Enhancing Prediction in Cyclone Separators through Computational Intelligence

Pressure drop prediction is critical to the design and performance of cyclone separators as industrial gas cleaning devices. The complex nonlinear relationship between cyclone Pressure Drop Coefficient (PDC) and geometrical dimensions suffice the need for state-of-the-art predictive modelling methods. Existing solutions have applied theoretical/semi-empirical techniques which fail to generalise well, and some intelligent techniques have also been applied such as the neural network which can still be improved for optimal equipment design. To this end, this paper firstly introduces a fuzzy modelling methodology, then presents an alternative Extended Kalman Filter (EKF) for the learning of a Multi-Layer Neural Network (MLNN). The Lagrange dual formulation of Support Vector Machine (SVM) regression model is deployed as well for comparison purposes. For optimal design of these models, manual and grid search techniques are used in a cross-validation setting subsequent to training. Based on the prediction accuracy of PDC, results show that the Fuzzy System (FS) is highly performing with testing mean squared error (MSE) of 3.97e-04 and correlation coefficient (R) of 99.70%. Furthermore, a significant improvement of EKF-trained network (MSE = 1.62e-04, R = 99.82%) over the traditional Back-Propagation Neural Network (BPNN) (MSE = 4.87e-04, R = 99.53%) is observed. SVM gives better prediction with radial basis kernel (MSE = 2.22e-04, R = 99.75) and provides comparable performance to universal approximators. In comparison to conventional theoretical and semi-empirical models, intelligent approaches can provide far better prediction accuracy over a wide range of cyclone designs, while the EKFMLNN performance is noteworthy.