About

The Computer Process Control Laboratory (CPC Lab), located in CME 1-112, provides students with hands-on experience connecting process-control theory with real industrial equipment. Through its experimental stations, the lab covers a broad range of topics, from instrumentation and automation to process dynamics, control theory, and optimization.

Students explore the fundamental elements of process control, including process measurement, instrumentation, control valves, feedback control, controller tuning, and single and multi-loop control strategies. The lab also supports advanced study in process modelling, system identification, state estimation, model predictive and optimal control, process monitoring, and process-systems engineering.

Please feel free to explore the lab’s facilities, learn about the courses that use them, and get to know the faculty and personnel who support the CPC Lab.

Facilities

The CPC Lab has a collection of pilot-scale stations that give students direct experience with the equipment, measurements, and interactions found in industrial process-control systems. The facilities support experiments with flow, level, temperature, pressure, valve behaviour, mixing, and reaction monitoring.

The Heated Tank System, Four-Tanks System, Valve Characterization unit, Batch Reactor, and Multi-loop System range from fundamental single-loop experiments to coupled, multivariable, cascade, recycle, and model-based control applications. Check the stations above to explore the equipment, instrumentation, control variables, and educational uses of each station.

Courses

The CPC Lab supports undergraduate and graduate courses that connect mathematical models and control concepts with real process behaviour. Students collect and analyse experimental data, identify dynamic models, configure controllers, and evaluate performance under realistic interactions, disturbances, delays, and nonlinearities.

CH E 446 establishes the foundations of process dynamics and feedback control, while CH E 472 develops mechanistic and data-based dynamic models. Advanced study continues through signal and industrial-data analysis in CH E 573 and digital, multivariable, optimal, and model predictive control in CH E 576.

People

Professor & Lab Supervisor

vprasad@ualberta.ca University profile

Fields of study

  • Process Systems Engineering
  • Process Control and Systems Engineering
  • Mathematical and Molecular Modeling
  • Artificial Intelligence and Machine Learning
  • Data Analytics
  • Reaction Engineering and Catalysis

Recent publication

Predicting solar cell efficiencies using historical data from a manufacturing process

The Canadian Journal of Chemical Engineering, 2025

Professor

jinfeng@ualberta.ca University profile

Fields of study

  • Process Control and Systems Engineering
  • Model Predictive Control
  • Optimal Control with Machine Learning
  • Data Analytics and Applied Machine Learning
  • System Identification
  • Irrigation Engineering