Introducing an IRC Project in Progress · 2018-02-28 · NSERC INDUSTRIAL RESEARCH CHAIR Control of...
Transcript of Introducing an IRC Project in Progress · 2018-02-28 · NSERC INDUSTRIAL RESEARCH CHAIR Control of...
NSERC INDUSTRIAL RESEARCH CHAIR
Control of Oilsands Processes
NEWSLETTER, IRC AND RELATED R&D ACTIVITIES, ISSUE 2, March 2013
Induction of Fellow of the
Canadian Academy of
Engineering, June 21, 2012.
CAE President (Left), Dr. Biao
Huang (Right)
Success Stories
Yu Zhao has developed a
water content soft sensor for an
inclined plate settler process,
which is used to reduce
residual water and fine solids
in oil sands extraction, through
collaborations with other IRC
team members and site
engineers. The soft sensor has
been tested online for over 6
months with satisfactory
performance. It has recently
been in Stewardship for
potential application in real-
time closed-loop control of the
water content.
The search for solutions to the oil sands development has
typically focused on the processes that make up an oil sands
operation. We take a different approach by focusing on the
systems that control these processes.
Near-infrared (NIR) Modeling and Prediction
Near-infrared (NIR) spectroscopy technology has been widely adopted as a
process analytical tool (PAT) among petroleum, petrochemical, pharmaceutical
and many other industry sectors. The NIR spectrometer can record sample
spectra in real time to capture chemical and physical properties of substances.
Once combined with the prediction model, the NIR can provide estimation of
quality variables significantly faster than traditional laboratory analysis. As
wavelength selection is an important issue in NIR modeling, we have
developed a novel algorithm that can select wavelength in real time and
estimate coefficients recursively according to the latest information. The new
updating approach has proven to be promising in predicting diesel properties
for a refinery plant.
Introducing System Performance Monitoring (SPM) and Diagnosis (SPD)
SPM and SPD refer to system wide performance monitoring and diagnosis. The term
"system" is a collection of controllers, processes, equipment and instruments. Both SPM
and SPD consist of four components, namely Control Performance Monitoring (CPM),
Process Performance Monitoring (PPM), Equipment Performance Monitoring (EPM)
and Instrument Performance Monitoring (IPM). CPM/CPD is well known by process
control engineers as it has been a field of active research in the control community over
the last 20 years and a number of commercial software packages are available in the
market. Other components have been less known mainly due to their involvements
beyond the traditional process control domain. Nevertheless, the concepts have already
been used in industry - multivariate statistical process monitoring is one of the examples
of PPM, equipment reliability assessment is an example of EPM and instrument gross
error detection is an example of IPM. Introduction of SPM/SPD will streamline the
different components and establish a unified framework to solve a variety of industrial
monitoring and diagnosis problems.
Plant-Wide Data Mining and Optimization
The goal of this project is to provide a plant wide optimization strategy for maximizing
the final production rate of Synthetic Crude Oil (SCO) by optimal operation of mining,
primary extraction, secondary extraction, primary upgrading and secondary upgrading.
The main research tasks in progress have focused on:
1. Oil sands process data mining and analysis
2. Feature extraction and clustering analysis
3. Correlation analysis and regression modelling
4. Disturbance analysis and modelling
5. Plant wide scheduling
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Introducing an IRC Project in Progress
Introducing New IRC Initiatives
NSERC INDUSTRIAL RESEARCH CHAIR
Control of Oilsands Processes
NEWSLETTER, IRC AND RELATED R&D ACTIVITIES, ISSUE 2, March 2013
Prior to joining Prof. Huang’s
research team as a postdoctoral
fellow in August 2010, he obtained
his Ph.D. degree in December 2009
from the Zhejiang University, China.
His research interests while doing
his Ph.D. were data reconciliation
and manufacturing execution system.
During time period from 2005 to
2009, he also worked part-time as an
automation engineer in SUPCON
and participated in many industry
projects, where he gained a broad
experience in the petrochemical
industry. Additionally, he published
several articles in refereed journals
and international conferences.
In the IRC group, he has mainly
been conducting research in the
development and implementation of
inferential sensors, inferential sensor
based advanced control and
controller performance monitoring
on site, as well as acting as the main
on-site project contact. He also
collaborated with other IRC team
members and the industrial partners.
His achievements in inferential
sensor and control implementation
have been valued by industrial
partners.
Bioinformatics Project Due to the importance of resource based industries in Alberta (e.g., oil, gas,
mining, forestry) and associated public concerns about environmental impacts,
by support of Alberta Health, our bioinformatics group has actively participated
in biomedical research and made significant progress in developing statistical
informatics tools for rapidly detection of chemicals in the environment. The most
important contributions include:
1) Predictive models were developed to evaluate chemical cytotoxicity.
Compared to the previous methods, the developed method can capture the entire
exposure effect over a continuous time period. It is of significance that the
proposed method can achieve the high-throughput screening of chemicals.
2) A library of environmental chemical profiles and pattern recognition
algorithms was compiled, which enables differentiability/classification of
chemicals based on the chemical mode of action. Compared to the previous
molecular-level classification, the developed method evaluates the toxicity
pathways that are more relevant to human health.
3) A novel toxicity index was proposed to assess the level of water
contamination. Compared to the other analytical techniques, the developed
method evaluates the mixed cytotoxic effects caused by multiple environmental
chemicals.
4) A novel method for quantitative analysis of very low cellular copies with
HPRT mutation induced by environmental mutagens was developed. The
proposed method provides important guidelines for monitoring environmental
mutagens through routine laboratory screening.
Recent Sample Publications
Book
Huang, B., Y. Qi, M. Murshed, Dynamic modeling and Predictive
Control in Solid Oxide Fuel Cells : First principle and data-based
approaches, ISBN: 978-0-470-97391-2, John Wiley & Sons, 2013.
Journals
R. Gonzalez, B. Huang, F. Xu, A. Espejo, Dynamic Bayesian approach
to gross error detection and compensation with application toward an oil
sands process, Chemical Engineering Science, 67, 44-56, 2012.
S. Khatibisepehr, B. Huang, A Bayesian Approach to Robust Process
Identification with ARX Models, AIChE J., 59, 845—859, 2013.
Y. Shardt, Y. Zhao, F. Qi, K. Lee, X. Yu, B. Huang, S.L. Shah,
Determining the State of a Process Control System: Current Trends and
Future Challenges, Invited Feature Articles on Process Control,
Canadian Journal of Chemical Engineering, 90, 217-245, 2012.
J. Xin, S. Wang, B. Huang, F. Forbes, Multiple Model Based LPV Soft
Sensor Development with Irregular/Missing Process Output
Measurement, Control Engineering Practice, 20, 165-172, 2012.
Introducing A
Researcher
Yu Miao
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NSERC INDUSTRIAL RESEARCH CHAIR
Control of Oilsands Processes
C INDUSTRIAL RESEARCH CHAIR NEWS LETTER, IRC AND RELATED R&D ACTIVITIES, ISSUE 2, March, 2013
Visitors
Ren Zhengyun, Ph. D.
Professor of
Donghua University
Shanghai
Zhiqiang Ge, Ph.D.
Associate Professor of
Zhejiang University
New
Members Chandy Somayaji, MSc
Research Assistant
Applied Mathematics
News and Events
2013 University of Alberta/Suncor/Syncrude NSERC IRC and AITF iCORE Industry Chair
Public Workshop/Tutorial on Soft Sensor and System Performance Diagnosis.
FRIDAY, April, 5th 2013, Venue: Oil Sands Discovery Center, 515 Mackenzie Blvd, Fort
McMurray, AB, RSVP to Seraphina Kwak <[email protected]> by April 25th.
08:30 - 09:00 Registration
09:00 - 0 9:30 Opening remarks, Syncrude, Suncor, UofA
09:30 - 10:10 Soft sensor development: Bayesian inference approach, Shima
10:10 - 10:40 Soft sensor development: Recursive approach, Swanand
10:40 - 11:10 Break
11:10 - 11:40 Soft sensor development: Just-in-time Approach, Mulang
11:40 - 12:10 Soft sensor development: PCA/PLS approach, Marziyeh
12:10 - 01:00 Lunch
01:00 - 01:40 Control performance diagnosis: PID/control valves, Yu Zhao/Yu Miao
01:40 - 02:20 System performance diagnosis: MPC/Instruments, Da/Ruben/Biao
02:20 - 02:40 Break
02:40 - 03:20 Soft sensor & system performance diagnosis: general architecture,
Biao/Shima/Elham/Da/Ruben
03:20 - 04:00 General discussion and closing remarks
2013 Spring IRC Update Meeting (Industry sponsors only)
THURSDAY, April 4th, 2013 01:30 - 02:00 Bayesian soft sensor and Recursive N2B soft sensor, Shima
02:00 - 02:30 Water content soft sensor, Yu Zhao
02:30 - 03:00 Discussion of DCS implementation
03:00 - 04:00 IRC advisory committee meeting
Contact: Professor Biao
Huang, P. Eng.
Department of Chemical and
Materials Engineering,
University of Alberta
7th Floor, ECERF
Edmonton, AB, T6G 2V4
Tel.: 780-492-9016,
Fax: 780-492-2881
E-mail:
Web:
www.oilsandscontrol.ualbert
a.ca/
IRC Advisory Committee
and sponsors:
Aris Espejo, P. Eng.
Eric Lau, P.Eng.
Dan Brown, P. Eng.
Ramesh Kadali, P. Eng.
Newsletter prepared by
Fadi Ibrahim
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The Process Control Lab has recently been equipped with Emerson DeltaV DCS. Since
DeltaV DCS is widely used in industry, the introduction of DeltaV helps to construct a
process control simulation environment for university research that is as real as industrial
process control applications, hence providing students and researchers a practical
platform to test various process monitoring, diagnosis, control and optimization strategies
and facilitating industrial transformation of academic achievements. DeltaV system is
connected to lab experimental devices such as multi-tank system through OPC. OPC
based connection provides cost effectiveness, flexibility and convenience in establishing
process control system. As an example of connection to multi-tank system, DeltaV
system communicates to the pilot-scale process through MATLAB OPC. The multi-tank
system is used as a real process and the control strategy is designed and implemented in
DeltaV or in MATLAB to control the tanks. In addition, system performance monitoring
and diagnosis tools can also be demonstrated in the Delta V system. MATLAB serves as
a communication interface between DeltaV and the multi-tank system as well as a
computation engine, greatly facilitating implementation of advanced algorithms.
Process Systems & Control Laboratory