Abstract：The content of free calcium oxide (fCaO) in cement clinker has an important impact on cement quality and production energy consumption.At this stage, the fCaO content of cement clinker is measured offline by chemical analysis method, but this method has obvious hysteresis for the operation guidance of the production process.Aiming at the problem that clinker fCaO could not be monitored online, a soft measurement modeling method of clinker fCaO based on multi-variable time series single-dimensional convolutional neural network (TS-CNN) was proposed.The time series of a certain historical time period that affects multiple variables of clinker fCaO was used as the models input, and the cement data was combined to extract the characteristics of each variable by using single-dimensional convolution pooling method to reduce the complexity of the network, and finally the extracted local information was integrated by the fully connected layer.Through the experimental comparison, the results show that the soft measurement method based on TS-CNN has higher prediction accuracy and more generalization ability.
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2020, Vol. 41 
