River Digital Twin for Water Quality Prediction
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Solution Overview
Problem
Current methods for monitoring river water quality are inadequate as they rely on traditional techniques that require frequent sampling, are time-consuming, and cannot predict fluctuations in water quality effectively, especially due to varying river ecosystems and multiple pollution sources.
Innovation Solution
A processor-implemented method and system that predicts river water quality by receiving a river layout, dividing it into segments based on input and output sub-streams, river characteristics, and environmental parameters, and using multi-criteria decision algorithms, search algorithms, classification algorithms, and physics-based models to simulate water quality indicators and determine necessary interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sampling methods are used to monitor river water quality, then water quality parameters can be measured, but the process is time-consuming and requires frequent manual sampling
Solution Approach 1:
The patent creates a virtual copy (digital twin) of the river system that replicates physical water quality conditions through simulation. This digital model allows continuous monitoring without physical sampling, eliminating time loss while maintaining measurement accuracy through physics-based models and sensor data integration
Solution Approach 2:
The patent replaces manual mechanical sampling operations with an automated computational system. The digital twin uses algorithms and simulations to substitute human-driven sampling processes, enabling continuous monitoring without the time constraints of manual field operations
2Loss of information
If traditional monitoring techniques are used, then water quality data can be collected, but the methods cannot predict actual fluctuations and require sufficient sample sizes
Solution Approach 1:
The digital twin performs preliminary simulations to predict water quality fluctuations before they occur in the physical system. By modeling various scenarios and stressors in advance, the system identifies potential quality changes without needing to collect large sample sizes to detect fluctuations
Solution Approach 2:
The system continuously compares digital twin predictions with actual sensor measurements, creating a feedback loop that refines the model's ability to predict fluctuations. This feedback mechanism enables accurate detection of water quality changes with minimal sampling
3Productivity
If scattered sampling points are used in conventional methods, then monitoring can be conducted, but the frequency of sampling is low and cannot capture dynamic changes
Solution Approach 1:
The digital twin serves multiple functions simultaneously: it monitors water quality across the entire river system, predicts future conditions, identifies pollution sources, and evaluates intervention strategies. This multi-functionality achieves comprehensive monitoring coverage and high measurement precision without requiring numerous physical sampling points
Data Source
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AI summary
The disclosure relates generally to methods and systems for predicting water quality of a river having a varying river ecosystem. Due to multiple and diverse factors, understanding and estimating the water quality of the river stream (river itself) is extremely and technically challenging. The present disclosure discloses a development of a river digital twin model utilizing a multi-modeling approach to comprehensively model the river and its varying ecosystems. The agents encompass entities that directly or indirectly introduce effluents or withdraw water from the river. Agents and their interactions are defined using a combination of behavior rules, correlations, and physics principles, creating the digital twin model that closely mimics the real river system. Physics-based equations are also employed in the present disclosure to capture the dynamics of the river, while relationships between different agents are established.