Prediction of Cement Compressive Strength by Combining Dynamic Models of Neural Networks

This study aimed at developing models predicting cement strength based on shallow neural networks (ANN) using exclusively industrial data. The models used physical, chemical, and early strength results to forecast those for 28- and 7-day. Neural networks were trained dynamically for a movable period and then used for a future period of at least one day. The study includes nine types of activation functions. The algorithms use the root mean square errors of testing sets (RMSEFuture) and their robustness as optimization criteria. The RMSEFuture of the best models with optimum ANNs was in the range of 1.36 MPa to 1.63 MPa, which is near or within the area of long-term repeatability of a very competent laboratory. Continuous application of the models in actual conditions of a cement plant in the long-term showed a performance at least equivalent to that calculated during the design step. This work is licensed under a Creative Commons Attribution 4.0 International License

Improving the Prediction of Cement Compressive Strength by Coupling of Dynamical Models

The dynamic approach of two well-known techniques has been used to predict a cement’s 28-day compressive strength: Multiple linear regression (MLR), and artificial neural networks (ANN). The modeling is based on Portland cement data and utilizes daily physical, chemical analyses, and early strength results at days 1 and 7. Two kinds of models have been built, containing the 1-day strength as an independent variable, or both 1- and 7-day strength. The models are dynamic because they are applied to a movable past period of TD days to calculate the parameters, and then used for a future period of TF days. The comparison is based on the residual error of the testing period, and TD, TF have been optimized. Eight ANNs of different complexity have been developed, but some of them are suffering from over-fitting. A third model has also been created with the coupling of the initial two. The time parameters as well as the filtering and weighting coefficients of the coupled model have been optimized. The simple ANNs with one node in the hidden layer, sigmoid or hyperbolic functions and bias, show better performance. The combination of the coupled model with these two best ANN techniques provides an improved prediction of 28-day strength compared with the initial model containing the 1-day strength. The sensitivity also of TF parameter is lower providing certain benefit in daily industrial application. The implementation of these methods in cement process control can contribute to quality improvement by maintaining a low variance of typical strength

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

Simulation of Cement Grinding Process for Optimal Control of SO3 Content

The control of cement grinding/mixing process in an industrial mill regarding SO3 content has been effectively simulated taking into account all its fundamental sides and particularities. Based on a simulator, two controllers of different philosophy have been studied: A classical proportional-integral (PI) controller as well as a nonlinear one (step changes, SC) consisting of certain classes of SO3 output ranges that result in certain levels of discrete corrections of gypsum feed. Initially, the simulator was implemented for grinding of a single cement type each time. Totally, three cement types were investigated. The controllers have been parameterized and compared using the minimal standard deviation of SO3 as a criterion. Both provided satisfactory SO3 consistency, but PI was more efficient against SC as with the double sampling period, the same minimum standard deviation was obtained leading to equal results with half the sampling actions. The simulation was also realized in milling of several cement types. A feedforward part was added to the feedback loop to face the case of cement type changing. The results of operation of this kind of controller in an industrial milling system contribute greatly to the improvement in cement quality