Process Dynamics and Control with Python

Source: AIChE
  • Course ID:
  • Type:
    eLearning (online) Course
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  • Duration:
    16 hours
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Ready to develop the control skills valued in today’s process industry? 

This online course is a hands-on approach to learning process control and systems dynamics—skills in high demand in the process industry according to an NSF-sponsored report. Industrial control expert John Hedengren will lead you through a complete start-to-finish process of physics-based modeling, data-driven methods and controller design.

If you have some knowledge of computer programming, take this course. You’ll walk through several introductory topics that develop your understanding of numerical methods in process control while you build skills you can apply to solve real problems in your operating plant.

Turn process control theory into practice   

Throughout this course, you’ll gain hands-on experience that will improve your performance as a control engineer, instrument engineer or process engineer or as a developer of digital twins or automation strategies for new processes. You’ll increase your confidence through unique access to interactive simulations and control challenge problems. You’ll take your proficiency to the next level using computer-aided tools, video solutions, step-by-step instructions, and hardware exercises. In addition, your understanding of simulation and theory will be reinforced as you develop a dynamic model and controller with real data using an Arduino-based Temperature Control Lab.

You have the mathematical skills from dynamics and control required to solve idealized textbook problems. Take the next step in this online course where you’ll gain new knowledge and be equipped to apply it to control challenges in today’s demanding process industry.

If you have some experience with Python already, you may consider taking ELA270: Introduction to Python for Chemical Engineers and ELA271: Introduction to Data Science with Python


John Hedengren

John Hedengren is a Professor at Brigham Young University and leads the PRISM group with interests in combining data science, optimization, and automation. He earned a doctoral degree at the University of Texas at Austin and worked 5 years with ExxonMobil Chemical as an Advanced Control Engineer prior to joining BYU in 2011. His industrial control experience with PLC and DCS systems includes OPTO22, Honeywell TPS/TDC3000, Experion system, OPC, and Modbus. His area of expertise is in process dynamics, control, and optimization with applications in fiber optic monitoring, automation of oil...Read more



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