Digital Twin Architecture for Wastewater Treatment

Lindström Project - Comprehensive Technology Stack Overview

🏭 Field Data
Sensors & Lab
🔗 Communication
Protocols & Gateways
🧠 Digital Twin
Models & Simulation
💾 Data Storage
Databases & Analytics
⚙️ Control Systems
PLC/SCADA/DCS
1. Data Sources
Field Instruments
Flow meters
Level sensors
Temperature sensors
Turbidity meters
Conductivity sensors
Laboratory Data
BOD/COD analysis
TSS/VSS measurements
Nutrient analysis (N, P)
Soft Sensors
Kalman filters
Virtual sensors
2. Communication
Field Protocols
4-20mA analog
HART protocol
Profibus DP
Ethernet/IP
Network Protocols
DNP3
BACnet
Gateways
Protocol converters
IoT gateways
3. Modeling Tools
Selected Platform
GPS-X (Hydromantis)
BioWin (EnviroSim)
WEST (DHI)
SIMBA# (ifak)
Custom Development
C++ (performance critical)
Rust (safety + performance)
MATLAB/Simulink
Model Types
ASM models (1, 2d, 3)
ML/AI models (Data-driven model)
Hybrid models
CFD models
4. Data Storage
Time Series
TimescaleDB
ClickHouse
Prometheus
Relational
SQL Server
MySQL
Oracle
Document/NoSQL
MongoDB
Redis (caching)
Elasticsearch
Dashboard (Data Analysis)
Grafana
Tableau
Plotly, Highchart
Power BI
5. Control Systems
PLC Systems
Allen-Bradley CompactLogix
Schneider Modicon
Mitsubishi FX series
SCADA/HMI
FactoryTalk View
Wonderware
Ignition (Inductive Auto.)
DCS Systems
Siemens PCS 7
ABB System 800xA
Honeywell Experion
Emerson DeltaV
Communication Protocols Comparison
OPC UA
✓ Secure, Platform-independent
✗ Complex setup
Modbus TCP
✓ Simple, Widely supported
✗ Limited security
MQTT
✓ Lightweight, IoT-friendly
✗ Not real-time
Selected Modeling Platforms
SUMO Simulation
✓ Customizable by User-Model option, Flexible
✗ Requires custom development
Python Custom
✓ Full control, Cost-effective
✗ Development time
Hybrid Approach
✓ Best of both worlds
✗ Integration complexity
Database Selection Criteria
InfluxDB
✓ Time-series optimized
✗ Limited JOIN operations
PostgreSQL
✓ ACID compliance, Extensions
✗ Time-series performance
SQL Server
✓ Enterprise features
✗ Licensing costs
🚀 Implementation Recommendations for Lindström Project
Phase 1 - Data Foundation: Start with existing field instruments, implement Modbus TCP communication, and set up InfluxDB for time-series data storage. Use Python for initial soft sensor development.
Phase 2 - Digital Twin Core: Deploy SUMO or develop custom Python models for biological processes. Implement OPC UA for standardized communication with Siemens PLCs.
Phase 3 - Advanced Analytics: Add machine learning-based soft sensors, implement predictive maintenance algorithms, and integrate with existing SCADA systems.
Phase 4 - Optimization: Deploy advanced process control algorithms, implement real-time optimization, and add digital dashboard for operators.
Key Considerations: Ensure cybersecurity measures (network segmentation, VPNs), plan for system redundancy, and maintain backup communication paths for critical control loops.

Selected Technology Stack for Lindström

Communication: OPC UA + Modbus TCP + MQTT | Modeling: SUMO + Python | Database: InfluxDB + PostgreSQL | Control: Siemens S7-1200 + S7.Net library+ C# GUI