Industrial AI Fundamentals

The Industrial AI Fundamentals course provides a comprehensive understanding of how Industrial AI can be leveraged to optimize and transform industrial processes. This course provides an understanding of what differentiates Industrial AI from off the shelf machine learning methods and exemplifies problems that it solves in various industrial settings to drive agility, guidance and automation. The course also includes case studies on real world examples of AI Implementation.

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Audience:

  • Managers, Executives driving digital transformation, sustainability and AI initiatives
  • Engineers, and anyone who is involved with Asset Optimization

Training Details

  • Course Id:

    IAI101

  • Duration:

    0.5 day(s)

  • CEUs Awarded:

    0.4

  • Level:

    Introductory

Benefits

  • Gain awareness and foundational knowledge on how to leverage different Industrial AI opportunities
  • Know how Industrial AI can help organizations quickly adapt to changing business conditions, uplevel the workforce and to drive operational efficiencies.
  • Understand how to prioritize Industrial AI programs relative to their expected ability to create business value, and how to measure that value.

Approach

Delivered through a blend of lectures, and case study discussions.

Tasks

Basic understanding of industrial processes and familiarity with programming concepts are beneficial.
No advanced AI or data science knowledge is required.
 

Agenda

  • Overview of AI
  • What is Industrial AI and its difference to AI
  • Industrial AI Value Levers
    • Key Industrial AI opportunities
  • Problems Industrial AI can solve
    • Agility:
      • Process Engineering - Hybrid Modeling
      • Power & Gas Demand Forecasting – Generate accurate load demand forecasts for the electrical/gas network
      • Renewable Energy Forecasting – Provide accurate power generation forecasts up to 35 days in the future
    • Guidance:
      • Real-time insights and Actionable guidance
      • Strategic Planning for Sustainability Pathways
      • Predictive Maintenance - Building predictive models to detect equipment failure
    • Automation:
      • Improved decision making in hydrocarbon exploration
      • Auto Routing and Auto Layout
  • Case Studies and Industry Applications
    • Real-world examples of AI implementation
    • Best practices
      • Selecting and cleaning data sets
  • Bring Your Own Model
    • Integrate your existing models into Industrial AI software applications

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Aspen Technology, Inc. awards Continuing Education Units (CEUs) for training classes conducted by our organization. One CEU is granted for every 10 hours of class participation.