AI Digital Twins for Water Distribution Networks

Creating Digital Twins of Water Distribution Networks Using AI

• The research explores creating a digital twin of a water distribution network (WDN) using artificial intelligence (AI) for more efficient monitoring, analysis, and optimization.
• A digital twin will be developed using real-world WDN data, including sensor data, hydraulic models, and operational data.
• AI algorithms will calibrate hydraulic models, detect anomalies, predict performance, and optimize operations within the digital twin
• Validation will occur using historical and real-time WDN data to prove the benefits of AI-powered digital twins in WDN management.

Introduction to Water Distribution Network Digital Twins

• Water distribution networks (WDNs) deliver potable water to consumers including residential, commercial, and industrial.
• WDNs are complex systems with pipes, pumps, valves, and storage tanks
• Aging infrastructure, rising demand, and climate change contribute to increasingly challenge operation and management.
• A digital twin is a virtual, continuously updated representation of a physical system.
• Digital twins can monitor, analyze, and optimize the performance of physical systems.
• Potential uses for WDN digital twins:
  • Monitoring the network's current state.
  • Detecting anomalies and predicting failures.
  • Optimizing operations, reducing costs.
  • Evaluating the impact of network modifications.
• Artificial intelligence (AI) can develop and implement digital twins.
• AI algorithms can calibrate hydraulic models, detect anomalies in real-time data, predict future performance, optimize operations, and control assets.

Literature Review on Digital Twins

• Digital twins show promise in manufacturing, aerospace, and healthcare industries.
• Van Thienen et al. (2020) used a digital twin to detect leaks and optimize pump operations.
• Ostfeld et al. (2021) presented an AI-based framework for a WDN digital twin with data acquisition, hydraulic modeling, anomaly detection, and optimization modules.
• Lee et al. (2022) used machine learning to predict water demand and optimize pump scheduling.

Research Gaps

• Current digital twins commonly rely on hydraulic models that can be difficult to calibrate.
• How AI can be used for WDN management is still being studied and developed
• Real-world case studies are needed to demonstrate the benefits of using AI-powered digital twins for managing WDNs.
• Studies like this one are needed to develop an AI-driven digital twin of a real-world WDN that calibrates hydraulic models, detects anomalies, forecasts performance, and optimizes operations.

Methodology for Creating a Digital Twin

• Involves data acquisition, hydraulic model calibration, anomaly detection, performance prediction, optimization, and validation.
• Data Acquisition: Collect sensor data (flow rates, pressures, water levels, water quality), hydraulic models (EPANET models), and operational data (pump schedules, valve settings, maintenance records).
• Hydraulic Model Calibration: Use AI algorithms to calibrate the hydraulic model using the collected data, ensuring accurate representation of the real-world WDN behavior.
  • Genetic algorithm (GA) optimizes model parameters.
  • Objective function is the root mean squared error (RMSE), where $RMSE = \sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_i - \hat{y}_i)^2}$.
• Anomaly Detection: Uses AI algorithms to detect real-time data anomalies, often indicating problems.
  • An autoencoder is used to detect anomalies using the reconstruction error formula: $RE = ||x - \hat{x}||^2$.
• Performance Prediction: AI algorithms predict future WDN behavior.
  • A recurrent neural network (RNN) is implemented in this research to predict water demand.
• Optimization: AI algorithms optimize WDN operation to reduce costs, improve water quality, and increase reliability.
  • Reinforcement learning (RL) optimizes pump scheduling to minimize energy consumption while also maintaining water pressure and meeting demand.
• Validation: Verify developed digital twin using historical and real-time data, measure performance with metrics like accuracy, precision, recall, and F1-score.

Expected Results

• The AI-powered digital twin will accurately calibrate hydraulic models, detect anomalies in real-time data, predict future performance, and optimize operations.
• Benefits include reduced costs, improved water quality, and increased reliability.
• Insights will be valuable for water utilities interested in using digital twins to improve WDN management.