AI for Process Optimization and Advanced Control

Course Description:

This course provides process engineers with a comprehensive understanding of how artificial intelligence technologies can be applied to process optimization and advanced control within industrial facilities. The course begins with a detailed examination of traditional process control architectures, including regulatory control loops, advanced process control systems, and real-time optimization frameworks. Participants then explore how machine learning models and data-driven analytics can be applied to improve process performance, identify nonlinear process relationships, and support predictive operational strategies.

Learning Objectives:

Upon completion of this course, participants will be able to:

1. Explain the structure and function of layered industrial process control architectures, including regulatory control systems, Advanced Process Control (APC), and real-time optimization frameworks.
2. Evaluate the limitations of traditional process control and first-principles modeling approaches when applied to complex, nonlinear industrial processes.
3. Describe how artificial intelligence and machine learning techniques can analyze large industrial datasets to predict process behavior and support operational optimization.
4. Identify how AI-based soft sensors and virtual analyzers can estimate critical process variables such as product composition and reaction conversion using real-time process measurements.
5. Analyze how AI-driven predictive models support real-time process optimization by forecasting operational outcomes and recommending improved operating conditions.
6. Assess how AI technologies integrate with distributed control systems (DCS), plant historians, and advanced process control platforms within industrial automation architectures.
7. Recognize cybersecurity, safety, and regulatory considerations associated with implementing AI-based analytics in industrial process environments, including the importance of maintaining independence between optimization systems and safety instrumented systems.
8. Apply engineering reasoning to evaluate AI-assisted optimization scenarios in refinery distillation systems, catalytic reactors, and plant-wide energy management applications.