R-WGAN-Based Multitimescale Enhancement Method for Predicting f-CaO Cement Clinker

To address the problem that the high dimensionality, time series, coupling, and multiple timescales of production data in the process industry lead to the low accuracy of traditional prediction models, we propose a multitimescale data enhancement and cement clinker free calcium oxide (f-CaO) prediction method based on the regression-Wasserstein generative adversarial net (R-WGAN) model. The model is built using a combination of WGAN and regression prediction networks. First, the data are extracted according to the principle of sliding window to eliminate the effect of time-varying delay between data in data enhancement and prediction, and a dual data pathway is used for data stitching so that data of different timescales can be enhanced at the same time. We then augment the data with a generator network, use a discriminator network to judge the goodness of the generated data, and propose an auxiliary evaluation strategy to evaluate whether the high-dimensional generated data conform to the actual laws, expand the training set of the regression prediction network with the generated data that conform to the laws, and finally achieve the prediction of cement clinker f-CaO. The model was applied in the quality management system of a cement company for simulation, and experiments showed that the model with data enhancement has the advantages of high accuracy, robustness, and good generalization in cement clinker f-CaO prediction.