Reverse Osmosis Performance Parameters

Monitoring a fluid and controlling a process in a membrane filtration plant to optimize cleaning and flushing procedures. The system uses sensors to measure characteristics of the feed, permeate, and retentate streams. By comparing these measurements, it determines if the cleaning has reached a target level. If so, it stops the flush to save fluid and avoid overcleaning. This prevents waste of expensive cleaning fluids while ensuring adequate cleaning. The system can also stop flushing if the time limit is reached. This prevents infinite flushing due to sensor errors.

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Intelligent monitoring and control system for reverse osmosis water treatment systems that improves accuracy, speed, and reliability compared to manual monitoring. The system uses sensors before and after each filter stage, as well as chlorine sensors and flow meters, to provide real-time data on water quality and flow rates. This data is analyzed by a controller to optimize dosing of chemicals, detect issues like filter blockage, and provide early warning of problems. The system also has a communication module to transmit data and alerts.

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Reverse osmosis membrane concentration rate control system for power plants that automatically adjusts the membrane concentration rate without manual intervention. It uses sensors to monitor parameters like water quality, flow, temperature, pressure, and concentration. A control module compares these values to predefined thresholds and triggers actions like opening/closing valves to optimize concentration, prevent fouling, and mitigate risks like organic pollution. This provides stable, efficient, and automated membrane concentration without manual sampling or control.

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Predicting membrane maintenance and replacement dates for water treatment systems using artificial intelligence. The method involves calculating standardized operating indicators for membranes based on normalized measurements. It learns regression models for each indicator using historical data. The normalized indicators are used to predict membrane aging and fouling. This allows generating alerts when membranes need cleaning or replacement without reconfiguring the models for different membrane types or architectures. The normalized indicators are calculated from measured variables like pressure, flow rate, salt passage, and temperature.

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Real-time monitoring of reverse osmosis (RO) membrane fouling to enable early detection, characterization, and mitigation of fouling. The technique involves isolating the permeate of a lead or tail element in each stage of an RO system and measuring its flux and rejection directly. This provides much higher sensitivity and faster detection of fouling compared to monitoring the entire stage or system. By isolating the lead or tail element permeate and measuring its performance, fouling can be detected several times faster. This allows for more targeted and effective fouling management strategies.

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Water treatment device with monitoring to prevent membrane fouling, oxidation, and damage in reverse osmosis systems. The device has multiple sensors before and after the filter to continuously monitor water quality indicators like temperature, conductivity, pH, flow rate, pressure. This allows real-time monitoring and early warning of issues like fouling, oxidation, damage. The device also has backwash capability for the filter to clean it. This comprehensive monitoring and cleaning approach prevents membrane issues that can impact system performance and longevity.

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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.

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Diagnostic device for reverse osmosis membrane systems used in sodium chloride regeneration. The device has a fault diagnosis mechanism inside the system enclosure. It includes a sensor, signal transmitter, receiver, power supply, and buzzer. The sensor monitors internal conditions, sends signals via the transmitter to a cloud server, and compares against threshold values. If exceeded, diagnostic information is sent to the receiver and the buzzer alarms. This allows remote monitoring and early fault detection in sodium chloride regeneration systems.

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A dynamic control system for reverse osmosis membrane fouling based on real-time monitoring of operating conditions. The system uses sensors on the inlet and outlet pipes of each membrane group to monitor parameters like pressure, conductivity, and flow rate. These signals are fed back to a control system to determine the best cleaning time for each membrane based on flux and transmembrane pressure differences. This allows targeted cleaning of fouled membranes instead of full system shutdowns.

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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.

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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.

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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.

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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.

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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.

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Monitoring the operation of a reverse osmosis membrane treatment system to detect fouling and take appropriate action. The monitoring involves measuring the power consumption of the water supply pump and monitoring the power value. Fouling tendency is determined based on fluctuations in supply flow rate, pressure, and temperature. If fouling is detected, measures like adding chemicals, changing pH, or temperature can be taken to suppress fouling.

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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.

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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.

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Desalination apparatus that can detect in advance the reduction in water flux through the membrane due to scale buildup. The apparatus monitors concentration factors N1 and N2 during desalination. N1 is the concentration ratio of charged species like calcium ions across the membrane, while N2 is the concentration ratio of uncharged species like organic compounds. Changes in N1 and N2 immediately and conspicuously indicate scale formation. By monitoring these factors, the apparatus can detect scale at an early stage and take preventative measures before flux reduction occurs.

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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.

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Automatic control system for reverse osmosis water treatment systems that optimizes operation and prevents issues like scaling and fouling. The system has modules for medicament dosing, central control, parameter monitoring, security filter replacement alarm, and reverse osmosis membrane cleaning alarm. It uses real-time monitoring of influent and produced water parameters to determine when to add chemicals, clean membranes, replace filters, etc.

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