报告题目:Robustness in Process Data Analytics

报告人：Prof. Biao Huang

Professor, Department of Chemical and Materials Engineering, University of Alberta, Canada

Fellow of the Canadian Academy of Engineering  

报告时间:2016年4月28日周四下午 14:00-15:00

报告地点:浙江大学玉泉校区工控新楼211

 

Modern process industry is awash with a huge amount of data. While useful information may be buried in some of data waiting for discovery, others may simply be noises. Extraction of information and knowledge discovery from data, particularly from day by day routine process operating data, is especially challenging. There are numerous issues such as data nonlinearity, non-Gaussian, high dimensionality, collinearity, multiple mode, outlier, missing measurement, etc., that must be considered during the information extraction process. This presentation will first give an overview on the general field of Data Analytics and then focus on one of the most important specific issues in Data Analytics, namely the irregularity of process data. The presentation is then highlighted by a discussion of state-of-the-art development of process data analytics to deal with robustness issues.

 

Biao Huang obtained his PhD degree in Process Control from the University of Alberta, Canada, in 1997. He had 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 Professor, NSERC Industrial Research Chair in Control of Oil Sands Processes and AITF Industry Chair in Process Control. He is a Fellow of the Canadian Academy of Engineering, Fellow of the Chemical Institute of Canada, and recipient of Germany’s Alexander von Humboldt Research Fellowship and Chinese Education Ministry’s Changjiang Scholar. He received a best paper award from Journal of Process Control as well as a number of other awards. Biao Huang’s research interests include: Bayesian inference, system identification, control performance assessment, fault detection and isolation, and soft sensors. He has applied his expertise extensively in industrial practice.