PREDICTION OF DEEP-BED FILTER PERFORMANCE USING RECURSIVE ALGORITHMS

The prediction of head loss and filtrate concentration are of consider able interest to the users of deep-bed filters. At present, such predi ction is done by using existing theoretical models. However, it is als o known that the coefficients in these theoretical models are sensitiv e to changing filtration conditions. Unless a wide range of possible c onditions (which occur in a filter run) have been investigated through trial filter runs predictions using conventional models may not be al ways reliable. In such circumstances, the use of recursive algorithms, which represent an interrelationship between various equally spaced s equential outcomes, may be very useful. It is possible that very simpl e recurrence relationships arc capable of describing filter performanc e satisfactorily. In this study, the potential use of recursive algori thms in predicting filter performance has been examined through the an alysis of several laboratory and field-based pilot-scale filter runs.