Characterisation of Membrane Fouling Using Neural Networks

1. Characterisation of Membrane Fouling Using Neural Networks PhD Student: Supervisor: Dan Libotean Jaume Giralt

2. Outline • Introduction • Objectives • Tools • Methodology • Summary

3. Water desalination • drinking and agricultural water • new water standards Introduction Objectives Tools Methodology Summary Introduction

4. Membrane Fouling • Adsorption • Clogging the pores • Deposits of solids • Crystallization and compaction of membrane structure • Gel layer • Bacterial growth Introduction Objectives Tools Methodology Summary Introduction

5. Membrane processes models few theoretical models • numbers of limitations • various compounds in raw material modelling optimisation control process models based on experimental data ANNs Introduction Objectives Tools Methodology Summary Introduction

6. Objectives • develop a model that use QSAR* parameters to predict membranes performance • identify the influence of different variables over the fouling phenomenon • predict the hydraulic resistance due to reversible and irreversible fouling *QSAR - Quantitative Structure Activity Relationship Introduction Objectives Tools Methodology Summary Objectives

7. Artificial Neural Networks • Backpropagation Introduction Objectives Tools Methodology Summary Tools

8. Artificial Neural Networks • Kohonen Self-Organizing Maps Introduction Objectives Tools Methodology Summary Tools

9. Artificial Neural Networks • Fuzzy ARTMAP Introduction Objectives Tools Methodology Summary Tools

10. Predicting RO Rejection using QSAR Analysis • Quantitative Structure Activity Relationships (QSAR) • Numerical Descriptors of Molecular….. • Mass • Shape • Charge • Polarity • Solubility • Branching • Cyclicity • Adjacency • Atom Types • Valence States • Many Others… • May describe compound rejection by RO membrane Components List Use MD to determine QSAR parameters Identify surrogate(s) for each cluster Classify the compounds in various clusters Identify QSAR parameters most correlated with membrane interactions Perform experiments using various membranes Construct NN for the prediction of the behaviour of all components Introduction Objectives Tools Methodology Summary Methodology

11. Prediction of Fouling Buena Vista Water Storage District pilot plant *Polymer and Separations Research Laboratory, UCLA, USA Introduction Objectives Tools Methodology Summary Methodology

12. Summary Necessity of modelling the membrane processes Predict membranes performance using QSAR parameters Predict reversible/irreversible fouling Introduction Objectives Tools Methodology Summary Summary

13. Questions?