Reverse Osmosis AI Recirculation Control for Scaling-Limited Yield
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Solution Overview
Problem
Existing reverse osmosis systems struggle to set an optimal yield due to variations in water conductivity and temperature, leading to inefficiencies and reduced permeate quality from scaling.
Innovation Solution
A reverse osmosis system utilizing a first and second conductivity sensor, a temperature sensor, and an AI unit with a statistical model to automatically adjust the recirculation proportion of concentrate based on measured conductivity and temperature, optimizing yield through machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the recirculation proportion of concentrate is adjusted to increase yield, then water productivity improves, but permeate quality deteriorates due to scaling
Solution Approach 1:
The system continuously measures the electrical conductivity of the permeate and uses this feedback to dynamically adjust the recirculation proportion of concentrate. The AI unit processes conductivity measurements along with temperature data to automatically optimize the recirculation ratio, ensuring permeate quality remains within target specifications while maximizing water yield.
Solution Approach 2:
The system changes operational parameters (recirculation proportion, pump speeds, valve opening intervals) based on measured conductivity and temperature values. The AI unit calculates optimal parameter settings by analyzing the relationship between input water conductivity, temperature, and desired permeate quality, thereby dynamically adapting system operation to maintain quality while improving productivity.
2Ease of operation
If conventional yield setting methods are used, then system operation is simple, but optimal yield cannot be achieved due to varying water conductivity and temperature
Solution Approach 1:
The system performs self-optimization by automatically adjusting the recirculation proportion based on real-time conductivity and temperature measurements. The AI unit continuously learns from operational data and autonomously determines optimal yield settings without requiring manual intervention or complex user input, thereby maintaining ease of operation while maximizing productivity.
Solution Approach 2:
The patent replaces manual yield setting mechanisms with an AI-based automated control system. Instead of requiring operators to manually adjust recirculation proportions based on experience, the system uses machine learning models that process sensor data and automatically calculate optimal settings, substituting mechanical/manual control with intelligent automation.
3Device complexity
If manual adjustment of recirculation proportion is performed, then system complexity is low, but response time to conductivity changes is slow
Solution Approach 1:
The system implements continuous feedback control by measuring electrical conductivity and temperature in real-time and automatically adjusting recirculation proportions accordingly. This closed-loop feedback mechanism enables rapid response to changes in water quality parameters, significantly reducing response time compared to manual adjustment methods.
Solution Approach 2:
The AI unit performs preliminary calculations and predictions about optimal recirculation settings based on current conductivity and temperature values. By pre-calculating optimal parameters before actual adjustments are needed, the system prepares control actions in advance, enabling faster response to changing conditions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Achieves an optimal water yield of 50-95% by maintaining permeate conductivity within a target range, minimizing scaling and enhancing system performance.
Implementation Method 1
a filter (15) comprising a membrane (11) for reverse osmosis
Implementation Method 2
a first conductivity sensor (1) for measurement of an electrical conductivity of water (2)
Implementation Method 3
a temperature sensor (7) for measurement of a temperature, in particular a temperature of the permeate
Data Source
AI summary
A reverse osmosis system includes a first conductivity sensor for measuring electrical conductivity of water supplied to the reverse osmosis system, and a second conductivity sensor for measuring electrical conductivity of a permeate produced by the reverse osmosis system. The system also includes an AI unit designed to use a statistical model for calculating and accordingly setting a proportion of a concentrate produced by the reverse osmosis system that is to be recirculated according to the measured electrical conductivity of the water supplied to the reverse osmosis system, and according to the measured electrical conductivity of the permeate produced by the reverse osmosis system. The statistical model can be trained with training data.