Validation of a Simple Fouling Model for a Submerged Membrane Bioreactor

Abstract

Most of the published membrane bioreactor (MBR) models have been proposed for process description and gain of insight, resulting in a large number of parameters to estimate from experimental data. These models are usually too complex for process control, and there is a need for simple, dedicated, dynamic models. In this study, attention is focused on the fouling phenomenon, which hampers the efficient operation of MBRs, and a simple model is proposed and validated using a large data base collected from a pilot plant. This model includes several manipulated variables, e.g., the permeate flow rate and the air scour, as well as a measurable perturbation such as the water temperature. To ease the identification procedure, a separation of the time scales of the process in slow and fast dynamics is exploited. The results show that the model can be used to predict the trans-membrane pressure behavior in a medium-term, about 10 days ahead.

The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review

“…Membrane fouling, the process by which foulants, namely colloidal (e.g., clays, flocs), biological (e.g., bacteria, fungi), organic (e.g., oils, polyelectrolytes, humic substances), and scaling (e.g., mineral precipitates in RO systems) foulants, deposit onto the membrane surface or in the membrane pores [72,73], may take different forms, the main mechanisms of which are adsorption (physical and/or chemical), pore blocking, deposition of a cake layer, and gel formation [74][75][76][77][78][79]. The extent of fouling, which stems from the nature of foulant-membrane interaction, is a complex function of the feed characteristics (e.g., foulant type, foulant concentration, and physicochemical properties of the foulants such as the functional groups, charge, size, and conformation [72,[80][81][82]), operating conditions (e.g., inadequate pretreatment, inadequate control of the hydrodynamics of the system, excessive flux, and low cross-flow velocity (in cross-flow systems) [72,[82][83][84]), and membrane properties (e.g., pore-size distribution, surface roughness, charge properties, and hydrophobicity [70,[85][86][87]). …”

Recent Advances in the Prediction of Fouling in Membrane Bioreactors

“…The mechanistic mathematical model [ 45 , 46 , 47 , 48 ] and computational fluid dynamics simulation [ 49 , 50 ] show excellent performance in a large number of simulation studies of transmembrane pressure difference and flux. However, the complexity of membrane fouling limits the development of these models.…”

“…Pimentel et al [ 46 ] proposed a model to reproduce the dynamics of TMP in submerged MBR with infiltration flow rate, aeration rate, solid concentration, and temperature as the inputs. This dynamic model could predict the evolution of TMP for an acceptable period (about 10 days), and the determination coefficient between the predicted TMP and the experimental data was about 0.95.…”