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

1

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

2

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 <skwak@ualberta.ca> 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:

biao.huang@ualberta.ca

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

3

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