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Dr.
Jie Bao
Associate Professor
BE MEng (Zhejiang), PhD (Qld)
Computer Process Control Group
Postgraduate Research Coordinator
School
of Chemical Sciences and Engineering
The University of New South Wales
UNSW SYDNEY NSW 2052
Australia
Telephone: +61 (2) 9385 6755
Facsimile: +61 (2) 9385 5966
Email: J.Bao@unsw.edu.au
EDUCATION:
Ph.D. Chem. Eng., University of Queensland, 1998
M.E. E.E., Zhejiang University, 1993
B.E. E.E., Zhejiang University, 1990
EMPLOYMENT:
School of Chemical Sciences & Engineering, UNSW, Associate Professor, 2008-
School of Chemical Sciences & Engineering, UNSW, Senior Lecturer, 2003-2007
School of Chemical Sciences &
Engineering, UNSW, Lecturer, 1999-2003
University of Alberta,
Edmonton, Canada, Postdoctoral Research Fellow, 1998-1999
University of Queensland, Australia,
Part-time tutor, 1994-1997
Control & Measurement Branch, ZUSTD Corp., Hangzhou, China, Assistant Engineer, 1993-1994
PROFESSIONAL ACTIVITIES:
Associate Editor, Journal of Process Control
Referee for scholarly journals including:
· Journal of Process Control
· IEEE transactions on Automatic Control
· IEE Proc. Control Theory & Applications
· Journal of Dynamic Systems, Measurement and Control
· Industrial & Engineering Chemistry Research
· Chemical Engineering Science
· Journal of Membrane Science
· Chemical Engineering Communications
· International Journal of System Sciences
· Canadian Journal of Chemical Engineering
· Asian-pacific Journal of Chemical Engineering
Research Interests:
Computer Process Control – Integration of process design and control; Fault tolerant control systems; Process control based on the Passivity Theorem; Decentralized process control; Robust control; Process control applications.
RECENT RESEARCH ACTIVITIES:
§ Plantwide
Control of Modern Chemical Processes from a Network Perspective
To achieve high
economical efficiency, modern chemical plants are becoming increasingly
complex, to an extent that cannot be effectively managed by existing process
modelling and control techniques. By exploring the physical fundamentals in
thermodynamics and their connections to control theory, this project aims to
develop a new modelling and control approach that can be applied to complicated
nonlinear processes. In this approach, processes over the entire plant are
analysed and controlled from a network perspective using the dissipativity
control theory. The outcomes of this project will form the cornerstones of a
new process control paradigm that offers more robust and reliable process
operation at any scale. (Supported by the Australian
Research Council. In collaboration with Prof. Erik
Ydstie, Carnegie Mellon University)
§ A Behaviour Approach to Optimization based Controller
Coordination for Complex Process
The complexity of plantwide chemical
systems is steadily increasing, driven by the gain in economic efficiency
offered by more complex and interactive plant designs. This project aims to
develop a new framework of complex process control using coordinated
optimization-based controllers. Control systems based on online optimization
are often most suitable for complex systems and can be applied to a large
ranges of control problems. An interaction analysis approach for plantwide
complex processes and the stability conditions for coordinated controllers
based on their historical behaviour will be developed. This will lead to a new
control approach that can be applied in many modern complex engineering
applications including control of renewable energy networks.. (Supported by FRG.)
§ Dynamic Controllability Analysis for Plantwide Process
Design and Control
Based on the concept of passive systems, this project aims to develop a new quantitative measure for dynamic controllability for design of plantwide process systems. Integration of process design and control has been widely recognized as an effective approach to improving process performance to meet increased economic, safety and environmental demands. Controllability evaluation plays an important role in this approach. The outcome of this research will be an easy to use controllability analysis method for nonlinear plantwide multi-unit systems, which can be used in early stages of process design to explore better opportunities for process improvements.
World-wide chemical plants represent many billions of dollars of investment. Improvements to the process designs in terms of controllability would have the potential to provide large economic benefits, as it implies improved productivity, reduced operating costs and product variability. This proposed research will be a step towards integration of process design and control, which has been widely recognized as the key to this improvement. The outcomes from this project may be readily implemented in process design practice, and therefore may have a direct impact to the Australian and world-wide process industries, helping to build a more efficient and environmental conscious Australian process industries. (Supported by the Australian Research Council. In collaboration with Prof. P.L. Lee, University of South Australia)
§ Soft Sensor Development for Mineral Processes Aided by Discrete Element Models
This project develops a new soft sensor
approach for mineral processes using discrete element models, exemplified by a
milling process. Monitoring and control of milling conditions is of
considerable interest to industry as grinding is the most energy intensive
process in mineral processing. However, direct monitoring of comminution
processes is not feasible due to the hostile environment inside the mills. By
using the discrete element models of milling processes, key process variables
and operating conditions inside the mills are simulated. Using both
multivariate statistical methods and the Gaussian process modelling technique,
the simulation results are then used to train soft sensor models that can
identify online the collision energy and the operating regions in terms of
breakage, from surface vibration measured during the milling operation.
(Supported by FRG. In
collaboration with Prof.
Aibing Yu)
§ Advanced Control of Aluminium Smelting cells
The aim of the project is to
improve auto-diagnosis of the occurrence of the root-cause for abnormal process
conditions in the smelting cells that adversely impact energy and environmental
efficiencies. (Supported by
CSIRO Aluminium Flagship Cluster. In collaboration with Profs.Welch and
Skyllas-Kazacos)
§ Advanced Control of Membrane Processes
This work aims to develop a dynamic process model and advanced control schemes for pressure driven membrane systems. Current methodologies, while functional, are conservative, narrow and slow and do not take advantage of process improvements achievable with tight active control. The expected outcomes include a validated model, control strategies that maximize productivity and minimize fouling during normal operation as well as during start-up and shut-down. (In collaboration with Prof. D.E. Wiley)
RESEARCH GRANTS AWARDED:
|
Studies
on Failure-tolerant Decentralised Control based on the Passivity Theorem |
Australian Research Council (ARC) small grant |
2000 |
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Passivity-based
Fault-tolerant Decentralized Control for Linear and Nonlinear Processes |
Australian Research Council (ARC) large grant |
2001-2003 |
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Enhancement
of DCS-Centred Process Control Experimental Rig |
Research Infrastructure Block Grant |
2000 |
|
An
Integrated Approach to Modelling and Robust Process Control |
University Research Support Program (URSP 2002, FRG 2003) |
2002-2003 |
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Defining Fundamental
Principles for the Design and Operation of Membrane Systems from Time-Varying
Performance Analysis |
Australian Research Council (ARC) Discovery Project |
2003-2005 |
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Dynamic Controllability
Analysis for Plantwide Process Design and Control |
Australian Research Council (ARC) Discovery Project |
2005-2007 |
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Soft sensor development for milling processes aided by
discrete element models |
Faculty Research Grant |
2007 |
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Interaction
analysis and decoupling control of complex processes |
International Science Linkages: Australia-China Special Fund |
2007-2009 |
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A behaviour approach to optimization
based controller coordination for complex process systems Chief investigator: Bao |
FRG |
2009 |
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Breakthrough
Technology for Primary Aluminium- Advanced Control: Process Data and
Regulation Approaches |
CSIRO
Aluminium Cluster |
2009-2012 |
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Plantwide
Control of Modern Chemical Processes from a Network Perspective |
Australian
Research Council (ARC) Discovery Project |
2010-2012 |
Members of Research
GROUP:
Current members:
|
Research Personnel |
Position |
Project |
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Dr. Christopher Menictas |
Research Fellow |
Advanced control of Aluminium smelters |
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Herry
Santoso |
PhD
candidate |
Controllability
analysis for typical chemical processes |
|
Luke
McElroy |
PhD
Candidate |
Soft
sensors for particulate systems |
|
Ridwan
Setiawan |
PhD
Candidate |
Controllability
analysis for plantwide process systems |
|
Shichao
Xu |
PhD
Candidate |
Analysis
and control of process networks |
|
Tri
Tran |
PhD
Candidate |
Novel
MPC control |
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Dave
Javan Tjakra |
PhD
candidate |
Modelling
and control of particulate systems |
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Winnie
Cheung |
PhD
candidate |
Modelling
and control of aluminium smelters |
PREVIOUS MEMBERS:
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Research Personnel |
Project |
Current affiliation |
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Dr. Frank Zhang |
Passivity based fault tolerant control (former PhD student) |
Honeywell Australia |
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Dr. Steven Su |
Passivity based fault tolerant control for nonlinear systems |
University of Technology, Sydney |
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Mr. Andika Suryodipuro |
Controllability analysis using frequency domain tools |
Institut Teknologi Bandung |
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Dr. Richard Chan |
An integrated approach to process modelling and control |
Carnegie Mellon University, USA |
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Dr. Kevin W.K. Yee |
Operability Analysis of a Multiple-Stage
Membrane Process |
TBA |
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Dr. Osvaldo Rojas |
Quantitative Dynamic Controllability Analysis for Integration of Process Design and Control |
TBA |
MAIN TEACHING ACTIVITIES:
CEIC3006 Process Dynamics and Control (Lecturer in Charge)
CEIC8102 Advanced Process Control (Lecturer in Charge)
CEIC3000 Chemical Engineering Fundamentals – 3: Process Modelling and Analysis (Co-lecturer, Model analysis part)
ADMINISTRATION:
Postgraduate Research Coordinator, School of
Chemical Sciences and Engineering
Taste of Research Coordinator, School of
Chemical Sciences and Engineering
SELECTED PUBLICATIONS
Books
▪ Bao J. and Lee P.L. (2007) Process Control: The Passive Systems Approach. Springer-Verlag London, ISBN: 978-1-84628-892-0.
Journal Publications
▪
Rojas OJ, Setiawan R, Bao J and Lee PL (2009)
Dynamic operability analysis of nonlinear process networks based on
dissipativity. AIChE J. 55(4): 963-982
▪
Xu S.C.; Bao J. (2009) Distributed Control
of Plantwide Chemical Processes. J.
Process Control (in press)
▪
Yee KWK, Wiley DE and Bao J (2009) A unified
model of the time dependence of flux decline for the long-term ultrafiltration
of whey. J.
Memb. Sci. 332(1-2): 69-80
▪
Santoso H, Bao J and Lee PL (2009) Operability
Analysis of MTBE Reactive Distillation Column using a Process Simulator. Chemical Product and Process Modelling (in press)
▪
McElroy
L, Bao J, Yang RY and Yu AB (2009) Soft-sensors for prediction of impact energy
in horizontal rotating drum. Powder
Technology (in press)
▪
Yee K.W.K.; Alexiadis A.; Bao J. and Wiley
D.E. (2009) Effects of recycle ratios on process dynamics and operability of a
whey ultrafiltration stage. Desalination 236(1-3): 216–223.
▪
McElroy L.; Bao J.; Yang R.Y. and Yu A.B. (2009) A Soft-Sensor Approach
to Flow Regime Detection for Milling Processes. Powder Technology 188(3): 234-241.
▪
Santoso H.; Bao J. and Lee P.L. (2009) The
Steady-State Region of Attraction under Linear Feedback Control: A Numerical
Approach. J. Process Control 19(3): 464–472.
▪
Santoso H.; Bao J. and Lee P.L. (2008)
Dynamic Operability Analysis for Stable and Unstable Linear Processes. Ind.
Eng. Chem. Res. 47(14): 4765–4774.
▪
Yang R.Y.; Yu A.B.; McElroy L. and Bao J. (2008) Numerical
simulation of particle dynamics in different flow regimes in a rotating drum. Powder
Technology 188:170–177.
▪
Xu S.C.; Bao J. (2008) Interaction
Analysis for Decentralized Control Based on Dissipativity. Asia-Pac.
J. Chem. Eng. 3(6):
656-666.
▪
Yee K.W.K.; Alexiadis A.; Bao J. and Wiley D.E. (2008) Effects of
multiple-stage membrane process designs on the achievable performance of
automatic control. J. Memb. Sci. 320 (1/2): 280-291.
▪
Rojas
O.J.; Bao J. and Lee P.L. (2008) On Dissipativity Passivity and Dynamic
Operability of Nonlinear Processes. J. Process
Control 18 (5): 515–526
▪ Bao J.; Chan K.H.; Zhang W.Z. and Lee P.L. (2007) An experimental pairing method for multi-loop control based on passivity. J. Process Control 17 (10): 787–798.
▪ Chan K.H. and Bao J. (2007) Model Predictive Control of Hammerstein Systems with Multivariable Nonlinearities. Ind. & Eng. Chem. Res. 46 (1): 168-180.
▪ Rojas O.J.; Bao J. and Lee P.L. (2007) A Dynamic Operability Analysis Approach for Nonlinear Processes. J. Process Control 17 (2): 157–172.
▪ Yee K.W.; Wiley D.E. and Bao J. (2007) Whey protein concentrate production by continuous ultrafiltration: Operability under constant operating conditions. J. Memb. Sci. 290(1/2): 125–137.
▪ Alexiadis A.; Wiley D.E.; Fletcher D.F. and Bao J. (2007) Laminar Flow Transitions in a 2D Channel with Circular Spacers. Ind. & Eng. Chem. Res. 46(16): 5387 – 5396.
▪ Santoso H., Bao J. and Lee P.L. (2007) Passivity Based Dynamic Controllability Analysis for Multi-Unit Processes. Chemical Product and Process Modelling 2 (2): Article 7.
▪ Alexiadis A.; Wiley D.E.; Vishnoi A.; Lee R.H.K.; Fletcher D.F. and Bao J. (2007) CFD modelling of reverse osmosis membrane flow and validation with experimental results. Desalination 217: 242–250.
▪ Santoso H., Rojas OJ, Bao J and Lee PL (2007) Nonlinear Process Operability Analysis Based on Steady-state Simulation: A Case Study. Chemical Product and Process Modelling 2 (2): Article 6.
▪ Su S.W.; Bao J. and Lee P.L. (2006) A Hybrid Active-Passive Fault Tolerant Control Approach. Asia-Pac. J. Chem. Eng. 1 (1-2): 54-62.
▪
Rojas O.J.; Bao J. and Lee P.L. (2006) Linear control
of nonlinear processes: the regions of steady-state attainability. Ind.
& Eng. Chem. Res. 45 (
▪ Chan K.H.; Bao J. and Whiten W.J. (2006) Identification of MIMO Hammerstein Systems Using Cardinal Spline Functions. J. Process Control 16 (7): 659–670.
▪ Su S.W.; Bao J. and Lee P.L. (2006) Conditions on Input Disturbance Suppression for Multivariable Nonlinear Systems on the Basis of Feed Forward Passivity. International Journal of Systems Science 37 (4): 225–233.
▪ Yee K.W.; Wiley D.E. and Bao J. (2006) Steady state operability of whey ultrafiltration (UF) system. Desalination 199 (1-3): 497-498.
▪ Alexiadis A.; Bao J.; Fletcher D.F.; Wiley D.E. and Clements D.J. (2006) Dynamic response of a high pressure reverse osmosis membrane simulation to time dependent disturbances. Desalination 191 (1-3): 397–403.
▪ Su S.W.; Bao J. and Lee P.L. (2006) Decentralized Control for Multivariable Processes with Actuator Nonlinearities. Dev. Chem. Eng. Mineral Process. 14 (1/2): 163-172.
▪ Chan K.H.; Bao J. and Whiten W.J. (2005) A New Approach to Control of MIMO Processes with Static Nonlinearities Using an Extended IMC Framework. Comput. & Chem. Eng. 30 (2): 329–342.
▪ Zhang W.Z.; Bao J. and Lee P.L. (2005) Process Dynamic Controllability Analysis Based on All-Pass Factorization. Ind. & Eng. Chem. Res. 44 (18): 7175-7188.
▪ Alexiadis A.; Bao J.; Fletcher D.F.; Wiley D.E. and Clements D.J. (2005) Analysis of the Dynamic Response of a Reverse Osmosis Membrane to Time Dependent Transmembrane Pressure Variation. Ind. & Eng. Chem. Res. 44 (20): 7823-7834.
▪ Su S.W.; Bao J. and Lee P.L. (2005) Control of Multivariable Hammerstein Systems by Using Feedforward Passivation. Ind. & Eng. Chem. Res. 44 (4): 891-899
▪ Su S.W.; Bao J. and Lee P.L. (2004) Analysis of Decentralized Integral Controllability for Nonlinear Systems. Comput. & Chem. Eng. 28 (9): 1781-1787.
▪ Bao J.; Zhang W.Z. and Lee P.L. (2003) Decentralized Fault-tolerant Control System Design for Unstable Processes. Chem. Eng. Sci. 58 (22): 5045-5054.
▪ Zhang W.Z.; Bao J. and Lee P.L. (2003) Control Structure Selection Based on Block Decentralized Integral Controllability. Ind. & Eng. Chem. Res. 42 (21): 5152-5156.
▪ Bao J.; Lee P.L.; Wang F.Y. and Zhou W.B. (2003) Robust Process Control Based on the Passivity Theorem. Dev. Chem. Eng. Mineral Process 11 (3/4): 287-308.
▪ Bao J.; McLellan P.J. and Forbes J.F. (2002) A Passivity-based Analysis for Decentralized Integral Controllability. Automatica 38 (2): 243-247.
▪ Zhang W.Z.; Bao J. and Lee P.L. (2002) Decentralized Unconditional Stability Conditions Based on the Passivity Theorem for Multi-loop Control Systems. Ind. & Eng. Chem. Res. 41 (6): 1569-1578.
▪ Bao J.; Zhang W.Z. and Lee P.L. (2002) Passivity-Based Decentralized Failure-Tolerant Control. Ind. & Eng. Chem. Res. 41 (23): 5702-5715
▪ Bao J.; Lee P.L.; Wang F.Y.; Zhou W.B. and Samyudia Y. (2000) A New Approach to Decentralized Control Using Passivity and Sector Stability Conditions. Chem. Eng. Commun. 182: 213-237.
▪ Bao J.; Forbes J.F. and McLellan P.J. (1999) Robust Multi-Loop PID Controller Design - A Successive Semi-Definite Programming Approach. Ind. & Eng. Chem. Res. 38 (9): 3407-3419.
▪ Bao J.; Lee P.L.; Wang F.Y. and Zhou W.B. (1998) New Robust Stability Criterion and Robust Controller Synthesis. Int. J. Robust Nonlinear Control 8 (1): 49-59.
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