Dynamic Modeling and Nonlinear Parameter Estimation of Nanofiltration Membranes
AIChE Member Credits 0.5 AIChE Members $19.00 AIChE Graduate Student Members Free AIChE Undergraduate Student Members Free Computing and Systems Technology Division Members Free Non-Members $29.00
- Type: Conference Presentation
- Conference Type: AIChE Spring Meeting and Global Congress on Process Safety
- Presentation Date: April 21, 2021
- Duration: 20 minutes
- Skill Level: Intermediate
- PDHs: 0.50
In this talk, we present an integrated experimental and data analysis technique to dynamically characterize nanofiltration membranes, resulting in differential-algebraic process models with quantified uncertainty suitable for process design. Experiments were run in a 10 mL dead-end filtration Amicon 8010 stirred cell connected with a dialysate tank. We conducted transport studies by loading both the cell and tank with predetermined concentrations of the desired salt and pressurizing the system with nitrogen gas. This apparatus design allows for continuous monitoring of permeate sample mass. Both retentate and sample concentrations are measured at regular intervals. We then postulate a family of differential-algebraic equation models for the system. Nonlinear regression is used to successfully calibrate hydraulic permeability, solute permeability coefficient, and reflection coefficient parameters in the model. These parameters correspond to the membrane transport properties and depend on the membrane chemistry, membrane nanostructure, and thermodynamics of the membrane-solution interface. As proof of concept, we consider diafiltration experiments for Mg2+ and K+ ions across an NF-90 nanofiltration membrane. We cast nonlinear parameter estimation as a multi-objective least-squares optimization problem and explore the trade-offs between different experimental observations, i.e., the mass of collected samples and sample concentrations. Through this analysis, we determined reflection coefficient is not identifiable in conventional filtration experiments and used the model to inform the diafiltration experiment design that covers higher concentrations. Finally, we discuss how the proposed dynamic experiments and data analysis technique require much less data than classical steady-state experiments to create and validate a model.
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|AIChE Member Credits||0.5|
|AIChE Graduate Student Members||Free|
|AIChE Undergraduate Student Members||Free|
|Computing and Systems Technology Division Members||Free|