Abstract Currently, traditional wet chemistry methods are used for quality control of bauxites. Such methods indirectly quantify the gibbsite and kaolinite content as available alumina (AvAl2O3) and reactive silica (RxSiO2), respectively, and it is very costly and time-consuming. In order to achieve a rapid and reliable method to estimate these parameters, as alternative to current wet chemistry methods, this study, evaluates the use of multivariate statistics – Partial Least Square Regression (PLSR) on XRD data of gibbsitic bauxites (Miltonia 3 plateau). The method was optimized through Principal Component Analysis (PCA) and factorial Design of Experiments (DOE). The results demonstrated that such method has a potential to be industrially applied to quality control of bauxites. In comparison with traditional method, the same precision is achieved for the ore samples, but faster, easier to implement and carry out the analyses, demands less laboratory space and manpower, and no reagent is necessary. In addition, the use of XRD for quality control makes it possible to track the mineralogy of the bauxite that feeds the Bayer process, and therefore, be aware of how it could impact the process.

中文翻译：

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使用 XRD 数据的多变量统计快速控制三水铝土矿品位的建议

摘要 目前，铝土矿的质量控制主要采用传统的湿化学方法。此类方法分别将三水铝石和高岭石含量间接量化为可用氧化铝 (AvAl2O3) 和反应性二氧化硅 (RxSiO2)，并且非常昂贵且耗时。为了获得一种快速可靠的方法来估计这些参数，作为当前湿化学方法的替代方法，本研究评估了多变量统计数据的使用 - 偏最小二乘回归 (PLSR) 对三水铝石铝土矿（Miltonia 3 高原）的 XRD 数据的使用. 该方法通过主成分分析 (PCA) 和因子实验设计 (DOE) 进行了优化。结果表明，该方法具有在工业上应用于铝土矿质量控制的潜力。与传统方法相比，矿石样品可达到相同的精度，但更快、更容易实施和进行分析，需要更少的实验室空间和人力，并且不需要试剂。此外，使用 XRD 进行质量控制可以跟踪为拜耳工艺提供原料的铝土矿的矿物学，因此，了解它如何影响工艺。