In collaboration with IIT-BHU and NCL Pune, C-DAC Patna is spearheading two landmark Digital Twin implementations funded by MeitY, Government of India. This initiative integrates advanced simulation, real-time analytics, and AI-driven insights to optimize operations in chemical process industries and urban infrastructure management. By virtually replicating complex systems, these Digital Twins enable predictive monitoring, risk mitigation, and scenario simulation, empowering decision-makers with actionable intelligence across industrial and smart city domains.

These pilots represent a strategic leap in India’s technological self-reliance, offering scalable, secure, and interoperable solutions tailored for high-impact sectors.

Process Safety Monitoring – AI-driven real-time monitoring and alerting to prevent hazardous conditions such as thermal runaway, pressure surges, or equipment failure.

Operations Optimization – Continuous tracking and adjustment of process parameters to improve yield, reduce energy consumption, and minimize waste.

Predictive Maintenance – Early detection of equipment wears and anomalies to schedule maintenance proactively, reducing unplanned downtime.

Quality Control Automation – Integration of sensor data and AI models to maintain consistent product quality through real-time process adjustments.

Intelligent Traffic Management – AI-based traffic signal control and adaptive routing to reduce congestion and improve mobility.

Environmental Monitoring & Alerts—A real–time visualization and notification system for air quality, noise levels, and other urban pollutants.

Flood Prediction & Management— Based on rainfall and drainage systems data, AI models to issue early flood warnings and support response planning.

Urban Infrastructure Monitoring – Continuous surveillance of critical infrastructure (roads, bridges, utilities) for operational maintenance

 Multi-Domain Application – Unified platform supporting industrial process optimization and urban infrastructure management.

  Real-Time Data Integration—Seamless ingestion of live sensor data enables continuous system monitoring, analysis, and control.

  Predictive Intelligence – AI/ML models for forecasting critical parameters like traffic density, pollutant levels, flood probability, and chemical process deviations.

  Simulation & What-If Analysis – Interactive scenario testing for emergency preparedness, operational policy assessment, and optimization strategies.

  Edge-AI Capability – Localized processing at the edge reduces latency, ensures faster response times, and minimizes cloud dependency.

  Immersive 3D Visualization – Intuitive 3D interfaces powered by Unreal Engine deliver actionable insights via interactive dashboards and virtual walkthroughs.

  Scalable & Interoperable – Modular architecture with plug-and-play compatibility across SCADA, GIS, and existing Smart City platforms.

  Data Acquisition Layer: Integration of heterogeneous sensors (temperature, pressure, flow, air quality, traffic cameras, etc.) via standard protocols (MODBUS, OPC-UA, MQTT).

  Data Platform: Cloud-agnostic architecture supporting Apache Kafka for data streaming, PostgreSQL with PostGIS for spatial data, and TimeScaleDB for time-series storage.

  Analytics Engine: AI/ML pipelines developed in TensorFlow, PyTorch, and Scikit-learn for predictive modeling and anomaly detection.

  Visualization & Interaction: Built on Unreal Engine with CesiumJS for 3D geospatial rendering; web dashboards using ReactJS and D3.js.

  Integration APIs: RESTful and GraphQL APIs to connect with external systems (e.g., SCADA, GIS, Smart City Command Centers).

  Compute Infrastructure: Hybrid deployment supporting on-premise servers with NVIDIA GPUs and edge devices with Jetson Nano/Orin for localized inference.

  Security Stack: TLS encryption, JWT-based authentication, RBAC (Role-Based Access Control), and integration with GovCERT guidelines for secure deployment.

• Operating Systems: Ubuntu Linux (Server/Edge Nodes); Windows 10/11 (Visualization Clients)

• Hardware: Industrial-grade edge gateways (Jetson Nano/Orin), GPU servers (NVIDIA A100/RTX 6000), IoT gateways, sensor network infrastructure

• Software Stack:

Backend: Python, Node.js

Frontend: ReactJS, Unreal Engine

Databases: PostgreSQL, TimeScaleDB

AI Frameworks: TensorFlow, PyTorch

Integration: REST/GraphQL APIs, MQTT brokers

Aditya Kumar Sinha

Scientist G, C-DAC Patna

infocdacpatna@cdac.in