Calcium silicate units are versatile and widely used construction materials for edifices. Their production process involves several factors that concern either the mixture of the raw materials or the curing process. The understanding of how raw materials and process variables interact in achieving the compressive strength of the final product enables a cost‐ and energy‐efficient layout of the production process. In this paper, we use mixture‐process experiments to derive a prediction model for compressive strength. We compare computer‐generated D‐optimal designs with different numbers of center points by various criteria and by their prediction variance throughout the design space. In contrast to traditional mixture designs, these designs take additional constraints on the mixture components into account and can include process variables. We review suitable response‐surface models, which combine mixture and process variables. Based on results from 72 experimental runs, a model for the mean compressive strength is built, combining expert knowledge with statistical model‐selection strategies. The resulting model covers not only linear effects of mixture components and process variables but also interactions and quadratic terms. For example, the influence of the lime share on compressive strength differs among the use of various sand mixtures. For desired values of predicted compressive strength, factor settings can thereby be found reducing costs and energy emission.

中文翻译：

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使用最佳混合物工艺设计和响应表面模型研究硅酸钙单元的性能

硅酸钙单元是通用的，广泛用于建筑物的建筑材料。他们的生产过程涉及几个因素，这些因素涉及原料的混合或固化过程。对原材料和过程变量如何相互作用以实现最终产品的抗压强度的了解，使生产过程的成本和能源效率得以实现。在本文中，我们使用混合过程实验来得出抗压强度的预测模型。我们通过各种标准及其在整个设计空间中的预测差异，比较具有不同中心点数量的计算机生成的D最优设计。与传统的混合物设计相比，这些设计考虑了对混合物成分的其他限制，并且可以包括过程变量。我们回顾了将混合物和过程变量结合起来的合适的响应面模型。根据72个实验的结果，将专家知识与统计模型选择策略结合起来，建立了平均抗压强度模型。生成的模型不仅涵盖混合物成分和过程变量的线性效应，还涉及相互作用和二次项。例如，石灰份额对抗压强度的影响在使用各种沙子混合物时会有所不同。对于预期的抗压强度的期望值，可以找到因数设置，从而降低了成本和能耗。将专家知识与统计模型选择策略相结合。生成的模型不仅涵盖混合物成分和过程变量的线性效应，还涉及相互作用和二次项。例如，石灰份额对抗压强度的影响在使用各种沙子混合物时会有所不同。对于期望的抗压强度的期望值，由此可以找到因子设置，从而降低了成本和能量排放。将专家知识与统计模型选择策略相结合。生成的模型不仅涵盖混合物成分和过程变量的线性效应，还涉及相互作用和二次项。例如，石灰份额对抗压强度的影响在使用各种沙子混合物时会有所不同。对于预期的抗压强度的期望值，可以找到因数设置，从而降低了成本和能耗。