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 non-linear 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 the suitability of intelligent techniques has not been widely explored for the task of pressure drop prediction in cyclone separators. To this end, this paper firstly introduces a fuzzy modelling methodology, then presents an alternative version of the Extended Kalman Filter (EKF) to train a Multi-Layer Neural Network (MLNN). The Lagrange dual formulation of Support Vector Machine (SVM) regression model is also deployed 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. Of the conventional models considered, the model of Shepherd and Lapple ( MSE = 7.3e-03, R = 97.88%) gives the best result which is still inferior to the intelligent models.