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Project Leader: Dr. Madiah Omar
Department: Chemical Engineering
Expertise: Process Control and Automation, Simulation and Modelling, Dry-Low Emission Gas Turbine, Artificial Intelligence, Machine Learning, Support Vector Machine, Predictive Maintenance, Induction Motor, Power Plant, Power Generation.

Digital-Twin and Predictive Maintenance Software of Dry-Low Emission Gas Turbine (DLE-TriP)
Centered Video Example

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TECHNOLOGY READINESS LEVEL:​ 7

BRIEF TECHNOLOGY
A complete package software of DLE gas turbine digital twin with the interactive and heuristic operation. DLE-TriP software includes Artificial Intelligence and the physical model of the DLE Gas Turbine, which will improve the user's technical know-how and predictive maintenance capability. Further, a real-time prediction is provided to monitor the turbine operation with the tripping probability.

DLE-TriP1.png

DLE-TriP2.png

PROBLEM STATEMENT & CURRENT ISSUES
  • No integrated monitoring and underutilized data for tripping prevention.
  • DLE gas turbine comes in a package from OEM. Limited enhancement can be proposed.
  • Expensive training and IoT package

USEFULNESS & APPLICATION
  • Provide efficient monitoring and tripping prediction in real time or using available data
  • Provide unlimited enhancement
  • ​Reduce the training and IoT package cost
Enhanced flexibility for any gas turbine setup with key features of tripping prediction and digital twinning from the simulator.

IMPACT OF THE PRODUCT
  • Similar product in the market only provide the monitoring capability and cloud storage
  • Specific prediction for DLE error is not covered and the probability to aid decision making is unavailable
  • Existing gas turbine diagnostic tool costs approximately 240,000 USD/year (per equipment), while the proposed software is 120,000 USD/year (all equipment).

MARKET POTENTIAL
  • Industry
  • Oil & Gas industry

INDUSTRY COLLABORATION
  • PETRONAS MLNG
  • UNITEN
  • Makhostia
  • Vellore Institute of Technology
  • IIUM
  • Serba Dinamik
  • SEKITO​

INTELECTUAL PROPERTY (IP)
Patent​        : PI2024001979
Copyright  : LY2024W00761
Copyright  : LY2024W00762​

Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia

Tel: 1-300-22-8887 | Enquiries: UCS Portal

Approval by Registrar of MOHE: KP(JPS)5195/US/7(9)

MOHE Registration Certification No: DU003(A)

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