System Pressure
48.3 psi
▲ 1.2% vs yesterday
Energy Cost / Day
$2,847
▼ 6.4% TOU optimized
NRW Rate
18.3%
→ Threshold 20%
Pump Stations
7/8
⚠ P3 maintenance
🗺 Zone Status
Click any card to view its timeseries data
📉 24h Demand + Pressure — Riverdale Metro
⚡ TOU Energy Cost
📋 Recent Activity
🔁 Recent Runs
| Run ID | Example | Solver | Obj Value | Iters | Duration | Status | Action |
|---|
🗺
Click any network
node to inspect
node to inspect
🔗 Modelica / Domain Component Registry
| ID | Type | Class | Ports | Parameters | Equations | Status |
|---|
📂 Data Files
⊕ Import New Data
📥
Drop files or click to browse
.npy · .csv · .json · .xml · .inp (EPANET)
👁 Preview — Select a file
| Metric | Value |
|---|---|
| Select a data file | |
⚗️ Preprocessing Pipeline
🔄 Workflow Runner — Riverdale Metro
📋 Step Detail — —
Click a step or run the workflow.
📊 Step Metrics
Run a workflow to see metrics.
🖥 Execution Log
⚙️ OCP Configuration
📉 Solver Progress
IdleConfigure and press Solve to start.
—
Iteration
—
Objective
—
‖∇f‖
—
CPU (s)
📊 State Trajectory
🎛 Control Schedule
Total Energy
3,421 kWh
▼ 8.2% vs baseline
Cost Saved
$412
▲ TOU shift
Min Pressure
32.1 psi
→ Min 28 psi
Pressure Deficit
0 nodes
✓ All satisfied
📉 Demand + Pressure (EPS)
⚡ Pump Energy by Hour
🍩 Water Balance
📋 Pump Schedule — Optimized vs Baseline
🧮 Zone Hydraulic Summary
📊 Scenario Comparison
| Metric | Riverdale Metro | Zone 5 MPC | Reservoir Alloc | CasADi OCP | Hydro System | Raven Hydrology |
|---|
🖥 System Console
🚀 Atlas Platform Hub
9 engineering frameworks · each with a quantified demo solution · powered by CasADi, RTC-Tools, Raven & Modelica
💧
Atlas Water Networks
Demo solution — Riverdale Metro Water District
Urban water distribution OCP integrating EPANET hydraulic simulation with CasADi NLP pump scheduling, zone-level MPC real-time control, and NRW diagnosis across networks up to 100k nodes.
Pump scheduling MILP / NLP
EPANET 100K-node hydraulics
Pressure zone MPC
NRW analysis
🏔
Atlas Reservoir Management
Demo solution — Multi-District Reservoir Allocation
Multi-reservoir LP yield optimisation with level-pool routing, inter-district water trading, Tennant environmental flow compliance, FEWS PI-XML data exchange, and 30-year planning horizons.
LP yield optimisation
Level-pool routing
E-flow compliance
Inter-district allocation
⚡
Atlas Hydropower Dispatch
Demo solution — Multi-Unit Hydro System + Pumped Storage
Multi-unit hydro OCP with RTC-Tools goal programming, pumped-storage TOU arbitrage, unit commitment MILP, reservoir level routing, and Tennant e-flow constraints over 48-hour rolling horizons.
RTC-Tools dispatch OCP
Pumped storage arbitrage
Unit commitment MILP
E-flow / Tennant method
🌧
Atlas Hydrology & Flood
Demo solution — 7-Catchment Basin, HBV + HEC-RAS 2D Flood Routing
Semi-distributed rainfall-runoff using Raven HBV/GR4J per HRU, HEC-RAS 1D/2D flood routing across 18,500-cell domains, 30-year RCP4.5/8.5 climate scenarios, and SWMM urban drainage.
Raven HBV rainfall-runoff
HEC-RAS 1D/2D routing
30-yr climate scenarios
Flood inundation mapping
⛏
Atlas Mine Water Balance
Demo solution — Gold Mine TSF + Underground Dewatering OCP
Mine water balance OCP with CasADi NLP — TSF freeboard control to ANCOLD guidelines, return water recycling, underground dewatering TOU pump scheduling, heap leach optimisation, and TDS evaporation tracking.
TSF freeboard OCP (ANCOLD)
UG dewatering scheduling
TOU pump cost minimisation
Heap leach water balance
🌊
Atlas Polder / Canal RTC
Demo solution — Woudagemaal Drainage System (Friesland, NL)
Dutch-style lowland polder real-time control using lexicographic GoalProgramming OCP and Modelica hydraulic components — P1 flood safety → P2 boezem service level → P3 TOU pump energy cost.
Lexicographic goal programming
Mixed-integer polder control
Tidal boundary OCP
Modelica acausal components
🏭
Atlas Wastewater Treatment
Demo solution — Ashbridges Bay WWTP, Toronto
Batch MILP scheduling under Ontario TOU tariffs. Shifts aeration load to off-peak, saves 13% energy without changing treatment process.
Live demo
MILP Scheduling
TOU optimisation
🏥
Atlas Asset Health
Condition monitoring, predictive maintenance and anomaly detection for pumps, turbines, dam embankments and pipe networks using ML and physics-based models.
SCADA anomaly detection
Pump curve degradation
Predictive maintenance
🤖
Atlas Intelligence
Embedded AI analyst for scenario generation, anomaly explanation and natural-language query across all Atlas framework domains — not a chatbot, a domain-aware engineering assistant.
Natural-language query
Scenario generation
Cross-domain synthesis
⚙️ Atlas Core Engine — System Status
CasADi / IPOPT NLP Solver
Available · v3.6.3
SciPy SLSQP (Linear fallback)
Available · v1.11
NumPy Rule-Based (backup)
Available
FastAPI REST endpoint
localhost:8000 · OK
Raven HBV Hydrological Model
7 catchments active
HEC-RAS 2D Solver
18,500 cells · warm start
EPANET Hydraulic Simulator
100K nodes · steady-state
Atlas Asset Health (ML)
Not installed
🔭 Capability Roadmap — Specified & Ready to Build
🌊 Flood Control vs. Revenue Pareto
Spring freshet trade-off: epsilon-constraint LP sweep produces a real Pareto frontier. Tells clients exactly how much revenue they give up per unit of flood margin — "how much revenue for how much safety."
Pareto LP
2 objectives
✓ Design-ready · 2–3 days
🔧 Maintenance Window Scheduling
Extends the unit-commitment MILP: binary mu,w selects the cheapest maintenance week per unit from eligible windows. Forces unit offline only during its chosen week — minimises lost revenue from downtime.
MILP binary
unit commitment
✓ Design-ready · 1–2 days
📋 Recent Scenarios
📊 Data Loaded
Water network files
12 files
Hydro time series
8,760 h/yr · 3 units
Raven hydrology data
30yr · 7 catchments
Mine water balance
168h OCP horizon
🎯 Solution Brief
The one-screen pitch for each digital twin — idea, challenge, solution, proof, and what's under the hood.