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Factor space differentiation of brick clays according to mineral content: Prediction of final brick product quality

机译:砖粘土根据矿物含量的因子空间分异:最终砖产品质量的预测

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Chemical composition and XRD qualitative analysis were used to calculate mineral contents of 139 brick clay raw materials using LPNORM. The second order polynomial models (SOP) for all the samples, which express the relation between mineral contents and the characteristics of fired laboratory products, did not fit to experimental data satisfactorily, due to low coefficients of determination (r(2)). In order to improve the models, the samples are divided into four groups in factor space (four quadrants), according to their mineral content similarity, using principal component analysis (PCA). Predictive models of compressive strength (CS), water absorption (WA), firing shrinkage (FS), weight loss during firing (WLF) and volume mass of cubes (VMC) are obtained for each of the groups. Second order polynomial (SOP) models are developed, and the influence of certain minerals to brick clay bricks quality within the groups is discussed. Developed models were able to predict the final quality of products in a wide range of mineral content and temperature treatment data, showing coefficient of determination (r(2)) in range between 0.704-0.995. In order to estimate the adequacy of these models, the results were applied to the experimental data and compared according to additional statistical tests, so the next values are determined: coefficients of determination, reduced chi-square (chi(2)), mean bias error (MBE), mean percent error (MPE) and root mean square error (RMSE). (C) 2015 Elsevier B.V. All rights reserved.
机译:利用LPNORM,通过化学成分和XRD定性分析,计算出139块砖粘土原料的矿物质含量。由于测定系数低(r(2)),所有样品的二阶多项式模型(SOP)都表达了矿物质含量与燃烧的实验室产品的特征之间的关系,因此无法令人满意地适合实验数据。为了改进模型,使用主成分分析(PCA)根据矿物含量的相似性,将样本在因子空间中分为四个组(四个象限)。获得了每个组的抗压强度(CS),吸水率(WA),烧成收缩率(FS),烧成过程中失重(WLF)和立方体体积质量(VMC)的预测模型。建立了二阶多项式(SOP)模型,并讨论了某些矿物对组内砖粘土砖质量的影响。开发的模型能够在广泛的矿物含量和温度处理数据中预测产品的最终质量,显示出确定系数(r(2))在0.704-0.995之间。为了估计这些模型的充分性,将结果应用于实验数据并根据其他统计检验进行比较,因此确定了下一个值:确定系数,减少的卡方(chi(2)),平均偏差误差(MBE),平均误差百分比(MPE)和均方根误差(RMSE)。 (C)2015 Elsevier B.V.保留所有权利。

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