Biao Huang, PhD

Area of Study / Keywords

Process Control Oil Sands Energy Automation Machine Learning Data Analytics System Identification Fault Detection

About

Biao Huang (Ph.D., PEng) joined the University of Alberta in 1997 as an Assistant Professor, promoted to Associate Professor in 2001, and full Professor in 2003. He is currently NSERC’s Senior Industrial Research Chair in Control of Oil Sands Processes, Fellow of the Canadian Academy of Engineering, Fellow of the Chemical Institute of Canada, and Fellow of IEEE. He also served as the Alberta Innovates Technology Futures’ Industry Chair in Process Control. He obtained a Ph.D. degree in Process Control from the University of Alberta in 1997. He received an MSc degree (1986) and a BSc degree (1983) in Automatic Control from the Beijing University of Aeronautics and Astronautics. He is a recipient of Germany’s Alexander von Humboldt Research Fellowship, Canadian Chemical Engineer Society’s Syncrude Canada Innovation Award, D.G. Fisher Award and Bantrel Award in Design and Industrial Practice; APEGA’s Summit Research Excellence Award; University of Alberta’s McCalla and Killam Professorship Awards; Petro-Canada Young Innovator Award; AsTech Outstanding Achievement in Science & Engineering Award; Best Paper Award from Journal of Process Control in the category “methodology/theory” for the period 2002 to 2005. He is currently the Editor-in-Chief for the journal Control Engineering Practice, Subject Editor for Journal of the Franklin Institute, Associate Editor for Journal of Process Control, and Editorial Board member for the Canadian Journal of Chemical Engineering and Journal Chemometrics and Intelligent Laboratory Systems. He is active in serving international societies including IFAC and IEEE.

Research

Our research activities mainly focus on process control, machine learning 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. 

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, Machine Learning, Bayesian Methods, Control Performance Monitoring, Soft Sensors

Teaching

Currently teaching:

CHE576 - Intermediate Process Control

CHE662 - Process Identification

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.

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