Prediction of operating state of hydrocyclones using vibrometry and 1D convolutional neural networks

Abstract

Due to high processing capacity requirements and constraints on industrial water utilization, operation of hydrocyclones at higher feed solid concentration is often unavoidable. Hydrocyclone operation at such higher feed solid concentrations is accompanied by the risk of onset of rope discharge, adversely affecting both the size separation and thickening performance of the hydrocyclone. Numerous condition monitoring efforts have been reported in the literature regarding the development of online techniques for detection of roping in hydrocyclones. However, detection of roping, post its commencement, only ever allows for corrective maintenance in the form of measures taken for shifting the discharge back to the desired spray profile. This study focuses on the characterization and online detection of a transition discharge state which precedes the onset of roping, thereby making the prevention of onset of roping, possible. The transition discharge state was delineated from the conventional spray and rope discharge types based upon the observed differences in the decay rates of underflow discharge spray angles with increasing feed solid concentrations. A binary classification model (spray – rope) and a ternary classification model (spray – transition – rope) were developed with a 1D Convolutional neural network architecture capable of taking 5 secs of tri-axial (3-channel) vibration signal data as input, and outputting the predicted discharge type labels with an accuracy of 97.31 % and 94.94 %, respectively.