Process Simulation of High-Pressure Nanofiltration (HPNF) for Membrane Brine Concentration (MBC): A Pilot-Scale Case...

Area of Science:

• Environmental Engineering
• Chemical Engineering
• Water Treatment Technologies

Background:

• Growing demand for sustainable water management solutions drives innovation in membrane brine concentration (MBC).
• Desalination and minimum liquid discharge (MLD) applications necessitate efficient brine management.
• High-pressure nanofiltration (HPNF) is explored as a key MBC technology.

Purpose of the Study:

• To develop and validate a simple model for high-pressure nanofiltration (HPNF) in membrane brine concentration (MBC).
• To predict water flux and total dissolved solids (TDS) concentration using integrated RO and mass balance equations.
• To optimize MBC process performance using response surface methodology (RSM).

Main Methods:

• Integration of reverse osmosis (RO) transport equations with mass balance equations.
• Development of a simple predictive model for HPNF brine concentration.
• Application of response surface methodology (RSM) for multi-criteria process optimization.

Main Results:

• The HPNF model demonstrated high accuracy (R² > 0.99) in predicting flux and TDS, despite pilot data limitations.
• Increased feed flow rate enhanced flux but raised specific energy consumption (SEC) and reduced recovery.
• Higher feed pressure improved recovery and brine concentration, while increased feed TDS decreased flux, recovery, and final brine TDS, increasing SEC.

Conclusions:

• The developed HPNF model provides a reliable tool for predicting MBC performance.
• Process parameters significantly influence flux, recovery, brine concentration, and energy consumption.
• RSM is effective for optimizing HPNF processes, with optimal settings dependent on specific improvement criteria.