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首页> 外文会议>International Conference on Informatics in Control, Automation and Robotics >Genetic algorithm based X-ray diffraction analysis for chemical control of aluminium smelters baths
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Genetic algorithm based X-ray diffraction analysis for chemical control of aluminium smelters baths

机译：基于遗传算法的X射线衍射分析对铝冶炼厂熔池的化学控制

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Aluminium production is based on the high-temperature electrolysis of alumina in molten fluoride salts. Part of the fluoride compounds continuously evaporates, which violates the optimal composition of the electrolyte in the electrolytic baths. It causes a technological necessity for regular adjustment of the electrolyte composition by the addition of fluorides according to results of automatic express analysis of the electrolyte. Control of the main composition characteristics is performed automatically by XRD phase analysis of crystallized electrolyte samples. The XRD method, usually used on aluminium smelters, requires periodic calibration with reference samples, whose phase composition is exactly known. The preparation of such samples is a rather complicated problem because samples include 5–6 different phases with variable microcrystalline structure. An alternative diffraction method is the Rietveld method, which does not require reference samples to be used. The method is based on the modelling of the experimental powder patterns of electrolyte samples as the sum of the phase of component powder patterns, calculated from their atomic crystal structure. The simulation includes a refinement of the profile parameters and crystal structure of phases by the nonlinear least squares method (LSM). The problem with the automation of this approach is the need to install a set of initial values of the parameters that can and should be automatically refined by LSM to exact values. To solve this problem, the article proposed an optimization method based on an evolutionary choice of initial values of profile and structural parameters using a genetic algorithm. The criterion of the evolution is the minimization of the profile R-factor, which represents the weighted discrepancy between the experimental and model powder patterns of the electrolyte sample. It is shown that this approach provides the necessary accuracy and complete automation of the electrolyte compositi- n control.
机译：铝的生产基于熔融氟化盐中氧化铝的高温电解。一部分氟化物连续蒸发，这违反了电解槽中电解质的最佳组成。根据电解质的自动表达分析的结果，通过添加氟化物来定期调节电解质组成导致了技术上的必要性。通过结晶电解质样品的XRD相分析自动执行主要成分特征的控制。通常用于铝冶炼厂的XRD方法需要定期对参考样品进行校准，参考样品的相组成是已知的。这种样品的制备是一个相当复杂的问题，因为样品包括5–6个具有可变微晶结构的不同相。可选的衍射方法是Rietveld方法，该方法不需要使用参考样品。该方法基于电解质样品的实验粉末图案的建模，该模型是根据其原子晶体结构计算出的成分粉末图案的相位之和。模拟包括通过非线性最小二乘法（LSM）改进轮廓参数和相的晶体结构。这种方法自动化的问题是需要安装一组参数的初始值，这些初始值可以并且应该由LSM自动调整为精确值。为了解决这个问题，本文提出了一种基于遗传算法的轮廓和结构参数初始值的进化选择的优化方法。演变的标准是最小化轮廓R因子，它代表了电解质样品的实验粉末模式和模型粉末模式之间的加权差异。结果表明，这种方法提供了必要的精度和电解质复合物控制的完全自动化。

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