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Predicting Particle Fineness in a Cement Mill

机译：预测水泥厂的颗粒细度

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Cement production is a multi-billion dollar industry, of which one of the main sub-processes, cement milling, is complex and non-linear. There is a need to model the fineness of particles exiting the milling circuit to better control the cement plant. This paper explores the relationship between the particle size of cement produced and the operation of the cement mill circuit. This paper aims to provide a model for predicting the fineness of particles exiting the milling circuit using data on the current and past states of the plant. A comprehensive literature review of the problem, as well as a discussion of potential modelling solutions, is provided. Blaine (particle fineness)is modelled using many different linear and non-linear models on 5 months of data from a Chinese cement plant. On a holdout test set a multi-layered perceptron achieved an MAE of 8.799 and a linear regression achieved a R2 of 0.481. discussion of the significance of various features for predicting Blaine is also presented. The results show some limited success from non-linear data-driven models and highlight some of the unique difficulties in modelling the cement mill and present recommendations for future research.
机译：水泥生产是一个价值数十亿美元的行业，其中主要的子过程之一，水泥铣削是复杂且非线性的。需要对离开研磨回路的颗粒的细度进行建模，以更好地控制水泥厂。本文探讨了生产的水泥的粒度与水泥磨粉机回路的运行之间的关系。本文旨在提供一个模型，用于使用工厂的当前和过去状态数据来预测离开铣刨回路的颗粒的细度。提供了对该问题的全面文献综述，并讨论了潜在的建模解决方案。使用许多不同的线性和非线性模型对中国水泥厂5个月的数据进行Blaine（颗粒细度）建模。在保持测试集上，多层感知器的MAE为8.799，线性回归的R为 2 为0.481。还讨论了各种功能对预测布莱恩的重要性的讨论。结果表明，非线性数据驱动模型取得的成功有限，突出了水泥厂建模中的一些独特困难，并提出了未来研究的建议。

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