AI in Process Industries

Wed, Nov 18

10 tickets left
CA$36.75Regular
  • Private

All ticket sales are final

About

Beyond Chatbots and Co-Pilots

Join a small-group working session for engineers, operators, and leaders exploring industrial AI and autonomous control.

You will leave with a clearer path from a promising model to a deployable industrial system.

This is not another generic chatbot or Copilot-style assistant discussion. We will focus on two distinct AI layers:
Physics AI: models that respect process behaviour and physical constraints to support monitoring, soft sensing, anomaly detection, and what-if analysis.

Physical AI: systems that perceive operating conditions and generate actions that controllers, equipment, or workflows can implement.

Across both layers, the relevant question has quickly evolved from whether AI can generate an output to whether the resulting action is safe, consistent, and right for the operational reality.

Together, we will work through:
- Where physics-based and data-driven AI create real operational value,
- Accuracy, consistency, and trustworthiness beyond a single benchmark,
- Governance, auditing, and accountability for AI-assisted decisions,
- Practical use cases across monitoring, optimization, and closed-loop control,
- The gap between a promising model and a deployable industrial system.

Bring a real challenge from your plant, control system, or AI initiative, so that we can work through how to constrain it with physics, validate it against operations, govern it with clear ownership, and integrate it responsibly into the control hierarchy.

If you build, operate, or govern safety-critical or energy-intensive systems, this practical, interactive, and confidential session is for you.

Speaker

The voice shaping the conversation

  • Oguzhan Dogru

    ​Oguzhan Dogru, Ph.D., P.Eng., is an Advanced Process Control leader with a decade of experience driving autonomous control and AI-driven transformation across the oil and energy sectors. Most recently, he led Support at CruxOCM, where he owned multi-million-dollar program budgets, led enterprise-scale AI initiatives that saved six to seven figures annually, and delivered 40% faster system startups, 50% fewer incidents, and 90% fewer operator interventions through the control frameworks he architected. His Ph.D. in Chemical Engineering from the University of Alberta laid the foundation for his work in reinforcement learning-based process control under sensory uncertainty, and continues to inform his design of adaptive control systems for complex industrial environments.

Get involved