馈神经网络（FFNN）。选择浸出温度，酸浓度，搅拌速率，浸出时间和氯化铁浓度作为输入因子，而铅的百分比产量是响应。设计为5-9-1的多层的感知，给出了最佳性能。 RSM和ANN模型的比较表明对响应的令人满意的预测，AAD分别为0.887％和0.377％，R2为0.9910和0.9989，以及0.815和0.290的RMSE。通过GA和RSM优化工具优化了过程参数。获得最佳铅产率90.33％，浸出温度为80.2℃，HNO3浓度为3.55米，搅拌速率为498.88rpm，浸出时间为86.91分钟，氯化铁浓度为0.35μm，使用RSM为87.11％通过ga实现。

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