Control technologies for advanced energy efficiency and environmental emission reduction in industrial plants
Submitting InstitutionQueen's University Belfast
Unit of AssessmentElectrical and Electronic Engineering, Metallurgy and Materials
Summary Impact TypeTechnological
Research Subject Area(s)
Mathematical Sciences: Applied Mathematics, Statistics
Engineering: Mechanical Engineering
Summary of the impact
Collaborations funded through EPSRC Interact and RCUK UK-China Science
Bridge resulted in QUB's advanced control research having important
economic and environmental impact in China, Pakistan, Vietnam. This
includes the creation of new core modules for the Shanghai Automation
Instrumentation Co (SAIC) SUPMAX Distributed Control System series of
products now in use for whole plant monitoring and control to maximise
energy efficiency and reduce pollutant emissions. These products have
since 2008 increased SAIC's revenue by over $50M p.a. Related networked
monitoring technologies have been successfully deployed in Baosteel's
hot-rolling production lines and in the Nantong Water Treatment Company
that treats 20,000 tonnes of industrial waste water daily.
Key QUB Researchers involved Li (Lecturer 2002, Senior
Lecturer 2007, Reader 2009, Professor 2012) Irwin (Professor 1989)
Scanlon (Senior Lecturer 2002, Professor 2008), Lightbody (1989-1997,
PhD, Lecturer), Peng (1999-2012, PhD then PDRA, Colandairaj (2003-2006,
PhD), Connally (2003-2006), Niu (2009-2011, PDRA), Du (2009-2010,
exchange student, then 2011-2012 PDRA).
Time period: 1995 to 2012
Research on modern control techniques in large scale industrial processes
and thermal power plants, led by Irwin and Li, has been a UoA major area
for many years. This has resulted in important advances ranging from the
control of individual units to whole system and networked control. A key
aspect has been the use of neural network based methods. These yield new
and much less complex models than previously available with proven high
effectiveness in control environments that operate over a wide range of
Initial research GR/K37161/01 (1995-98) led to new modelling and
identification methods and associated algorithms for use in processes
where the underlying dynamics are either too complicated to build a
white-box model or where process knowledge is either unknown or partially
known. Subsequent research GR/S85191/01 (2004-07), investigated the use of
neural network methods and resulted in very important advances in a new
neural modelling approach based on the use of genetic algorithms for use
in non-linear dynamic systems. Here salient non-linear basis functions are
extracted, using first principle laws, and used as the basic building
blocks to optimally construct appropriate neural network models. These
have a simple structure and produce excellent prediction performance and
good interpretability [1, 2].
A major challenge is the selection of a small basis function subset from
a large candidate pool. However a major breakthrough was made by
integrating the forward and backward selection using one integrated
framework. Computational requirements significantly reduce by the
repetitive application of least squares operations involving matrix
inversion. This led to enhanced model performance and greater numerical
efficiency  as compared with previous orthogonal least squares methods.
Application to pollutant emission prediction and control in thermal power
plants was demonstrated  though collaboration with the British Coal
Utilisation Research Association, E.ON UK Power Technology Ltd.
Politecnico di Milano and the Shanghai Key Laboratory of Power Station
Parallel research on whole system control led to other important advances
with Multivariate Statistical Process Control (MSPC) theory being applied
to non-linear dynamical process monitoring . This led to new MSPC
monitoring software tools that were subsequently developed commercially.
An important related challenge is the control of large scale systems where
units are geographically distributed and connected via various
communication networks. Research in this area EP/F00477X/1 resulted in new
wireless network control technologies and a new and verified Simulink
co-simulation tool for the IEEE 801.11 protocol. This uses a novel
approach to adapt sampling interval based on an 'a priori', static
sampling policy and assures control stability — in a mean square sense —
by using discrete-time Markov jump linear system theory. In addition, a
new inverse Gaussian model for the statistical distribution of network
induced delays was created .
References to the research
The six publications covering/underpinning this research are listed
below. These have undergone rigorous peer review and the research funded
through the externally peer-reviewed external grants shown. The three
highlighted papers* are indicative of the quality underpinning the
IEEE Transactions on Automatic Control publishes high-quality
papers and technical notes on the theory, design and applications of
control engineering. Current Impact factor is 2.11. Control
Engineering Practice, published by Elsevier is a Journal of IFAC,
the International Federation of Automatic Control. It publishes papers
which illustrate the direct application of control theory. Current Impact
factor 2.03. IET Control Theory & Applications is devoted to
control systems in the broadest sense, covering new theoretical results
and the applications of new and established control methods. Current
Impact factor 1.
(1) P. Connally, K. Li, G. W. Irwin. "Integrated Structure Selection and
Parameter Optimisation for Eng-genes Neural Models", Neurocomputing, Vol.
71, No 13-15, 2964-2977, 2008. DOI: 10.1016/j.neucom.2007.06.005.
(2) Peng, J, Li, K, Thompson, S and Wieringa, P A. "Distribution-based
Adaptive Bounding Genetic Algorithm for Continuous Optimisation Problems"
(2007). Applied Mathematics and Computation, Vol. 185, pp 1063-1077, 2007.
*(3) Li, K, Peng, J and Irwin , G. "A fast nonlinear model identification
method" (2005), IEEE Transactions on Automatic Control, vol. 50, no. 8, pp
1211-1216, August 2005. DOI: 10.1109/TAC.2005.852557.
Paper has currently received 113 citations, Scopus.
*(4) K. Li, S. Thompson, J. Peng. "Modelling and prediction of NOx
emission in a coal-fired power generation plant" (2004). Control
Engineering Practice, Vol. 12, 707-723, 2004, Pergamon. DOI: 10.1016/S0967-0661(03)00171-0.
Paper has currently received 44 citations, Scopus.
(5) Hartnett, M., Lightbody, G. and Irwin G.W. "Chemometric techniques in
multivariate statistical modelling of process plant" (1996). Analyst,
Royal Society of Chemistry, June 1996, Vol. 121, pp. 749-754. DOI: 10.1039/AN99621007496.
*(6) Colandairaj, J, Irwin, G W and Scanlon, W G. "Wireless networked
control systems with QoS-based sampling" (2007). IET Control Theory Appl.
(formerly published as IEE Proceedings Control Theory & Applications),
vol. 1, no. 1, pp 430-438, January 2007. DOI: 10.1049/iet-cta:20050519.
Paper has currently received 62 citations, Scopus.
Research Grant funding
UoA Academics George W Irwin (Professor - PI),
UoA Funding EPSRC GR/K37161/01 "ROPA: Performance Enhancement of
Complex Industrial Processes by Online Auditing and Prediction", Mar 1995
to Feb 1998, £106K.
UoA Academics Kang Li (Professor - PI),
UoA Funding EPSRC GR/S85191/01 "Eng-genes: a new genetic modelling
approach for real-time operation and control of engineering systems", May
2004 to August 2007, £124K
UoA Academics George W Irwin (Professor - CI),
UoA Funding EPSRC EP/F00477X/1 "Wireless Interconnectivity and
Control of. Active Systems (WICAS)", Dec 2007 to Nov 2008, £97K.
UoA Academics George W. Irwin (Professor, CI), Kang Li (Professor
UoA Funding EPSRC EP/C004884/1 "INTERACT: Establishing New
Research Links with Chinese Universities and Chinese Academy of Science",
April 2005 to May 2006, £18K.
UoA Academics George W. Irwin (Professor, PI), Kang Li (Professor
UoA Funding RCUK EP/G042594/1 "UK-China Bridge in Sustainable
Energy and Built Environment", Sept 2009 to August 2012, £860K.
Details of the impact
The research described facilitated major QUB led UK-China collaborations
funded through (a) the EPSRC Interact Scheme (2005-2006) and (b) the RCUK
UK-China Science Bridge project (2009-2012). These activities created
important links between QUB, Shanghai University, Shanghai Automation
Instrumentation Co (SAIC) and Baosight and led to the creation of new
joint twinned laboratories on energy and automation at both QUB and at the
Key Laboratory for Power Station Automation Technology at Shanghai
University1. QUB's activities have focused on proof-of-concept
testing and technology transfer based on the advanced control technologies
described, as well as training programmes and thematic workshops,
involving UK and Chinese project partners. This led to extensive staff,
student and knowledge transfer exchange and further research on the
deployment of this new technology in industry.
At SAIC these activities led to the creation of new core modules that
have since been incorporated into the company's SUPMAX distributed control
system (DCS) series of commercial products, the first of its kind
manufactured in China. This has included incorporating QUB's
identification and optimisation techniques [1-3] into a networked
self-learning control module. This takes the form of an advanced PID
self-tuning software package that is part of the Distributed Processing
Unit, with this being used for both unit and plant-wide control. In
addition, the networked control techniques described  have been used to
develop a new wireless handset manipulator to improve operation
flexibility. A novel Profibus-DP to Ethernet protocol converter has also
been developed that significantly improves the communication and control
capacity of the SUPMAX products.
SUPMAX DCS has now been used in several major projects for whole plant
monitoring and control to maximise energy efficiency and reduce pollutant
emissions. In particular, it has been used in the Nantong Waste
incineration thermal power generation plant to monitor the whole plant and
to control three large circulating fluidised bed waste incinerators. These
process 1,500 tonnes of solid waste daily and supply 120 tonnes per hour
of steam to nearby companies supplying 200M KWh to China's national grid
annually. This plant is now a showcase in China for municipal solid waste
SUPMAX DCS products have also been successfully used in other plants in
China as well as in Vietnam, Pakistan and other developing countries where
energy efficiency and environmental protection is essential for
sustainable development. These showcase projects have led to significant
energy savings and environmental protection, with ensuing societal impact.
The introduction of these products has also enhanced SAIC's revenues2
by over US $ 50M p.a. i.e. also has had significant economic impact.
The Multivariate Statistical Process Control MSPC techniques  and the
wired/wireless networked monitoring and control systems created  have
also been developed, successfully tested and deployed in the hot-rolling
production lines of the Baosteel Group. These have been used for condition
monitoring, fault-diagnosis, and information management3, as
well as in the Nantong Water Treatment Co. which treats 20,000 tonnes of
industrial waste water daily for the Rugao industrial development zone.
The Zhejiang Ninghai and Shanghai Caohejing power stations have also
benefitted from the UoA's technology [1-4] using the technology to
control, flow rate, pressure and temperature in hydrogen-cooled oil
pipeline pressure systems as well as in a 1000MW pulveriser system. Again
this has resulted in significant improvements in energy efficiency and
This impact has led in 2009 to the award of second prize for "Science and
Technology Progress" at the Shanghai Government's annual Science and
Technology Awards Conference and in 2010 to the award of the "Creation
Prize" at China's International Industry Fair in 2010, organised by
China's National Development and Reform Commission and several Chinese
Tackling climate change and maintaining energy security are twin
challenges facing both developed and developing countries. China is now
the second largest consumer of primary energy and the world's top country
for CO2 emissions. The research undertaken is therefore having an
important impact in helping to address these societal and environmental
Note SAIC, (part of the Shanghai Electric Group), is the world's
largest manufacturer of steam turbines, http://www.shanghai-electric.com/en/Pages/default.aspx).
Baosight, (part of the Baosteel Group) is the world's second-largest steel
producer measured by crude steel output (www.baosteel.com/group_en/).
Sources to corroborate the impact
1Leader of the UK-China Science Bridge Joint Lab on Energy and
Automation Shanghai University.
2Chief Engineer SUPMAX product development, applications and
sales: Shanghai Automation Instrumentation Co. Ltd.
3Chief engineer Central Technology of Baosteel Detection Co.
Ltd Baosteel Co., Ltd.
RCUK funded UK-China Science Bridge project at Queen's University Belfast
UK-China Science Bridge joint laboratory on power and automation
SUPMAX distributed networked monitoring and control systems
Chinese language sites:
Second prize for advancing science and technology awarded by the Shanghai
Government in 2009 (The key technologies for networked measurement and
control system and power plant
Creation prize in the China International Industry Fair in 2010
"Measurement and Control System based on hybrid networks towards the
Internet of things oriented applications"