Techniques for Monitoring Reverse Osmosis System Performance

Adjusting the pH of the water is treated with reverse osmosis membrane filtration to maintain optimum water quality. The method involves measuring the pH and water quality of the treated water, changing the inflow water pH by a certain amount, and comparing average water quality before and after the pH change. If the water quality deteriorates, the inflow pH is adjusted to improve it. This iterative process ensures the treated water quality is always within a predetermined range.

Modeling, optimization, and simulation of reverse osmosis (RO) plants to improve the accuracy of predicting when membranes will degrade below threshold levels. A digital twin model of the RO plant is created using plant topology, asset relationships, historical data, and virtual sensors. The model predicts membrane performance degradation over time. Optimization functions are used to determine when performance will fall below thresholds. Notifications are generated to schedule servicing before performance degrades. This enables proactive maintenance to improve plant efficiency.

Controlling a reverse osmosis seawater desalination plant to improve efficiency and reliability. The control system monitors water quality indicators like turbidity and iron levels and pressure differentials in the ultrafiltration and reverse osmosis stages. It adjusts the chemical injection rate based on these measurements to maintain optimal filtration performance without over-injecting.

Machine learning is being used to optimize reverse osmosis systems by reducing cleaning frequency and associated costs. Monitoring reverse osmosis system parameters like pressures, flows, salinity, and temperature becomes essential, which the system does. It calculates fouling indicators like A- and B-values. It uses historical data to train an ML model to predict fouling progression. It then simulates future operation scenarios to find the lowest cost point to clean the membranes.

An automatic feedback control system is used to maintain desired flow rates and water purity levels in a reverse osmosis filtration system. The system uses multiple feedback circuits to iteratively adjust feed and concentrate flow rates entering and exiting the filter based on the permeate flow rate and purity measurements.

Proportioning valve for reverse osmosis drinking water systems that reduces waste water generation by modulating the wastewater flow in proportion to the product water flow. The valve has a sliding piston that opens or closes a reject water channel based on pressure differentials. A sensor detects tank pressure and controls the piston position to balance product and wastewater rates. This optimizes the rejection efficiency while minimizing wastewater.

Predicting fouling in reverse osmosis membranes to determine when to do chemical cleaning and membrane replacement. The method involves collecting process and water quality data, normalizing it for temperature and flow rate, generating a prediction equation for fouling based on the normalized factors, and using the equation to predict fouling levels and determine maintenance timing.

Method for controlling water purification systems with reverse osmosis to optimize recovery and purity while reducing water consumption. The system measures the feed water quality and sets a target recovery rate. The feed pump speed is controlled to reach the target permeate quality. The drain flow rate is adjusted to achieve the target recovery.

A method for controlling a water purification apparatus utilizing reverse osmosis to consistently produce purified water with desired conductivity while minimizing energy and waste. The method involves recirculating a portion of reject water to achieve a recovery ratio, measuring RO membrane temperature and permeate flow rate, and controlling the feed pump to maintain an energy-efficient permeate flow rate based on temperature and desired conductivity.

An in-situ method to assess fouling and critical flux of reverse osmosis membranes. The method uses electrical impedance spectroscopy to monitor membrane fouling. Impedance is measured at low frequencies across the membrane while flux is varied. A critical flux is identified where impedance changes, indicating fouling. By tracking critical flux over time, fouling buildup can be monitored.