A Novel Modeling Optimization Approach for a Seven-Channel Titania Ceramic Membrane in an Oily Wastewater Filtration...

Area of Science:

• Materials Science
• Environmental Engineering
• Chemical Engineering

Background:

• Produced water treatment is critical for environmental protection and water resource management.
• Ceramic ultrafiltration membranes offer a robust solution for challenging water matrices.
• Optimizing operational parameters is key to maximizing membrane efficiency and lifespan.

Purpose of the Study:

• To investigate the impact of operational conditions on a novel seven-channel titania ceramic ultrafiltration membrane.
• To determine optimal operating parameters for treating produced water.
• To model and predict membrane performance using statistical and artificial intelligence approaches.

Main Methods:

• Full factorial design (2^3) experiment to study transmembrane pressure (TMP), crossflow velocity (CFV), and filtration time (FT).
• Eleven experimental runs to record membrane permeate flux decline and total permeate volume.
• Application of Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) for performance modeling.

Main Results:

• Optimized conditions (1.5 bar, 1 m/s, 2 h) yielded 99% oil rejection and a flux of 297 LMH.
• A 38% initial flux decline and 8.14 L total permeate volume were observed under optimal settings.
• Regression models achieved a high goodness of fit (R=0.999), with ANN predictions validated at R^2=99%.

Conclusions:

• Operational conditions significantly influence the performance of titania ceramic ultrafiltration membranes.
• The developed models accurately predict membrane behavior, aiding in process optimization.
• This research demonstrates the potential of the novel membrane for effective produced water treatment.