Stevan Dubljevic, PhD

Assistant Professor, Faculty of Engineering - Chemical and Materials Engineering Dept


Assistant Professor, Faculty of Engineering - Chemical and Materials Engineering Dept
(780) 248-1596
13-265 Donadeo Innovation Centre For Engineering
9211-116 St
Edmonton AB
T6G 2H5



Stevan S. Dubljevic is an Associate professor at the Chemical and Materials Engineering Department at the University of Alberta. He received his Ph.D. in 2005 from the Henry Samueli School of Engineering and Applied Science at University of California in Los Angeles (UCLA), M.S. degree (2001) from the Texas A&M University (Texas), and the B.Sc. degree (1997) from the Belgrade University (Serbia). He held independent post-doctoral researcher position at the Cardiology Division of the UCLA's David Geen School of Medicine (2006-2009). He is the recipient of the American Heart Association (AHA) Western States Aliate Post-doctoral Grant Award (2007-2009) and the recipient of the O. Hugo Schuck Award for Applications, from American Automatic Control Council (AACC) 2007. His research interests include systems engineering, with the emphasis on model predictive control of distributed parameter systems, dynamics and optimization of material and chemical process operations, computational modelling and simulation of biological systems (cardiac electrophysiological systems) and biomedical engineering. He is the reviewer for the IEEE Transaction on Automatic Control, IEEE Transaction on Control Systems Technology, Automatica, Industrial & Engineering Chemical Research, International Journal of System Science, American Control Conference, Conference on Decision and Control, and program coordinator for the AIChE Annual Meetings (2014).


The large scale distributed parameter systems modelling, control and operational maximization have been an area of interest to both academic and industrial researchers. Bridging the time and length scales in these systems, from the standpoint of modelling and parameter identification that serve the purpose of prediction and control is our main research focus. To address the issues of distributed parameter systems modelling and control our research has focused on process control, dynamics and optimization, process operations, constrained optimal control of transport-reaction processes (tubular, packed bed reactors, plug flow crystallizers), and computational modelling and simulation of cardiac electrophysiological system and industrial relevant process applications (material processing - crystal growth, mixing, thin films processing, Li-ion batteries, and gas & oil industrial processing). 

In the realm of materials processing, our research program is aimed at systematic development of the process controller synthesis for semiconductor crystal processing that can cope with both the intrinsic transport-reaction process (growing domain and moving interface) and/or engineered (moving boundary sensing/actuation) time varying model features with simultaneous inclusion of optimality, stringent performance characteristics and constraints on allowable energy for control purposes. In particular, we address three different industrially relevant semiconductor processes modelled by parabolic partial differential equations (PDEs) within the unifying framework of model modal predictive control (MMPC), which describe 

  1. Czochralski (CZ) crystal growth process with moving actuators/sensors;
  2. Bridgman crystal growth process; 
  3. the melted zone refining process with boundary actuation and time-varying system's features. 

Our group long-term objective is to completely address the question of an ultimate ``best'' possible controller synthesis for the crystal growth semiconductor manufacturing that should be implemented in industrial practice.  

The industry important issue of stringent crystal grown radius control and crystal uniformity in the CZ crystal growth process, the striation and radial segregation in the Bridgman process, reduction of impurities in the zone refining process, and time processing reduction with minimization of energy utilized will be achieved with this proposed program controller synthesis.

Pipeline Water Network monitoring: In this are of my research we are focused on development of deployable autonomous swimming and crawling robots to provide free leak and integrity of the water system in facility or large scale piping/channel manifolds. In particular, we employ deep learning and training on the big data structures utilized for real-time and permanent monitoring of the facilities and/or pipeline or water channel network systems.

Mixing: An understanding of viscous mixing is of considerable technological importance in the context of materials processing, reactive and non-reactive polymer processing, food processing and/or stabilization of hazardous waste. In most cases mixing is to be controlled and enhanced, while in few cases suppressed. Mixing barriers are the consequence of particular time varying flow features which can be identified by the framework of Lagrangian coherent structures (LCS). We explore fully nonlinear time-varying flows and we address the identification, tracking and stability of transport induced barriers in melt flows related to crystal growth processes, as well as in the case of pollutants transport (fume, dust, or toxic chemicals released from an oil production plant) by investigation of the wind flow field. This technique is successfully employed in the monitoring of water resources in the San Francisco Bay area. The mixing barriers are of extreme importance for pollution monitoring.

Along the same line, the mixing and modelling of biomass torrefaction reactors is explored and validated with realistic torrefaction reactor data. This research effort extends to our interest in mixing of particulate flow given by the auger reactor model of a torrefaction reactor. 

Solar & Geo-Thermal: The recent advances in design of zero-net energy homes is explored on actual example of Calgary Drake Landing Solar Community which is explored by integrating solar-geothermal (borehole)-gas infrastructure and operations. We provided novel insight by accounting for distributed and seasonal changes as well as for the daily fluctuations in power demand. 

Cardiac electrophysiology-One of the venues of our research program is the control and computational modelling of cardiac electrophysiological system. In particular, we aim at exploring the possibility to suppress alternans by dynamic control methods in human ventricles, and possibly to prevent sudden cardiac death by addressing the alternans annihilation from a control point of view and accounting explicitly for the practical implementation of the controllers.

Keywords: Automation, Materials Processing, Process Control, Robotics in Process Systems, Energy renewable systems, Bio-systems, Oil/gas well completions (drilling, multiphase flows), Monitoring of oil wells, Biochar, Cardiac systems control (pacemakers & defibrillators) and monitoring , Solar & Geo-Thermal systems  


CH E 472 - Modelling Process Dynamics

Mechanistic and empirical modelling of process dynamics; continuous- and discrete-time models; model fitting and regression analysis. Corequisites: CH E 314, 318 and 345. Credit cannot be obtained in this course if previous credit has been obtained for CH E 572.

Winter Term 2021
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.

Winter Term 2021

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