Modeling and optimization of chlorophenol rejection for spiral wound reverse osmosis membrane modules

Area of Science:

• Environmental Science
• Chemical Engineering
• Water Treatment Technologies

Background:

• Chlorophenol contamination poses risks to water quality.
• Reverse osmosis (RO) is a key technology for water purification.
• Predicting RO performance for specific contaminants like chlorophenols is challenging due to complex interactions.

Purpose of the Study:

• To develop and validate an artificial neural network (ANN) model for predicting chlorophenol rejection from aqueous solutions.
• To assess the performance of spiral wound reverse osmosis (SWRO) modules in removing chlorophenols.
• To analyze the non-linear relationships between SWRO process parameters and chlorophenol rejection.

Main Methods:

• Development of an artificial neural network (ANN) model.
• Experimental testing of spiral wound reverse osmosis (SWRO) modules.
• Comparison of ANN model predictions with experimental data for chlorophenol rejection.

Main Results:

• The ANN model demonstrated a high degree of accuracy in predicting chlorophenol rejection.
• Achieved an overall agreement of almost 99.9% between predicted and experimental results.
• Identified complex, non-linear dependencies of chlorophenol rejection on feed pressure, temperature, concentration, and flow rate.

Conclusions:

• ANN models are effective tools for understanding and predicting chlorophenol rejection in SWRO processes.
• The developed ANN model provides a reliable method for assessing SWRO module performance.
• This approach enhances the understanding of contaminant removal mechanisms in water treatment.