Jinfeng Liu, PhD

Professor, Faculty of Engineering - Chemical and Materials Engineering Dept


Professor, Faculty of Engineering - Chemical and Materials Engineering Dept
(780) 492-1317
13-269 Donadeo Innovation Centre For Engineering
9211-116 St
Edmonton AB
T6G 2H5


Area of Study / Keywords

Process Systems Engineering Mathematical and Molecular Modeling Energy Artificial Intelligence


Dr. Liu received the BS and MS degrees in Control Science and Engineering from Zhejiang University in 2003 and 2006, respectively. He received the PhD degree in Chemical Engineering from the University of California, Los Angeles in 2011. Before joining the University of Alberta in April, 2012, Dr. Liu was a postdoctoral researcher at the University of California, Los Angeles. 


Dr. Liu's research interests are in the general areas of process control theory and practice with emphasis on model predictive control, networked control systems, process monitoring, and optimal control of chemical processes, energy systems, wastewater treatment plants, agriculture irrigation systems and biomedical systems. The long-term goal is to develop techniques enabling sustainable smart plant operations, in which physical processes, computations, and communication are integrated to address plant-wide safety and environmental and economic considerations.

Keywords: Process Control, Model Predictive Control, Networked Control Systems, Moving Horizon Estimation, Optimization, Energy Systems, Wastewater Treatment Plants, Precision Irrigation, Biomedical


CH E 446 - Process Dynamics and Control

Introduction to process modeling and transient response analysis; design and analysis of feedback systems; stability analysis; process control applications; process control using digital computers. Prerequisites: CME 265, MATH 201 and 209. Corequisite: CH E 312.

CH E 573 - Digital Signal Processing for Chemical Engineers

Time and frequency domain representation of signals; Fourier Transform; spectral analysis of data; analysis of multivariate data; treatment of outliers and missing values in industrial data; filter design. Prerequisites: CH E 358 and 446.

CH E 663 - Optimal and Model Predictive Control

Intended for graduate students who are familiar with basic modern control theory. Solution methods for dynamical systems, stability theory, classical optimal control methods, model predictive control and its computational tools.

CME 481 - Colloquium I

Communication and oral presentations. Graded on a pass/fail basis. Prerequisite: 85 units completed or consent of instructor.

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