The Development of Real-Time Fouling Monitoring and Control Systems for Reverse Osmosis Membrane Cleaning using Deep Reinforcement Learning

This dissertation investigates potential applications for Machine Learning (ML) and real-time fouling monitors in Reverse Osmosis (RO) desalination. The main objective was to develop a framework that minimizes the cost of membrane fouling by deploying AI-generated cleaning patterns and real-time fouling monitoring. Membrane manufacturers and researchers typically recommend cleaning (standard operating procedure – SOP) when normalized permeate flow, a performance metric tracking the decline of permeate flow/output from its initial baseline with respect to operating pressure, reaches 0.85-0.90 of baseline values. This study used estimates of production cost, internal profitability metrics, and permeate volume output to evaluate and compare the impact of time selection for cleaning intervention. The cleanings initiated when the normalized permeate flow reached 0.85 represented the control for cleaning intervention times. In deciding optimal times for cleaning intervention, a Deep Reinforcement Learning (RL) agent was trained to signal cleaning between 0.85-0.90 normalized with a cost-based reward system. A laboratory-scale RO flat membrane desalination system platform was developed as a model plant, and data from the platform and used to train the model and examine both simulated and actual control of when to trigger membrane cleaning, replacing the control operator's 0.85 cleaning threshold. Compared to SOP, the intelligent operator showed consistent savings in production costs at the expense of total permeate volume output. The simulated operation using the RL initiated yielded 9% less permeate water but reduced the cost per unit volume ($/m3) by 12.3%. When the RL agent was used to initiate cleaning on the laboratory-scale RO desalination system platform, the system produced 21% less permeate water but reduced production cost ($/m3) by 16.0%. These results are consistent with an RL agent that prioritizes production cost savings over product volume output.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Mechanical Engineering

Department dc:contributor.department

Mechanical Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2023

Author dc:creator

• Titus Glover, Kyle Ian Kwartei

Chair dc:contributor.committeechair

• Lesko, John J.

Committee members dc:contributor.committeemember

• Davalos, Rafael V.
• Boreyko, Jonathan B.
• Zuo, Lei

• Reverse Osmosis
• Desalination
• Crossflow System
• Machine Learning
• Deep Reinforcement Learning
• Electrical Impedance Spectroscopy

Statement dc:rights

• Creative Commons Attribution-NonCommercial 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by-nc/4.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:38067

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/115741