📋 Case Study
Calibration of Lagos Metropolitan Water Network
Persistent model–field mismatch (>25% pressure error) due to undocumented pipe replacements and unaccounted demand growth
🏗️ Project Overview
Nigerian utility upgrading aging infrastructure across 12 zones
🎯 Challenge
Persistent model–field mismatch (>25% pressure error) due to undocumented pipe replacements and unaccounted demand growth
🔧 Design Approach
Two-phase calibration: (1) top-down demand multiplier tuning per zone; (2) bottom-up roughness adjustment using 87 pressure loggers and 14 flow meters
📐 Design Diagram
AI-generated project design illustration
📐 Key Calculations
Zone Demand Multiplier
Measured Avg. Flow / Modeled Avg. Flow
Result: 1.32–1.68
Corrects for undocumented population growth and illegal connections
C-value Adjustment
Hazen–Williams recalibration via EPANET Solver
Result: C = 92 → 78 (CI mains), C = 140 → 115 (PVC laterals)
Accounts for tuberculation and biofilm accumulation
📊 Results
Pressure RMS error reduced from 27.4 m to 3.8 m; model validated across 92% of monitoring points; enabled predictive leakage hotspot mapping💡 Lessons Learned
- •Field data granularity dictates calibration fidelity
- •Demand multipliers must be updated quarterly
- •Material-specific aging curves improve long-term model accuracy
✅ Key Takeaways
- 1Field data granularity dictates calibration fidelity
- 2Demand multipliers must be updated quarterly
- 3Material-specific aging curves improve long-term model accuracy
📐 Prerequisites
Understand these before this topic
➡️ Next Step
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🔗 Engineering Applications
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