Improving cement production process with data-augmented sequence to sequence-Wasserstein generative adversarial networks model for accurate prediction of f-CaO.

This paper proposes a method to address the issue of insufficient capture of temporal dependencies in cement production processes, which is based on a data-augmented Seq2Seq-WGAN (Sequence to Sequence-Wasserstein Generate Adversarial Network) model. Considering the existence of various temporal scales in cement production processes, we use WGAN to generate a large amount of f-CaO label data and employ Seq2Seq to solve the problem of unequal length input-output sequences. We use the unlabeled relevant variable data as the input to the encoder of the Seq2Seq-WGAN model and use the generated labels as the input to the decoder, thus fully exploring the temporal dependency relationships between input and output variables.

A spatio-temporal data decoupling convolution network model for specific surface area prediction in cement grind process.

The specific surface area is one of the important indicators for measuring the quality of cement products. Realizing accurate prediction for specific surface area is very important for the production scheduling of the cement industry, energy conservation and consumption reduction and improvement of cement performance. However, due to the non-linearity, uncertainty, multiple interference, dynamic time-varying delay and multi scales in cement grinding process, it is difficult to establish an accurate soft-sensing model for cement quality prediction.

A soft sensor model based on long&short-term memory dual pathways convolutional gated recurrent unit network for predicting cement specific surface area.

The specific surface area of cement is an important index for the quality of cement products. But the time-varying delay, non-linearity and data redundancy in the process industry data make it difficult to establish an accurate online monitoring model. To solve the problems, a soft sensor model based on long&short-term memory dual pathways convolutional gated recurrent unit network (L/S-ConvGRU) is proposed for predicting the cement specific surface area.

A Synchronous Prediction Model Based on Multi-Channel CNN with Moving Window for Coal and Electricity Consumption in Cement Calcination Process.

The precision and reliability of the synchronous prediction of multi energy consumption indicators such as electricity and coal consumption are important for the production optimization of industrial processes (e.g., in the cement industry) due to the deficiency of the coupling relationship of the two indicators while forecasting separately.