Reduce Electrical/Fuel Operating Expenses & Boost Profits with Auto-AI
Process-control techniques for energy-consuming assets (boilers, compressors, etc.) are costly and complex—demanding time and resources that don’t guarantee results. (Sound familiar?)
It doesn't have to be this way!
This webinar provides guidance on reducing real-time energy costs by taking an operational edge AI/ML framework to democratize your analytics solutions. Learn how to implement this approach in a matter of minutes, all without learning any data science or scripting any code.
It doesn't have to be this way!
This webinar provides guidance on reducing real-time energy costs by taking an operational edge AI/ML framework to democratize your analytics solutions. Learn how to implement this approach in a matter of minutes, all without learning any data science or scripting any code.
Takeaways from this webinar:
- How maintenance/process engineers are adding practical, augmented AI intelligence to their PID techniques even in Industry 3.0 infrastructure and control systems
- How manufacturing citizen-data scientists are improving industry KPIs such as KwH-generated and natural gas consumed in real-time
- Configuring an accurate edge/AI data-driven operational baseline of your energy assets, while mitigating the manipulation of your single source of truth
- Ways to deploy an edge/AI operational digital twin ML model to forecast supply chain events
- How edge-based AI can automatically optimize your process-equipment parameters, reinforce a SMART dynamic set point in real time, and provide smart root-causal analysis/fidelity to industrial problem solving
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