Abstract
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.
Highlights
The energy efficient operation of grinding circuits remains paramount in the processing of minerals, as these circuits account for a major proportion of the energy consumed in concentrator operations.Optimal control of grinding circuits is key towards achieving these goals
The segmented time series data are are transformed to images, and features are extracted from these images by use use of aaof
The grinding circuit consisted of a fully autogenous mill closed with a recirculating load originating from the oversize material of a screen
Summary
The energy efficient operation of grinding circuits remains paramount in the processing of minerals, as these circuits account for a major proportion of the energy consumed in concentrator operations.Optimal control of grinding circuits is key towards achieving these goals. As a consequence, advanced control systems have been developed, e.g., model predictive controllers [2,3,4], fuzzy logic [5], expert system controllers [6,7] and other nonlinear control systems, such as recently proposed by Inapakurthi et al [8]. Despite these advances, the behavior of grinding circuits can be highly nonlinear and fully automated control may not be feasible in practice.
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