NAMS 2002 Workshop - ICOM 2008

Membrane Modeling III - Process Simulations – 5
Wednesday July 16, 11:45 AM-12:15 PM, O’ahu/Waialua
Hybrid Modeling: An Alternative Way to Predict and Control the Behavior of
Cross-Flow Membrane Filtration Processes
S. Curcio (Speaker), University of Calabria, Rende, Italy
V. Calabro', University of Calabria, Rende, Italy
G. Iorio, University of Calabria, Rende, Italy - gabriele.iorio@unical.it
The aim of the present paper is to develop a hybrid model predicting the behavior
of ultrafiltration process, performed in pulsating conditions. The hybrid model
actually consists of two different components: a fundamental, theoretical model
describing the unsteady-state transport of both momentum and mass in the
module channel and through the membrane, and a very simple cause- effect
model, based on an artificial neural network (ANN). The theoretical model,
described by a system of partial differential equations solved by Finite Elements
Method (FEM), allows predicting the time evolution of concentration polarization
and of permeate flux decay as a function of process input variables. The neural
model, instead, is used to determine, in a wide range of operating conditions, the
functional relationship existing between the concentration of the rejected species
adsorbed on the membrane surface and the additional resistance due to the
membrane fouling. The main advantage of hybrid modeling actually regards the
possibility to describe some well-assessed phenomena, such as concentration
polarization phenomena and their dependence on the operating conditions, by
means of a fundamental theoretical approach. Some others, like the complex
interactions existing between the adsorbed solute(s) and the membrane surface,
could be very difficult to interpret and, therefore, to express in terms of proper
mathematical relationships. An artificial neural network can make up for this
limited knowledge of complex physical phenomena with the identification of
rather simple single input single output (SISO) models, based on ANN. The
observed reliability of hybrid model predictions suggested the possibility of
implementing an advanced control system that could generate proper trans-
membrane pressure and feed flow rate pulsations, thus promoting polarized layer
disruption and, consequently, membrane performance enhancement. This
feedback control system has been developed by the integration of different
computational environments, thus resulting in the manipulation of the UF
experiments operating conditions as control variables, according to the hybrid
model suggestions for a permeate flux enhancement. In particular, the effects of
proportional, integral and derivative control actions on the responses of the
controlled process have been examined.