scholarly journals Nonlinear dynamic process monitoring using deep dynamic principal component analysis

2022 ◽  
Vol 10 (1) ◽  
pp. 55-64
Author(s):  
Simin Li ◽  
Shuanghua Yang ◽  
Yi Cao ◽  
Zuzen Ji
Keyword(s):  
Principal Component Analysis ◽  
Process Monitoring ◽  
Dynamic Process ◽  
Nonlinear Dynamic ◽  
Principal Component ◽  
Component Analysis ◽  
Dynamic Principal Component Analysis

scholarly journals Dynamic Monitoring of Grinding Circuits by Use of Global Recurrence Plots and Convolutional Neural Networks

Minerals ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 958
Author(s):  
Jacques Olivier ◽  
Chris Aldrich
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Convolutional Neural Networks ◽  
Process Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Further Training ◽  
Efficient Operation ◽  
Dynamic Principal Component Analysis ◽  
Grinding Circuits

Reliable control of grinding circuits is critical to more efficient operation of concentrator plants. In many cases, operators still play a key role in the supervisory control of grinding circuits but are not always able to act timely to deal with disturbances, such as changes in the mill feed. Reliable process monitoring can play a major role in assisting operators to take more timely and reliable action. These monitoring systems need to be able to deal with what could be complex nonlinear dynamic behavior of comminution circuits. To this end, a dynamic process monitoring approach is proposed based on the use of convolutional neural networks. To take advantage of the availability of pretrained neural networks, the grinding circuit variables are treated as time series which can be converted into images. Features extracted from these networks are subsequently analyzed in a multivariate process monitoring framework with an underlying principal component model. Two variants of the approach based on convolutional neural networks are compared with dynamic principal component analysis on a simulated and real-world case studies. In the first variant, the pretrained neural network is used as a feature extractor without any further training. In the second variant, features are extracted following further training of the network in a synthetic binary classification problem designed to enhance the extracted features. The second approach yielded nominally better results than what could be obtained with dynamic principal component analysis and the approach using features extracted by transfer learning.

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