Digital Twin Solutions for Power Plants & Heavy Industry

We follow a seven-step framework to keep the scope clear, de-risk implementation, and ensure that the twin becomes part of daily operations—not just a one-off experiment.

Step 1 – Define the scope

We do not build a twin for the entire plant on day one. Instead, we select a specific unit or asset class such as:

• Single gas turbine

• Boiler or HRSG

• Main generator

• Critical pumps or compressors

For each unit, we define critical KPIs like heat rate, turbine inlet temperature (TIT), compressor efficiency, steam temperature and drum pressure, bearing health, and more.

Step 2 – Collect operational & engineering data

We bring together three major data domains:

• Real-time OT data:
  Temperatures, pressures, flow rates, RPM, vibration signatures, fuel consumption, etc., from SCADA/DCS/PLC systems.

• Historical records:
  1–3 years of archives from historians, fault logs, and maintenance reports.

• Engineering documentation:
  P&IDs, technical manuals, OEM curves, heat-balance diagrams, and efficiency maps.

Step 3 – Build the mathematical and AI models

We develop and then combine two model classes:

• Physics-based models:
  Thermodynamic cycles, heat-transfer equations, mechanical efficiency, fluid dynamics, and OEM performance curves give us an “ideal” performance baseline.

• Machine-learning models:
  Time-series forecasting (e.g., LSTM), boosting models, and autoencoders learn from real plant history to predict fouling, degradation, and abnormal behavior.

• Hybrid Digital Twin:
  The hybrid twin merges physics and ML so that it matches real behavior under real conditions, not just nameplate ratings.

Step 4 – Connect the twin to live data

We integrate with your existing systems using industrial protocols:

• OPC UA, MQTT, or Modbus connections to SCADA/DCS/PLCs

• Historian feeds for contextual and delayed data

• Tuned update frequencies (e.g., every second for turbines, every few seconds for boilers, every minute for balance of plant)

Step 5 – Build the simulation & prediction engine

The engine continuously calculates:

• Expected efficiency and heat rate

• Degradation levels and fouling indicators

• Remaining Useful Life (RUL) of critical components

• Fuel-optimization scenarios under different load distributions

• Failure probabilities within defined time horizons (hours, days, weeks)

Step 6 – Design the Digital Twin dashboard

We deliver live dashboards that show:

• Real-time performance:
  Load vs. fuel usage, temperature profiles, pressure maps, efficiency indicators.

• Asset condition:
  Bearing and blade health indices, vibration spectra, and other condition monitoring metrics.

• Predictive alerts:
  Days-to-failure counters, high-risk anomaly notifications, and recommended actions.

• Optimization insights:
  Suggested load setpoints, fuel-saving modes, and stress-reduction strategies.

Step 7 – Enable your teams

We close the loop with people and process:

• Hands-on workshops for operations and maintenance

• “What-if” simulation sessions for engineering teams

• Cost-savings and ROI dashboards for management

• Standard operating procedures (runbooks) based on twin insights