Biao Huang, PhD

Professor, Faculty of Engineering - Chemical and Materials Engineering Dept


Professor, Faculty of Engineering - Chemical and Materials Engineering Dept
(780) 492-9016
12-265 Donadeo Innovation Centre For Engineering
9211-116 St
Edmonton AB
T6G 2H5



Biao Huang received his PhD degree in Process Control from the University of Alberta, Canada, in 1997. He held MSc degree (1986) and BSc degree (1983) in Automatic Control from the Beijing University of Aeronautics and Astronautics. He joined the University of Alberta in 1997 as an Assistant Professor in the Department of Chemical and Materials Engineering, and is currently a Full Professor, NSERC Senior Industrial Research Chair in Control of Oil Sands Processes, and Alberta Innovates Industry Chair in Process Control. He is an IEEE Fellow, Fellow of the Canadian Academy of Engineering, and Fellow of the Chemical Institute of Canada. He is a recipient of a number of awards including Alexander von Humboldt Research Fellowship from Germany, Best Paper award from IFAC Journal of Process Control, APEGA Summit Award in Research Excellence, and Bantrel Award in Design and Industrial Practice, etc. He has published 5 books and over 320 peer-reviewed journal papers. His research interests include: process control, data analytics, Bayesian inference, system identification, control performance assessment, fault detection and isolation, and soft sensors. He has applied his expertise extensively in industrial practice. He is currently the Editor-in-Chief for IFAC Journal Control Engineering Practice, Subject Editor for Journal of the Franklin Institute, and Associate Editor for Journal of Process Control. He has also recently served as Associate Editor for the Canadian Journal of Chemical Engineering, NOC Chair for 2015 IFAC ADCHEM and IPC Chair for 2017 AdCONIP.


Our research activities mainly focus on process control and data analytics. Specifically, the research activities in process control includes control performance assessment, state estimation, fault diagnosis and soft sensing. The research activities in data analytics include process identification, Bayesian inference, process monitoring, data-based optimization and machine learning. Our research outcomes have been widely applied in industrial processes particularly oil sands processes. 

Everyone in a process plant, from plant managers to engineers to technicians, relies on a massive amount of data, which plays a significant role in daily analysis and decision making. Common process control practice is to develop models based on data and then controllers with the aid of process knowledge. But as modern process data has increased in dimensionality, diversity and complexity, traditional analytical tools have been unable to keep up with this onslaught of complex data. High dimensionality of data and irregularities during data collection pose many challenges in modeling, analysis and control design, thereby casting serious doubt on the validity of traditional techniques. As a result, the process control research community is under ever increasing pressure to deliver analytic tools to cope with the challenges of the modern day practices of the process industries. Responding to this pressure and motivated by the real-life challenges faced by process industries, we endeavor to provide a solution to fundamental problems encountered in process identification, fault detection and soft sensing in the presence of high dimensionality and irregularities in modern datasets. Our research program will develop new analytics techniques by which this enormous amount of data can be fruitfully utilized, to achieve safe and intelligent process operations. 

Not only do we develop new techniques, but also we apply the techniques to industries. While the economic benefits to oil sands development are evident, sustainable development of this valuable Canadian resource requires that the costs of oil extraction as well as environmental consequences be minimized. Over the last decade, due to the global demand, the oil sands industry has undergone an unprecedented development accompanied by a significant increase of production costs. Market reality requires strategic oil sands development and calls for reduction of operation costs. However, the search for solutions to the sustainable oil sands development (e.g. water use, greenhouse gas emissions, tailings) has focused on the processes that make up the oil sands operation. Our research takes a different approach by focusing on the systems that control these processes. Process control systems are critical because they allow for steady and safe process operations, efficient production, consistent product quality, less waste, and better control of emissions. Achieving better process control in the oil sands industry is not simply a matter of importing technologies from other industries. Control systems in the oil sands industry face unique challenges. Our research is aimed at developing solutions that will lead to innovative estimation, monitoring and data-mining technologies in addition to supporting and improving the established solutions for the industry. Specifically, solutions for sensors, process safety and optimal operation are developed. These practical solutions are backed by sound fundamental research. Through our research program, we will also extend the breadth and depth of Canada's oil sands expertise by creating a pool of highly trained engineers in process systems engineering and control. Our integrated program will enable collaboration with industry to convert research outcomes into implemented solutions, and train highly qualified personnel with excellent job prospects. 

Keywords: Process Control, System Identification, State Estimation, Fault Detection and Diagnosis, Data Analytics, Bayesian Methods, Control Performance Monitoring, Soft Sensors


CH E 576 - Intermediate Process Control

Digital and multivariable process control techniques; discrete-time analysis of dynamic systems; digital feedback control; Kalman filter and linear quadratic optimal control; model predictive control. Prerequisite: CH E 446 or equivalent.

Winter Term 2021
CH E 662 - Process Identification

Selected topics related to empirical modelling of process systems are undertaken. Emphasis on time-series based modelling theory and techniques, (e.g., nonparametric, parametric, spectrum analysis, nonlinear, and closed-loop identification methods), model validation, experimental design, and applications in forecasting, analysis, and control.

Winter Term 2021

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