Prediction of Deep‐Bed—Filter Performance Using Recursive Algorithms

Abstract

The prediction of head loss and filtrate concentration are of considerable interest to the users of deep‐bed filters. At present, such prediction is done by using existing theoretical models. However, it is also known that the coefficients in these theoretical models are sensitive to changing filtration conditions. Unless a wide range of possible conditions (which occur in a filter run) have been investigated through trial filter runs, predictions using conventional models may not be always reliable. In such circumstances, the use of recursive algorithms, which represent an interrelationship between various equally spaced sequential outcomes, may be very useful. It is possible that very simple recurrence relationships are capable of describing filter performance satisfactorily. In this study, the potential use of recursive algorithms in predicting filter performance has been examined through the analysis of several laboratory and field‐based pilot‐scale filter runs.