Optimizing the Sulphates Content of Cemen tUsing Multivariable Modelling and Uncertainty Analysis
This study aims at determining the effect of the cement composition and mortar age on the optimum content of sulphates using maximization of compressive strength as a criterion. Extensive experimentation has been performed to achieve this purpose by utilizing four cement types and measuring the strength at ages ranging from 2 days up to 63 months. Strength is correlated with the ratio of sulphates to clinker content, SO3/CL and moles SO3/ moles C3A. A generalized model correlating SO3, cement composition and curing time is developed, intensely indicating the necessity of placing the SO3 target within the optimum region and realizing it as well. The set of four equations involves polynomial and logarithmic equations correlating the variables. A variance analysis based on error propagation technique, proves that SO3 affects not only average value of strength but also its variability.
Optimizing the Cement Compressive Strength Prediction by Applying Coupled Linear Models
This study is aiming to search the optimum settings of two kinds of dynamic linear models predicting cement typical strength and afterwards to couple them to achieve optimality. The modeling is based on physical and chemical characteristics and on the early strength of the Portland cement types studied. More than 3000 sets of industrial results were used for this purpose. Models parameters are calculated using a moving past period of length TD and are tested in a future period of length TF. The moving period characterizing the dynamical models attempts to assure that changes in the process are taken into account during the parameters calculation. Among the models the coupled one is noticeably superior as regards the mean square residual error. The implementation of these methods in the daily quality control is an essential factor of quality improvement by maintaining a low variance of typical strength. Key-Words: Strength, Model, Cement, Dynamic, Prediction, Quality Control
Annual Reports
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