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Biao Huang: New Research Opportunities 2011 Project
1: Control of Oil Sands Processes Research Associate (1 position), Postdoctoral Fellow (2 positions), PhD
(3 positions), MSc (3 positions), in collaboration
with Syncrude Canada Ltd., Suncor Energy Inc., and
NSERC Project description By 2015,
bitumen production from the oil sands in Alberta is forecast to make up 75%
of Canadian crude production, moving Canada from the world's eighth largest
crude oil producer to the fourth largest. While the economic benefits to oil
sands development are clear, sustainable development of this valuable
resource requires that environmental consequences be minimized. The search
for solutions to the environmental consequences of oil sands development
(e.g. water use, greenhouse gas emissions, tailings) has typically focused on
the processes that make up an oil sands operation. We propose to take 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, 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.
This project is aimed at developing solutions that will lead to innovative
estimation, control and optimization technologies. Specifically, solutions
for process data, instruments, soft sensors, and plant-wide operations are
proposed. These practical solutions are backed up by sound fundamental
research. Through this 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. Our integrated program will enable
collaboration with industry to convert research outcomes into implemented
solutions, and train highly qualified personnel with excellent job prospects.
Past
collaboration experiences with oil sands industries have shown excellent job
opportunities for the students, PDFs and Research Associates, not only in oil
sands industry but also in other industry sectors. Qualification: Research Associate: Must have completed a postdoctoral fellow or equivalence experience, and have experience in at least two of the following areas: system identification, experiment design, filtering and estimation, control performance monitoring, fault detection and isolation. Since this position involves a supervision role and frequent contact with industry, the successful candidate must possess a strong English communication skill. Postdoctoral Fellow: Within 5 years graduation from a PhD degree. Since this position involves a frequent contact with industry, the successful candidate must possess a strong English communication skill. PhD/MSc: A degree in Process Control, Control Systems, Automatic Control, or Chemical Engineering. Project 2: Optimization of
Manufacturing Process Automation in the Presence of Time-Varying Operating
Conditions PhD (2 positions), PDF (1 position), in
collaboration with Sherritt International Corporation,
Suncor Firebag, Agrium Inc. and NSERC Automatic control systems are critical to
manufacturing today because they allow for steady process operations,
consistent product quality, less waste, and control of emissions. However,
most current systems are not designed to optimally handle time-varying
operating conditions introduced by time-varying factors such as feedstock changes,
product grade changes, demand changes, multi-modes due to process
nonlinearities and even operator shift changes. The inability to finely
control processes can lead to lower product quality, additional waste, and
increased emissions of environmentally undesirable substances. As a result,
it is difficult to meet both increased product quality specifications and
more stringent environmental regulations - facts of life for industry today -
with conventional automation control. We propose to develop a set of tools
for improved process control of systems with time-varying characteristics.
The approach we will take is based on system identification and optimization
of a set of control laws including time invariant as well as time variant
control laws for a class of time-varying processes. Our objective is to
develop strategies to ensure product quality specifications while minimizing
control input energy. Time-varying system identification and process control,
particularly for the hybrid or switching systems, are areas of active
research. Our solution will be designed to leverage automation system
infrastructure from the existing to a foreseeable 10-year horizon so that it
can be implemented in the industrial distributed control systems (DCS)
without additional capital cost. This will make it possible to quickly apply
the technology to industry. Improved control reduces variability, allowing
processes to be operated closer to their setpoints
without violating constraints. This will lead to better product quality and a
reduced environmental footprint. Optimizing the control of time-varying
operations will make industry more efficient and more competitive in the
global marketplace. This project provides great opportunities
for gaining collaboration experiences with industries Qualification: A degree in Process Control, Control Systems, Automatic Control, or Chemical Engineering. Project 3: Dynamic
modeling for prediction of toxicity induced by pollutants in complex water
mixture PDF (1 position) PhD/MSc (1
position) A real-time cell electronic
sensor (RT-CES) array has recently been proven valuable for continuously
monitoring dynamic cytotoxicity responses of living
cells. With RT-CES technology, we have established a preliminary framework
for dynamic cytotoxicity modeling by applying
dynamic systems modeling theory and this dynamic model has shown a great
potential in modeling cell dynamics in the presence of toxicant and, more
importantly, predicting the cytotoxicity response
of the cells. The success of our preliminary study leads to this work. We
expect that this monitoring system has a great potential to be more reliable,
more accurate, more environmentally and ecologically friendly, and faster
than other conventional methods. The key technology to be used includes
dynamic RT-CES monitoring system, rigorous mathematical modeling, dynamic
predictions, and direct testing of the toxicant effect on living cells. Qualification: PhD/MSc: A degree in Process Control, Chemical Engineering, Biomedical, or Applied Statistics. Other Industrial PDF, PhD/MSc projects PDF Project There will be an on-going PDF position opening for a Syncrude Canada sponsored contract in the area of process control/monitoring/soft sensor. Background required: Process Control and good knowledge in Applied Statistics. The candidate must have strong communication skills in English.
PhD/MSc Project
Fault detection and isolation, soft sensor development for Syncrude Canada Ltd., Extraction Operations Background: Process Control or Chemical Engineering The project provides an excellent opportunity to direct contact with industry engineers/operators
For other projects, refer to Graduate Information Booklet
If interested, please send an email to biao.huang@ualberta.ca. |
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