Real-Time Water Quality Assessment for Particle-Size Dosage Control
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Solution Overview
Problem
Traditional methods for monitoring water quality, particularly particle-size distribution, are time-consuming, labor-intensive, and do not provide real-time data, leading to inefficiencies in water treatment processes and inconsistent results.
Innovation Solution
A machine learning model is employed to determine particle-size distribution in water samples using turbidity and total suspended solids data, enabling real-time adjustments to water treatment processes by optimizing chemical dosages for optimal floc formation and aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual sampling and laboratory analysis are used to monitor water quality, then detailed particle-size information can be obtained, but the process is time-consuming and does not provide real-time data
Solution Approach 1:
The patent replaces manual mechanical sampling and laboratory analysis with an automated optical detection system using turbidity meters and particle counters that continuously measure water samples in real-time, eliminating the time delay between sampling and analysis while maintaining measurement capability
Solution Approach 2:
The patent introduces machine learning models as an intermediary that correlates turbidity and particle count data with particle-size distribution information, enabling real-time estimation of particle-size characteristics without direct manual measurement, thus bridging the gap between rapid automated sensing and detailed particle-size analysis
2Productivity
If conventional tools such as turbidity meters and particle counters are used, then real-time data can be obtained, but detailed particle-size distribution information is not provided
Solution Approach 1:
The patent introduces machine learning models as an intermediary that correlates turbidity and particle count data with particle-size distribution information, enabling real-time estimation of particle-size characteristics without direct manual measurement
Solution Approach 2:
The system uses feedback from continuous turbidity and particle count measurements to continuously update and refine particle-size distribution estimates through the machine learning model, maintaining accurate real-time information about particle characteristics
3Device complexity
If manual sampling methods are used, then equipment costs can be reduced, but labor intensity and inconsistency increase
Solution Approach 1:
The system implements self-service automation where the turbidity meters, particle counters, and machine learning models automatically perform water quality assessment without manual intervention, eliminating labor-intensive sampling while ensuring consistent, repeatable measurements through standardized automated procedures
Data Source
AI summary
Methods and systems for water quality assessment are disclosed. The method includes obtaining a first input data indicative of properties of a first liquid sample, the first input data including turbidity data and total suspended solids data for the first liquid sample, where the first liquid sample is acquired from a liquid source. The method further includes determining, using a computer processor and a machine learning model, a first predicted particle-size distribution of the first liquid sample based on the first input data, where particle-size distribution is controlled, at least in part, by a set of dosage parameters configurable by a water quality system. The method further includes determining, with an optimizer applied to the machine learning model, an optimal set of dosage parameters based on the first predicted particle-size distribution and adjusting the set of dosage parameters of the water quality system to the optimal set of dosage parameters.