PROCESS CONTROL GROUP
Welcome to the webpage of
the Process Control Group at UNSW. Led by Prof Jie Bao, we work on process
system engineering, including dynamic process modelling, control and analysis.
Our research is focused on
· advanced control theory development,
control based on dissipativity theory;
of process design and control;
detection and tolerant control systems;
and control of collective dynamics.
· process control applications,
control of membrane systems;
detection and control of aluminium reduction cells;
of flow batteries;
of coal handling and preparation processes.
is an expert in dissipativity/passivity based process control. He has been
awarded more than $3.5M competitive research grants including 8 ARC DP/large
grants, 1 CSIRO National Flagship Research Cluster Project and a number of industrial projects. He published extensively
in major process control and chemical engineering journals. He is an
Associate Editor of Journal of Process
Control (an International Federation of Automatic Control
of Chemical Engineering
University of New South Wales
SYDNEY NSW 2052
+61 (2) 9385 6755
+61 (2) 9385 5966
Michael J. Tippett received a B.E. in
Industrial Chemistry/B. Com. in Business Economics with first class Honours
and the University Medal from the University of New South Wales (UNSW),
Sydney, Australia in 2009. He completed
his PhD study in 2013 in the area of distributed control systems in the
School of Chemical Engineering at UNSW.
His current research interests include: distributed and decentralized control,
adaptive control, model predictive control, dissipativity-based analysis and
control and their applications to chemical processes.
Dr. Ruigang Wang
Dr. Ruigang Wang received a B.E. in
Automobile Engineering from Beihang University,
Beijing, and an M.E. in Mechanical Engineering from Shanghai Jiaotong University, Shanghai, China, in 2009 and 2012, respectively.
He was awarded the PhD degree in Chemical Engineering in The University of
New South Wales, Sydney, Australia, in 2017. His research interests include
contraction theory, dissipativity theory, model predictive control,
distributed control, fault detection and diagnosis..
Dr. Yuchen Yao
Yuchen Yao received a B.E. in Chemical
Engineering with first class Honours from the University of New South Wales
(UNSW), Sydney, Australia. He is expected to be rewarded his PhD in 2017 in
the area of advanced control and monitoring in aluminium smelters from UNSW.
His current research include data analysis,
modelling, monitoring, fault detection and fault diagnosis of industrial
Postgraduate Research Students
Md Parvez Akter
Advanced Control of
Distributed Energy Storage Systems
Steven (Yifeng) Li
Advanced Control of
Vanadium Redox Batteries
Advanced Control of
Aluminium Smelting Cells
Fault Tolerant Control
CURRENT/RECENT RESEARCH PROJECTS:
Integrated Approach to Distributed Fault Diagnosis and Fault-tolerant Control
for Plantwide Processes
(ARC Discovery Project: DP160101810, 2016-2018)
Modern industrial processes are very complex, with distributed process
units via a network of material and energy streams. Their operations
increasingly depend on automatic control systems, which can make the plants
susceptible to faults such as sensor/actuator failures. Occurrence of faults is
increased by the common practice to operate processes close to their design
constraints for economic considerations. This project will develop a new
approach to detect and reduce the impact of these faults, which can cause
significant economic, environment and safety problems.
Based on the concept of dissipative systems, this project aims to develop
a novel integrated approach to distributed fault diagnosis and fault-tolerant
control for plantwide processes. The key dynamic features of normal and
abnormal processes are captured by their dissipativity properties, which are
used to develop an efficient online fault diagnosis approach based on process
input and output trajectories, without the use of state estimators or residual
generators. Using the dissipativity framework, a distributed fault diagnosis
approach will be developed to identify the locations and faults in a process
network. A distributed fault tolerant control approach will be developed to
ensure plantwide stability and performance.
Supported by the Australian Research
Council. In collaboration with Profs.
M. Skyllas-Kazacos, and V.G. Agelidis.
of Distributed Energy Storage System using Vanadium Batteries (ARC Discovery
Project: DP150103100, 2015-2017)
This project aims to develop a new control approach to distributed energy
storage at stack, system and microgrid levels, utilising one of the most
promising flow battery technologies - Vanadium Redox batteries. This is the
first attempt of a storage centric approach that includes (1) an integrated
approach to design and control of Vanadium flow batteries with novel advanced
power electronics technologies to achieve optimal charging/discharging
conditions and (2) a scalable distributed energy storage and power management
approach incorporating energy pricing for storage dispatch that allows
distributed autonomous controllers to achieve optimal local techno-economic
performance and microgrid-wide efficiency and reliability.
§ Dissipativity based Distributed Model Predictive Control for Complex
Industrial Processes (ARC Discovery Project: DP130103330,
Based on the behavioural approach to systems and dissipativity theory,
this project aims to integrate nonlinear control theory with distributed
optimization to develop a novel distributed predictive control approach for
complex industrial processes. In this approach, the global objectives (i.e.,
the plantwide stability and performance) are converted into the local
constraints of dissipativity conditions for non-cooperative optimization
performed in the distributed controllers. The outcomes will include a framework
and the fundamental control theory for distributed autonomous model predictive
control that achieves improved scalability, flexibility and robustness compared
with existing distributed predictive control approaches.
Control of Modern Chemical Processes from a Network Perspective (ARC Discovery Project: DP1093045, 2010-2012)
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.
Control of Membrane Processes (ARC Discovery Project: DP110101643, 2011-2013)
Fouling reduces throughput and
productivity of membrane systems and as such increases operating costs and
reduces profitability of water treatment industries. This work aims to reduce
membrane fouling by reducing the amount of solute at the membrane surface. This
is achieved by implementing destabilizing electro-osmotic flow control. The
significance of this project lies in linking feedback control of
electro-osmotic effects with spacer design to maximize flow instabilities. This
project will advance modelling of flow in membrane channels and develop a novel
feedback flow control strategy that enhances mixing. The effectiveness and
operability of the new fouling reduction approach on real-world membrane
systems will be evaluated. With over $9bn worth of membrane-based
desalination plants either in operation, under construction or being planned in
Australia, the expected outcomes of this project will lead to significant
social and economical benefit and provide greater
Supported by the Australian Research
Council. In collaboration with Prof. D.E. Wiley and Dr.
Alessio Alexiadis, Washington University in
Current Distribution Monitoring and Analysis (DUBAL, 2013-2015)
Supported by Dubai Aluminium Company. In collaboration with Prof. M.
Skyllas-Kazacos and B. J. Welch.
§ Advanced Control of Aluminium Smelting Cells (CSIRO National Research Flagship
production of aluminium is highly energy intensive, with energy costs representing
22-36% of operating costs in smelters. The Australian aluminium smelting
industry consumed 29,500 GWh of electricity in 2007, 13% of final electricity
consumption in Australia. The long term sustainability of the aluminium
smelting industry depends on energy-efficient production technologies for
global competitiveness. 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. The
expected outcomes include: (1) An adaptive model for the change in control
signal and control algorithms with different abnormalities and at different
operating line current levels; (2) A sequence of diagnostic sub-routines based
on processing signals at different; (3) A schemes for alarms and guidelines for
human interface interaction when needed.
§ Advanced Dynamic Control for Paste Thickeners (ACARP Project: C21055, 2012-2013)
The objective of this project is to develop an
online dynamic feedback control approach to improve the operation of paste
thickeners through adopting modern control strategies (in particular, model
predictive control) already successfully applied in the petro-chemical
industry. This would be an ideal test case for applying advanced dynamic control
for complete CHPPs or other variable dynamic processes such as flotation.