Corrosion Detection using Surface Images for a large Petrochemical Plant

Business Objective: Processing of huge numbers of images using Computer Vision for predicting the severity of corrosion or fatigue and recommending preventive actions. Key Solution:
  • A pilot was conducted using surface images of the assets and structures, taken at different times during the inspection, maintenance or shutdown/ turnaround in last few years.
  • The images and associated data were ingested into tcg mcube and placed in the contextual form based on the classification of assets and structures.
  • Part of the images and associated data were used to train the computer vision AI model, while rest was used for the testing.
  • It was observed that the condition of bad images and related severity of corrosion or fatigue were predicted with an accuracy of more than 90%.
  • A recommendation engine was also developed using associated data points and business feedback/rules.
  • The engine provided recommendations like repaint or part/ section repair or on high severity, even replacement.
  • Data Used
    • 9K+ Images of equipment (tanks, heat exchangers, columns, reactor, separator/ surge drum), pipes, valves, fittings, flare stack, pipe support, structures of plant.
  • Technology Used
    • Digital Camera, User Interface to upload images to tcg mcube
    • tcg mcube – Computer Vision (R-CNN, DNN)
  Business Impacts & Outcomes:
  • Severity of corrosion or fatigue to the assets or structures was reported based on the images
    • Low severity (closer to 0.10) means, not much corrosion or damage has occurred to the metallic surface
    • Medium severity (closer to 0.70) signifies the asset/structure needs repair
    • High severity (closer to 0.99) means replacement is required
  • Based on level of severity, insights were provided on factors contributing to the damage (RCA)
  • Recommendations like ‘No Action Needed’, ‘Repair’ or ‘Replacement’ was also provided through the system

Remote Plant Monitoring System for a Large Petrochemicals Manufacturer

Business Objective

Remotely monitor real-time health of the various systems in the plant

Key Solution

  • Built a virtual plant as a digital replica of the real plant
  • Enabled monitoring of plant health in real-time with key KPIs, equipment condition and parameters operating range
  • Detailed monitoring of all the systems within the plant – feedstock, cracking, storage, polymerization, bagging, utilities etc.
  • The solution diagnosed the problem of equipment with bad health or those operating at off-limit parameters; identified the root cause and recommended actions, enabling the operator to take quick and valuable plant floor level decisions

Business Impacts & Outcomes

  • Improved production and yield
  • Reduced energy consumption
  • Improved uptime of plant and assets
  • Maximized operational life of plant assets

Business Impacts & Outcomes:

  • Improved production and yield
  • Reduced energy consumption
  • Improved uptime of plant and assets
  • Maximized operational life of plant assets