Process Systems and Control Engineering

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== Publications [[Image:GoogleScholar.png|frameless|80px|link=http://scholar.google.ca/citations?user=6JlrZfAAAAAJ&hl=en]] ==
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__NOTITLE__
  
=== Books and Edited Issues ===
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We work in the general area of process systems and control engineering. Our primary research objective is to develop computing technologies for smart and sustainable manufacturing and production using systems, computing and engineering principles. We are actively working on the following projects:
  
# J. Liu and H. E. Durand (Eds.). ''New Directions on Model Predictive Control'', Special issue of ''Mathematics'', 2018. http://www.mdpi.com/journal/mathematics/special_issues/New_Directions_Model_Predictive_Control
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=== Smart closed-loop agriculture irrigation ===
# M. Ellis, J. Liu, and P. D. Christofides. ''Economic Model Predictive Control: Theory, Formulations and Chemical Process Applications.'' Advances in Industrial Control, Springer-Verlag, London, England, 2016. http://www.springer.com/us/book/9783319411071
 
# P. Mhaskar, J. Liu, and P. D. Christofides. ''Fault-Tolerant Process Control: Methods and Applications.'' Springer-Verlag, London, England, 2013. (281 pages). http://www.springer.com/engineering/control/book/978-1-4471-4807-4
 
# P. D. Christofides, J. Liu, and D. Munoz de la Pena. ''Networked and Distributed Predictive Control: Methods and Nonlinear Process Network Applications.'' Advances in Industrial Control Series. Springer-Verlag, London, England, 2011. (230 pages). http://www.springer.com/engineering/control/book/978-0-85729-581-1
 
  
=== Journal Articles ===
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Water is essential for our daily life and is at the core of sustainable development. While about 70%
(* indicates corresponding author)
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of the Earth’s surface is covered by water, less than 2.5% of it is freshwater. The global water needs
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of the fast growing world population are already exceeding the currently available fresh water supply.
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The largest use of fresh water is in agriculture, which accounts for about 70% of the global fresh water
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consumption. As population growth continues, 60% more food will be needed to satisfy the demand of
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more than 9 billion people worldwide by 2050. However, in many regions (even the ones in water-rich
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countries such as USA and Canada), water allocated to irrigation is largely capped or will be capped in
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the near future and any further increase in irrigated acres needs to come essentially from improvements
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in water-use efficiency. The irrigation water-use efficiency worldwide is currently very low (around 50%
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to 60%). It is clear that policies related to water conservation and new technologies for more efficient
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water consumption need to be developed.
  
# S. A. Hussain*, R. K. K. Yuen, J. Liu, J. Wang. Adaptive modeling for reliability in optimal control of complex HVAC systems. ''Building Simulation'', 12 pages, 2019. https://doi.org/10.1007/s12273-019-0558-9
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<gallery mode="packed-overlay">
<!-- [[Image:320_pdf_icon_16.gif|17px|link=https://doi.org/10.1007/s12273-019-0558-9]] -->
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File:Closedloop.png|''Closed-loop irrigation block diagram''
# J. Nahar, S. Liu, J. Liu*, S. L. Shah. Improved storm water management through irrigation rescheduling for city parks. ''Control Engineering Practice'', 87:111-121, 2019. https://doi.org/10.1016/j.conengprac.2019.03.019 
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File:Demofarm.png|''A research farm''
# J. Nahar, S. Liu, Y. Mao, J. Liu*, S. L. Shah. Closed-loop scheduling and control for precision irrigation. ''Industrial & Engineering Chemistry Research'', 58:11485-11497, 2019. https://www.doi.org/10.1021/acs.iecr.8b06184
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File:Remotesensing.png|''Estimated surface soil moisture using remote sensing''
# S. Liu, Y. Mao, and J. Liu*. Model predictive control with generalized zone tracking. ''IEEE Transactions on Automatic Control'', in press. https://www.doi.org/10.1109/TAC.2019.2902041
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</gallery>
# X. Yin, J. Zeng, and J. Liu*. Forming distributed state estimation network from decentralized estimators. ''IEEE Transactions on Control Systems Technology'', in press. https://doi.org/10.1109/TCST.2018.2866556
 
# J. McAllister, Z. Li, J. Liu* and U. Simonsmeier. Erythropoietin dose optimization for anemia in chronic kidney disease using recursive zone model predictive control. ''IEEE Transactions on Control Systems Technology'', 27:1181-1193, 2019. https://doi.org/10.1109/TCST.2018.2803052
 
# L. Zhang*, W. Xie, and J. Liu. Robust control of saturating systems with Markovian packet dropouts under distributed MPC. ''ISA Transactions'', 85:49-59, 2019. https://doi.org/10.1016/j.isatra.2018.08.027
 
# J. Nahar, J. Liu*, and S. L. Shah. Parameter and state estimation of an agro-hydrological system based on system observability analysis. ''Computers & Chemical Engineering'', 121:450-464, 2019. https://doi.org/10.1016/j.compchemeng.2018.11.015
 
# X. Yin and J. Liu*. Subsystem decomposition of process networks for simultaneous distributed state estimation and control. ''AIChE Journal'', 65:904-914, 2019. https://doi.org/10.1002/aic.16426
 
# J. Cui, S. Liu, J. Liu*, and X. Liu. A comparative study of MPC and economic MPC of wind energy conversion systems. ''Energies'', 11, 23 pages, 2018. https://doi.org/10.3390/en11113127
 
# A. Zhang, X. Yin, S. Liu, J. Zeng, and J. Liu*. Distributed economic model predictive control of wastewater treatment plants. ''Chemical Engineering Research and Design'', 141:144-155, 2018. https://doi.org/10.1016/j.cherd.2018.10.039
 
# Y. Mao, S. Liu, J. Nahar, J. Liu*, and D. Feng. Soil moisture regulation of agro-hydrological systems using zone model predictive control. ''Computers and Electronics in Agriculture'', 154:239-247, 2018. https://doi.org/10.1016/j.compag.2018.09.011
 
# B. Decardi-Nelson, S. Liu, and J. Liu*. Improving flexibility and energy efficiency of post-combustion CO2 capture plants using economic model predictive control. ''Processes'', 6, 22 pages, 2018. https://doi.org/10.3390/pr6090135
 
# G. Yang, J. Liu, and B. Huang*. Limits of control performance for distributed networked control systems in presence of communication delays. ''International Journal of Adaptive Control and Signal Processing'', 32:1282-1293, 2018. http://dx.doi.org/10.1002/acs.2913
 
# J. McAllister, Z. Li*, J. Liu, and U. Simonsmeier. EPO dosage optimization for anemia management: stochasitc control under uncertainty using conditional value at risk. ''Processes'', 6, 22 pages, 2018. https://doi.org/10.3390/pr6050060
 
# S. Liu and J. Liu*. Economic model predictive control with zone tracking. ''Mathematics'', 6, 19 pages, 2018. https://doi.org/10.3390/math6050065
 
# X. Yin, B. Decardi-Nelson and J. Liu*. Subsystem decomposition and distributed moving horizon estimation of wastewater treatment plants. ''Chemical Engineering Research and Design'', 134, 405-419, 2018. https://doi.org/10.1016/j.cherd.2018.04.032
 
# X. Yin and J. Liu*. State estimation of wastewater treatment plants based on model approximation. ''Computers & Chemical Engineering'', 111:79-91, 2018. https://doi.org/10.1016/j.compchemeng.2018.01.003
 
# M. Rashedi, O. Xu, S. Kwak, S. Sedghi, J. Liu and B. Huang*. An integrated first principle modeling to steam assisted gravity drainage (SAGD). ''Journal of Petroleum Science and Engineering'', 163:501-510, 2018. https://doi.org/10.1016/j.petrol.2018.01.005
 
# M. Rashedi, J. Liu and B. Huang*. Triggered communication in distributed adaptive high-gain EKF. ''IEEE Transactions on Industrial Informatics'', 14:58-68, 2018. http://dx.doi.org/10.1109/TII.2017.2715340
 
# B. Hassanzadeh*, J. Liu and J. F. Forbes. A bi-level optimization approach to coordination of distributed model predictive control systems. ''Industrial & Engineering Chemistry Research'', 57:1516-1530, 2018. http://pubs.acs.org/doi/abs/10.1021/acs.iecr.7b02414
 
# T. An, X. Yin, J. Liu* and J. F. Forbes. Coordinated distributed moving horizon state estimation for linear systems based on prediction-driven method. ''Canadian Journal of Chemical Engineering'', 95:1953-1967, 2017. http://dx.doi.org/10.1002/cjce.22917
 
# X. Yin and J. Liu*. Distributed output-feedback fault detection and isolation of cascade process networks. ''AIChE Journal'', 63:4329-4342, 2017. http://dx.doi.org/10.1002/aic.15791
 
# K. Arulmaran and J. Liu*. Handling model plant mismatch in state estimation using a multiple model based approach. ''Industrial & Engineering Chemistry Research'', 56:5339-5351, 2017. http://pubs.acs.org/doi/abs/10.1021/acs.iecr.7b00234
 
# M. Rashedi, J. Liu and B. Huang*. Distributed adaptive high-gain extended Kalman filtering for nonlinear systems. ''International Journal of Robust and Nonlinear Control'', 27:4873-4902, 2017. http://onlinelibrary.wiley.com/doi/10.1002/rnc.3838/full
 
# X. Yin and J. Liu*. Distributed moving horizon state estimation of two-time-scale nonlinear systems. ''Automatica'', 79:152-161, 2017.  http://www.sciencedirect.com/science/article/pii/S000510981730033X
 
# X. Yin and J. Liu*. Input-output paring accounting for both structure and strength in coupling. ''AIChE Journal'', 63:1226-1235, 2017.  http://dx.doi.org/10.1002/aic.15511
 
# B. Hassanzadeh*, P. Hallas, J. Liu and J. F. Forbes. Distributed model predictive control of nonlinear systems based on price-driven coordination. ''Industrial & Engineering Chemistry Research'', 55:9711-9724, 2016. http://dx.doi.org/10.1021/acs.iecr.6b01862
 
# J. Zeng, J. Liu*, T. Zou and D. Yuan. Distributed extended Kalman filtering for wastewater treatment processes. ''Industrial & Engineering Chemistry Research'', 55:7720-7729, 2016. http://dx.doi.org/10.1021/acs.iecr.6b00529
 
# S. Liu and J. Liu*. Economic model predictive control with extended horizon. ''Automatica'', 73:180-192, 2016. http://www.sciencedirect.com/science/article/pii/S0005109816302540
 
# M. Rashedi, J. Liu and B. Huang*. Communication delays and data losses in distributed adaptive high-gain EKF. ''AIChE Journal'', 62:4321-4333, 2016. http://dx.doi.org/10.1002/aic.15351
 
# X. Yin, K. Arulmaran, J. Liu* and J. Zeng. Subsystem decomposition and configuration for distributed state estimation. ''AIChE Journal'', 62:1995-2003, 2016. http://dx.doi.org/10.1002/aic.15170
 
# J. Zhang, X. Yin and J. Liu*. Economic MPC of deep cone thickeners in coal beneficiation. ''Canadian Journal of Chemical Engineering'', 94:498-505, 2016. http://dx.doi.org/10.1002/cjce.22419
 
# C. Zheng, M. J. Tippett, J. Bao* and J. Liu. Dissipativity-based distributed model predictive control with low rate communication. ''AIChE Journal'', 61:3288-3303, 2015. http://dx.doi.org/10.1002/aic.14899
 
# J. Zeng and J. Liu*. Economic model predictive control of wastewater treatment processes. ''Industrial & Engineering Chemistry Research'', 54:5710-5721, 2015. http://dx.doi.org/10.1021/ie504995n
 
# O. Xu*, J. Liu, Y. Fu and X. Chen. Dual updating strategy for moving-window partial least-squares based on model performance assessment. ''Industrial & Engineering Chemistry Research'', 54:5273-5284, 2015. http://dx.doi.org/10.1021/ie503783p
 
# S. Liu, J. Zhang and J. Liu*. Economic MPC with terminal cost and application to an oilsand primary separation vessel. ''Chemical Engineering Science'', 136:27-37, 2015. http://dx.doi.org/10.1016/j.ces.2015.01.041
 
# J. Zeng and J. Liu*. Distributed moving horizon state estimation: Simultaneously handling communication delays and data losses. ''Systems & Control Letters'', 75:56-68, 2015. http://dx.doi.org/10.1016/j.sysconle.2014.11.007
 
# S. Liu and J. Liu*. Distributed Lyapunov-based model predictive control with neighbor-to-neighbor communication. ''AIChE Journal'', 60:4124-4133, 2014. http://onlinelibrary.wiley.com/doi/10.1002/aic.14579/pdf
 
# J. Zhang, S. Liu and J. Liu*. Economic model predictive control with triggered evaluations: state and output feedback. ''Journal of Process Control'', 24:1197-1206, 2014. http://dx.doi.org/10.1016/j.jprocont.2014.03.009
 
# J. Zhang and J. Liu*. Observer-enhanced distributed moving horizon state estimation subject to communication delays. ''Journal of Process Control'', 24:672-686, 2014. http://dx.doi.org/10.1016/j.jprocont.2014.03.012
 
# M. Ellis, J. Zhang, J. Liu and P. D. Christofides*. Robust moving horizon estimation based output feedback economic model predictive control.  ''Systems & Control Letters'', 68:101-109, 2014. http://dx.doi.org/10.1016/j.sysconle.2014.03.003
 
# J. Zhang and J. Liu*. Two triggered information transmission algorithms for distributed moving horizon state estimation. ''Systems & Control Letters'', 65:1-12, 2014. http://dx.doi.org/10.1016/j.sysconle.2013.12.003
 
# S. Liu, J. Liu*, Y. Feng, and G. Rong*. Performance assessment of decentralized control systems: An iterative approach. ''Control Engineering Practice'', 22:252-263, 2014. http://dx.doi.org/10.1016/j.conengprac.2012.10.003
 
# J. Zhang and J. Liu*. Distributed moving horizon state estimation for nonlinear systems with bounded uncertainties. ''Journal of Process Control'', 23:1281-1295, 2013. http://dx.doi.org/10.1016/j.jprocont.2013.08.005
 
# Lyapunov-based MPC with robust moving horizon estimation and its triggered implementation. ''AIChE Journal'', 59:4273-4286, 2013. http://dx.doi.org/10.1002/aic.14187
 
# S. Liu, J. Zhang, J. Liu*, Y. Feng, and G. Rong.  Distributed model predictive control with asynchronous controller evaluations.  ''Canadian Journal of Chemical Engineering'', 91:1609-1620, 2013.  http://dx.doi.org/10.1002/cjce.21840
 
# J. Liu*.  Moving horizon state estimation for nonlinear systems with bounded uncertainties.  ''Chemical Engineering Science'', 93:376-386, 2013.  http://dx.doi.org/10.1016/j.ces.2013.02.030
 
# M. Heidarinejad, J. Liu, and P. D. Christofides*. Algorithms for improved fixed-time performance of Lyapunov-based economic model predictive control of nonlinear systems. ''Journal of Process Control'', 23:404-414, 2013.  http://dx.doi.org/10.1016/j.jprocont.2012.11.003
 
# P. D. Christofides*, R. Scattolini, D. Munoz de la Pena, and J. Liu.  Distributed model predictive control: A tutorial review and future research directions.  ''Computers & Chemical Engineering'', 51:21-41, 2013.  http://dx.doi.org/10.1016/j.compchemeng.2012.05.011
 
# W. Qi, J. Liu, and P. D. Christofides*.  Distributed supervisory predictive control of distributed wind and solar energy generation systems.  ''IEEE Transactions on Control System Technology'', 21:504-512, 2013.  
 
# M. Heidarinejad, J. Liu, and P. D. Christofides*.  Distributed model predictive control of switched nonlinear systems with scheduled mode transitions.  ''AIChE Journal'', 59:860-871, 2013.  http://dx.doi.org/10.1002/aic.14003
 
# M. Heidarinejad, J. Liu, and P. D. Christofides*.  Economic model predictive control of switched nonlinear systems.  ''Systems & Control Letters'', 62:77-84, 2013.  http://dx.doi.org/10.1016/j.sysconle.2012.11.002
 
# M. Heidarinejad, J. Liu, and P. D. Christofides*.  State estimation-based economic model predictive control of nonlinear systems.  ''Systems & Control Letters'', 61:926-935, 2012.  http://dx.doi.org/10.1016/j.sysconle.2012.06.007
 
# D. Chilin, J. Liu, X. Chen, and P. D. Christofides*.  Fault detection and isolation and fault tolerant control of a catalytic alkylation of benzene process.  ''Chemical Engineering Science'', 78:155-166, 2012.
 
# X. Chen, M. Heidarinejad, J. Liu, and P. D. Christofides*.  Distributed economic MPC: Application to a nonlinear chemical process network.  ''Journal of Process Control'', 22:689-699, 2012.
 
# X. Chen, M. Heidarinejad, J. Liu, and P. D. Christofides*.  Composite fast-slow MPC design for nonlinear singularly perturbed systems.  ''AIChE Journal'', 58:1802-1811, 2012.
 
# A. Leosirikul, D. Chilin, J. Liu, J. F. Davis, and P. D. Christofides*.  Monitoring and retuning of low-level PID control loops.  ''Chemical Engineering Science'', 69:287-295, 2012.
 
# M. Heidarinejad, J. Liu, and P. D. Christofides*.  Economic model predictive control of nonlinear process systems using lyapunov techniques.  ''AIChE Journal'', 58:855-870, 2012.
 
# D. Chilin, J. Liu, J. F. Davis, and P. D. Christofides*.  Data-based monitoring and reconfiguration of a distributed model predictive control system.  ''International Journal of Robust and Nonlinear Control'', 22:68-88, 2012.
 
# W. Qi, J. Liu, and P. D. Christofides*.  Supervisory predictive control for long-term scheduling of an integrated wind/solar energy generation and water desalination system.  ''IEEE Transactions on Control Systems Technology'', 20:504-512, 2012.
 
# J. Liu, X. Chen, D. Munoz de la Pena, and P. D. Christofides*.  Iterative distributed model predictive control of nonlinear systems: Handling asynchronous, delayed measurements.  ''IEEE Transactions on Automatic Control'', 57:528-534, 2012.
 
# X. Chen, M. Heidarinejad, J. Liu, D. Munoz de la Pena, and P. D. Christofides*.  Model predictive control of nonlinear singularly perturbed systems: Application to a large-scale process network.  ''Journal of Process Control'', 21:1296-1305, 2011.
 
# W. Qi, J. Liu, and P. D. Christofides*.  A distributed control framework for smart grid development: Energy/water system optimal operation and electric grid integration.  ''Journal of Process Control'', 21:1504-1516, 2011.
 
# M. Heidarinejad, J. Liu, D. Munoz de la Pena, J. F. Davis, and P. D. Christofides*.  Handling communication disruptions in distributed model predictive control of nonlinear systems.  ''Journal of Process Control'', 21:173-181, 2011.
 
# M. Heidarinejad, J. Liu, D. Munoz de la Pena, P. D. Christofides*, and J. F. Davis.  Multirate Lyapunov-based distributed model predictive control of nonlinear uncertain systems.  ''Journal of Process Control'', 21:1231-1242, 2011.
 
# W. Qi, J. Liu, X. Chen, and P. D. Christofides*.  Supervisory predictive control of stand-alone wind-solar energy generation systems.  ''IEEE Transactions on Control Systems Technology'', 19:199-207, 2011.
 
# D. Chilin, J. Liu, D. Munoz de la Pena, P. D. Christofides*, and J. F. Davis.  Detection, isolation and handling of actuator faults in distributed model predictive control systems.  ''Journal of Process Control'', 20:1059-1075, 2010.
 
# J. Liu, X. Chen, D. Munoz de la Pena, and P. D. Christofides*.  Sequential and iterative architectures for distributed model predictive control of nonlinear process systems.  ''AIChE Journal'', 56:2137-2149, 2010.
 
# J. Liu, B. J. Ohran, D. Munoz de la Pena, P. D. Christofides*, and J. F. Davis.  Monitoring and handling of actuator faults in two-tier control systems for nonlinear processes.  ''Chemical Engineering Science'', 65:3179-3190, 2010.
 
# J. Liu, D. Munoz de la Pena, and P. D. Christofides*.  Distributed model predictive control of nonlinear systems subject to asynchronous and delayed measurements.  ''Automatica'', 46:52-61, 2010.
 
# J. Liu, D. Munoz de la Pena, B. J. Ohran, P. D. Christofides*, and J. F. Davis.  A two-tier control architecture for nonlinear process systems with continuous/asynchronous feedback.  ''International Journal of Control'', 83:257-272, 2010.
 
# B. Ohran, J. Liu, D. Munoz de la Pena, P. D. Christofides*, and J. F. Davis.  Data-based fault detection and isolation using feedback control: Output feedback and optimality.  ''Chemical Engineering Science'', 64:2370-2383, 2009.
 
# J. Liu, D. Munoz de la Pena, P. D. Christofides*, and J. F. Davis.  Lyapunov-based model predictive control of nonlinear systems subject to time-varying measurement delays.  ''International Journal of Adaptive Control and Signal Processing'', 23:788-807, 2009.
 
# J. Liu, D. Munoz de la Pena, and P. D. Christofides*.  Distributed model predictive control of nonlinear process systems.  ''AIChE Journal'', 55:1171-1184, 2009.
 
# J. Liu, D. Munoz de la Pena, P. D. Christofides*, and J. F. Davis.  Lyapunov-based model predictive control of particulate processes subject to asynchronous measurements.  ''Particle and Particle Systems Characterization'', 25:360-375, 2008.
 
# J. Liu, D. Munoz de la Pena, B. J. Ohran, P. D. Christofides*, and J. F. Davis.  A two-tier architecture for networked process control.  ''Chemical Engineering Science'', 63:5394-5409, 2008.
 
# G. Rong*, J. Liu, and H. Gu.  Mining dynamic association rules in databases.  ''Control Theory & Applications'', 24:127-131, 2007.
 
# J. Liu and G. Rong*.  Application of web text mining in study assistance.  ''Information Science'', 24:400-404, 2006.
 
  
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In the current practice, the amount of water to be irrigated and the time to apply the irrigation are
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determined in advance based on the irrigator’s (typically the farmer’s) knowledge. The actual conditions
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in the field are generally not considered in determining the irrigation amount and time. Thus from a
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process systems engineering (PSE) perspective, the current irrigation practice is an open-loop decision
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making process. It is well recognized in process control that open-loop control is quite imprecise. It is indeed one of the
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main causes of the low irrigation water-use efficiency.
  
</li></ol><p class="a"><a href="publications.html#Top" target="_top"><small> Back to top </small></a><strong><a name="Chapters"></a>Book Chapters</strong><ol><li>
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To meet the challenges of water sustainability, the key is to close the decision-support loop to form
J. Liu, D. Munoz de la Pena, and P. D. Christofides.  
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a closed-loop system for precision irrigation. In the closed-loop system, sensing instruments (e.g.,
''Lyapunov-based DMPC Schemes: Sequential and Iterative Approaches,''
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soil moisture and nutrient sensors, evapotranspiration (ET) gauge, thermal cameras on drones or
Chapter 30 of ''Distributed MPC Made Easy,''
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satellite imagery data) can be used to collect various real-time field information regularly. The various
pages 479-494. Springer-Verlag, Berlin, 2014.
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field information can then be fused together to get estimates of the entire field’s conditions. The
</li><li>
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estimated field conditions could then be fed back to an adaptive or self-learning control system. The
J. Liu, D. Munoz de la Pena, and P. D. Christofides.  
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self-learning control system would then calculate the best irrigation commands for the next few hours
''Distributed Model Predictive Control System Design Using Lyapunov Techniques,''
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or day(s) based on a field model, the estimated field conditions, local weather forecast as well as other
volume 384 of ''Lecture Notes in Control and Information Science,''
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pre-specified irrigation requirements. Due to significant nonlinearities, uncertainties,
pages 181-194. Springer-Verlag, Berlin, 2009.
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and large scale of agricultural fields, there are great challenges in modeling, sensing, estimation and
</li><li>
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control algorithm development that need to be addressed. We have been actively developing agro-
J. Liu and G. Rong.  
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hydrological models, remote sensing and sensor fusing algorithms, optimal control algorithms to realize
''Mining Dynamic Association Rules in Databases, ''
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this revolutionary closed-loop smart irrigation vision in collaboration with different partners (including
volume 3801 of ''Lecture Notes in Computer Science,''
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sensing instruments provider, sprinkler manufacturer, farmers, and government agency).
pages 688-695. Springer-Verlag, Berlin, 2005.  
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</li></ol><p class="a"><a href="publications.html#Top" target="_top"><small> Back to top </small></a><strong><a name="Conferences"></a>Conference Papers </strong>''(Presenter underlined)''<ol><li><span>R. Nian</span>, J. Liu, B. Huang and T. Mutasa.  
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=== Advanced process control framework for smart manufacturing ===
Fault-tolerant control system: A reinforcement learning approach.  
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In ''Proceedings of the SICE Annual Conference'',
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The process industry is in the transition from the traditional process operations paradigm to the smart
accepted, Hiroshima, Japan, 2019.
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manufacturing paradigm. Smart manufacturing is the dramatically intensified and pervasive application of manufacturing intelligence throughout the manufacturing and supply chain enterprise. In smart manufacturing, different function modules are tightly integrated together through real-time communication and respond as a coordinated and performance-oriented enterprise, accounting for economic
</li><li>
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performance, environmental sustainability, health and safety. This transition to smart manufacturing
Y. Mao, S. Liu, <span>B. Decardi-Nelson</span> and J. Liu.
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poses significant challenges for process control and operations. We have been working on advanced
Min-max economic MPC of networked control systems with transmission delays.  
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process control techniques that enable the transition to smart manufacturing.
In ''Proceedings of the American Control Conference'',
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pages 1164-1169, Philadephia, PA, USA, 2019.
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<gallery mode="packed-overlay">
</li><li><span>L. Zhang</span>, J. Liu, W. Xie and X. Yin.  
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File:Distributedcontrol.png|''Distributed predictive control''
Robust model predictive control of the cutterhead system in tunnel boring machines.  
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File:Distributedestimation.png|''Distributed estimation and monitoring''
In ''Proceedings of the 28th International Symposium on Industrial Electronics'',  
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</gallery>
pages 2277-2282, Vancouver, Canada, 2019.
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</li><li><span>G. Yan</span>, J. Liu and B. Huang.  
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MV benchmark for networked control systems with random communication delays.  
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<u>'''''Cooperative predictive control framework'''''</u>. Model predictive control (MPC) is the most widely
In ''Proceedings of the 12th IFAC Symposium on Dynamics and Control of Process Systems'',  
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used advanced process control technique and is in the category of core manufacturing intelligence.
pages 970-975, Florianopolis, Brazil, 2019.  
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Traditionally, independently designed MPCs are used to control the important operating units in a
</li><li><span>S. Liu</span> and J. Liu. 
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plant in a decentralized fashion and there is no or limited coordination between these MPCs. In our
Economic model predictive control with zone tracking.  
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work, we have developed cooperative distributed MPC framework in which individual MPCs communicate with each other and coordinate their actions to achieve significantly improved plant-wide control
In ''Proceedings of the 6th IFAC Conference on Nonlinear Model Predictive Control'',
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performance. We have also developed economic MPC algorithms that can handle very general control
pages 16-21, Madison, WI, USA, 2018.  
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objective (e.g., maximize economic performance) directly. Process nonlinearity, stability, plant-wide
</li><li><span>Y. Xin</span> and J. Liu.
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performance as well as potential communication issues are all carefully taken into account in our designs. Our developed cooperative distributed MPC and economic MPC framework is well fitted to
State estimation of wastewater treatment plants based on reduced-order model.
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smart manufacturing and can be used for coordinated process control or act as a distributed advanced
In ''Proceedings of the 10th International Symposium on Advanced Control of Chemical Processes'', 
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decision support system.
pages 566-571, Shenyang, China, 2018.  
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</li><li>
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<u>'''''Distributed estimation and process monitoring'''''</u>. Process monitoring has played a critical role
Y. Mao, S. Liu, J. Nahar, J. Liu and F. Ding. 
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in the traditional process operations paradigm. It will continue to be one critical component in smart
Regulation of soil moisture using zone model predictive control.
+
manufacturing. In our work, we have developed distributed state estimation algorithms that are suitable
In ''Proceedings of the 10th International Symposium on Advanced Control of Chemical Processes'', 
+
for process monitoring in smart manufacturing. In our developed algorithms, there are more than one
pages 756-761, Shenyang, China, 2018.
+
estimators that can communicate to exchange information and work collaboratively to estimate the state
</li><li>
+
of the entire process network. We have developed a very general distributed state estimation
J. McAllister, J. Liu, Z. Li and U. Simonsmeier. 
+
framework for process monitoring purpose. The developed distributed estimation framework is unique
Erythropoiesis-stimulating-agent dose optimization for anemia management in chronic kidney disease using recursive constrained modeling and zone model predictive control.  
+
in that it does not require the local subsystem estimators to be of the same type. It may be used to
In ''Proceedings of the American Control Conference'',
+
transform existing decentralised estimators to a distributed state estimator network, which could be
pages 2326-2331, Milwaukee, Wisconsin, 2018.  
+
very useful as we transforming existing facilities to smart plants.
</li><li><span>B. Decardi-Nelson</span>, S. Liu and J. Liu. 
+
 
A comparison of economic and tracking model predictive control of a post combustion CO2 capture process.  
+
<u>'''''Process networks decomposition'''''</u>. As we move towards smart manufacturing, complex and tightly
In ''Proceedings of the American Control Conference'', 
+
integrated process networks are becoming more and more common in manufacturing industries due
pages 3921-3926, Milwaukee, Wisconsin, 2018.  
+
to their economic efficiency. A typical process network consists of several operating units, which are
</li><li>
+
connected with each other via material, energy and information flows. When considering cooperative
J. Nahar, J. Liu and <span>S. L. Shah</span>. 
+
distributed predictive control of these process networks, how to decompose the process network for
Observability analysis of an agro-hydrological system.
+
distributed control system design is very important and is a fundamental problem. Improper subsystem
In ''Proceedings of the Control Conference of Africa'', 
+
decomposition may lead to the increase of communication and computation burdens, reduced control
50-2:110-114, Johannesburg, South Africa, 2017.
+
performance, or even instability of the entire system. We have investigated this fundamental problem
</li><li><span>S. Liu</span> and J. Liu. 
+
from different perspectives including time-scale multiplicity, physical closeness and sensitivity between
A terminal cost for economic model predictive control with local optimality.
+
different variables, and community structure detection based on representing a system as a graph.
In ''Proceedings of the American Control Conference'', 
+
 
pages 1954-1959, Seattle, WA, 2017.
+
=== Applications to energy systems, wastewater treatment, and anemia management ===
</li><li><span>X. Yin</span>, J. Zeng and J. Liu. 
+
 
From decentralized to distributed state estimation.
+
We have applied our developed advanced process control techniques to optimize the operations of
In ''Proceedings of the American Control Conference'', 
+
various energy systems including wind-solar systems, deep cone thickener and froth flotation process
pages 1904-1909, Seattle, WA, 2017.
+
in coal beneficiation, primary separation vessel in oilsands separation, steam assisted gravity drainage
</li><li>
+
oil recovery processes, post-combustion CO2 capture plants, and coal-fired boiler-turbine systems. We
X. Yin and <span>J. Liu</span>.
+
have also developed model reduction, decomposition, and optimal control algorithms for wastewater
Distributed output feedback fault tolerant detection and isolation for cascade process networks.
+
treatment plants. Features of these systems are explicitly taken into account in our algorithms.
In ''Proceedings of the 6th International Symposium on Advanced Control of Industrial Processes'',
+
 
pages 547-552, Taipei, Taiwan, 2017 (Keynote paper).
+
We have also applied advanced process control techniques to develop modeling and control algorithms
</li><li>
+
for anemia management in chronic kidney disease. Chronic kidney disease affects millions of people
J. Ren, J. McAllister, Z. Li, <span>J. Liu</span> and U. Simonsmeier.
+
throughout the world today. In this work, we have developed an economic zone MPC algorithm to
Modeling of hemoglobin response to erythropoietin therapy through constrained optimization.
+
optimally recommend erythropoietin (EPO) dosing amounts to the physician to maintain a patient’
In ''Proceedings of the 6th International Symposium on Advanced Control of Industrial Processes'',
+
hemoglobin within a target range. We have also developed a high fidelity patient simulator based on
pages 245-250, Taipei, Taiwan, 2017.
+
pharmacokinetics and pharmacodynamics model and identified patient parameters for control perfor-
</li><li><span>C. Zheng</span>, J. Bao and J. Liu.
+
mance evaluation. The developed algorithms are currently implemented in the commercial product of
Robust control of plantwide chemical processes based on parameter dependent dissipativity.
+
our industrial partner.
In ''Proceedings of 2016 Australian Control Conference'',
 
pages 305-310, Newcastle, Australia, 2016.
 
</li><li><span>T. An</span>, J. Liu and J. F. Forbes.
 
Coordinated distributed MHE for linear systems.
 
In ''Proceedings of the 55th IEEE Conference on Decision and Control'',
 
pages 105-110, Las Vegas, USA, 2016.
 
</li><li><span>J. Zeng</span>, J. Liu, T. Zou and D. Yuan.
 
State estimation of wastewater treatment processes using distributed extended Kalman filters.
 
In ''Proceedings of the 55th IEEE Conference on Decision and Control'',
 
pages 6721--6726, Las Vegas, USA, 2016.
 
</li><li><span>X. Yin</span>, K. Arulmaran and J. Liu.
 
Subsystem decomposition for distributed state estimation of nonlinear systems.
 
In ''Proceedings of the American Control Conference'',
 
pages 5569-5574, Boston, MA, 2016.
 
</li><li><span>S. Liu</span> and J. Liu.
 
Economic model predictive control for scheduled switching operations.
 
In ''Proceedings of the American Control Conference'',
 
pages 1784-1789, Boston, MA, 2016.
 
</li><li><span>S. Liu</span>, J. Zhang and J. Liu.
 
Economic MPC with terminal cost and application to oilsand separation.
 
In ''Proceedings of the 9th International Symposium on Advanced Control of Chemical Processes'',
 
pages 20-25, Whistler, British Columbia, Canada, 2015.
 
</li><li><span>M. Rashedi</span>, J. Liu and B. Huang.
 
Distributed adaptive high-gain extended Kalman filtering for nonlinear systems.
 
In ''Proceedings of the 9th International Symposium on Advanced Control of Chemical Processes'',
 
pages 158-163, Whistler, British Columbia, Canada, 2015.
 
</li><li>
 
M. J. Tippett, <span>C. Zheng</span>, J. Bao and J. Liu.
 
Dissipativity-based analysis of controller networks with reduced rate communication.
 
In ''Proceedings of the 9th International Symposium on Advanced Control of Chemical Processes'',
 
pages 705-710, Whistler, British Columbia, Canada, 2015.
 
</li><li>
 
        J. Zeng and <span>J. Liu</span>.
 
        Distributed moving horizon estimation subject to communication delays and losses.
 
        In ''Proceedings of the American Control Conference'',
 
        pages 5533-5538, Chicago, IL, 2015.
 
</li><li>
 
        S. Li, <span>J. Liu</span> and J. F. Forbes.
 
        Convergence properties of two coordinated distributed MPC algorithms.
 
        In ''Proceedings of the American Control Conference'',
 
        pages 5377-5383, Chicago, IL, 2015.
 
</li><li><span>J. Nahar</span>, J. Liu and S. L. Shah.
 
        A systems engineering approach for automated irrigation.
 
        In ''Proceedings of the International Conference on Chemical Engineering 2014 (ICChE2014)'',
 
        pages 167-171, Dhaka, Bangladesh, 2014.
 
</li><li><span>B. Hassanzadeh</span>, J. Liu and J. F. Forbes.
 
        An analytic price-driven coordination scheme for distributed model predictive control systems.
 
        In ''Proceedings of the 53rd IEEE Conference on Decision and Control'',
 
        pages 2461-2466, Los Angeles, CA, USA, 2014.
 
    </li><li><span>J. Zhang</span>  and J. Liu.
 
Distributed moving horizon state estimation with triggered communication.
 
In ''Proceedings of the American Control Conference'',
 
pages 5700-5705, Portland, OR, USA, 2014.
 
</li><li><span>C. Zheng</span>, M. J. Tippett, J. Bao and J. Liu.
 
Multirate dissipativity-based distributed MPC.
 
In ''Proceedings of the Australian Control Conference'',
 
pages 325-330, Perth, Australia, 2013.
 
</li><li><span>J. Liu</span>.
 
Robust moving horizon state estimation for nonlinear systems.
 
In ''Proceedings of the American Control Conference'',
 
pages 253-258, Washington DC, USA, 2013.
 
</li><li>
 
        S. Liu, <span>J. Liu</span>, Y. Feng, and G. Rong.
 
        Achievable performance of decentralized control systems.
 
In ''Proceedings of the American Control Conference'',
 
pages 5817-5822, Washington DC, USA, 2013.
 
</li><li><span>B. Hassanzadeh</span>, H. Pakravesh, J. Liu and J. F. Forbes.
 
Coordinated-distributed MPC of nonlinear systems based on price-driven coordination.
 
In ''Proceedings of the American Control Conference'',
 
pages 3159--3164, Washington DC, USA, 2013.
 
</li><li>
 
M. Heidarinejad, J. Liu, and <span>P. D. Christofides</span>.
 
On fixed-time performance of Lyapunov-based economic model predictive control of nonlinear systems.
 
In ''Proceedings of the American Control Conference'',
 
pages 3171--3176, Washington DC, USA, 2013.
 
</li><li><span>M. J. Tippett</span>, J. Bao, and J. Liu.
 
Plant-wide control of chemical systems exhibiting time-scale separation.
 
In '' Proceedings of CHEMECA 2012'',
 
pager 118, Wellington, New Zealand, 2012.
 
</li><li>
 
A. Leosirikul, D. Chilin, <span>J. Liu</span>, J. F. Davis, and P. D. Christofides.
 
Monitoring of low-level PID control loops.
 
In '' Proceedings of the American Control Conference'',
 
pages 5664-5669, Montreal, Canada, 2012.
 
</li><li>
 
M. Heidarinejad, <span>J. Liu</span>, and P. D. Christofides.
 
Distributed model predictive control of switched nonlinear systems.
 
In ''Proceedings of the American Control Conference,''
 
pages 3198-3203, Montreal, Canada, 2012.
 
</li><li>
 
X. Chen, M. Heidarinejad, <span>J. Liu</span>, and P. D. Christofides.
 
Composite fast-slow MPC design for nonlinear singularly perturbed systems: Stability analysis.
 
In ''Proceedings of the American Control Conference'',
 
pages 4136-4141, Montreal, Canada, 2012.
 
</li><li><span>P. D. Christofides</span>, R. Scattolini, D. Munoz de la Pena, and J. Liu.
 
Distributed model predictive control: A tutorial review.
 
In ''Proceedings of Chemical Process Control-8'',
 
22 pages, Savannah, Georgia, 2012.
 
</li><li>
 
X. Chen, <span>M. Heidarinejad</span>, J. Liu, D. Munoz de la Pena, and P. D. Christofides.
 
Model predictive control of nonlinear singularly perturbed systems: Application to a reactor-separator process network.
 
In ''Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference'',
 
pages 8125-8132, Orlando, Florida, 2011.
 
</li><li><span>M. Heidarinejad</span>, J. Liu, and P. D. Christofides.
 
Lyapunov-based economic model predictive control of nonlinear systems: Handling asynchronous, delayed measurements and distributed implementation.
 
In ''Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference'',
 
pages 4646-4653, Orlando, Florida, 2011.
 
</li><li><span>D. Chilin</span>, J. Liu, J. F. Davis, and P. D. Christofides.
 
Data-based monitoring and reconfiguration of a distributed model predictive control system.
 
In ''Proceedings of the American Control Conference'',
 
pages 3158-3165, San Francisco, California, 2011.
 
</li><li><span>M. Heidarinejad</span>, J. Liu, and P. D. Christofides.
 
Lyapunov-based economic model predictive control of nonlinear systems.
 
In ''Proceedings of the American Control Conference'',
 
pages 5195-5200, San Francisco, California, 2011.
 
</li><li><span>M. Heidarinejad</span>, J. Liu, D. Munoz de la Pena, J. F. Davis, and P. D. Christofides.
 
Multirate distributed model predictive control of nonlinear systems.
 
In ''Proceedings of the American Control Conference'',
 
pages 5181-5188, San Francisco, Califorina, 2011.
 
</li><li>
 
W. Qi, <span>J. Liu</span>, and P. D. Christofides.
 
A two-time-scale framework for supervisory predictive control of an integrated wind/solar energy generation and water desalination system.
 
In ''Proceedings of the American Control Conference'',
 
pages 2677-2682, San Francisco, California, 2011.
 
</li><li><span>J. Liu</span>, X. Chen, D. Munoz de la Pena, and P. D. Christofides.
 
Iterative distributed model predictive control of nonlinear systems: Handling delayed measurements.
 
In ''Proceedings of the 49th IEEE Conference on Decision and Control'',
 
pages 7251-7258, Atlanta, Georgia, 2010.
 
</li><li>
 
X. Chen, J. Liu, D. Munoz de la Pena, and <span>P. D. Christofides</span>.
 
Sequential and iterative distributed model predictive control of nonlinear process systems subject to asynchronous measurements.
 
In ''Proceedings of the 9th IFAC Symposium on Dynamics and Control of Process Systems'',
 
pages 611-616, Leuven, Belgium, 2010.
 
</li><li>
 
M. Heidarinejad, J. Liu, D. Munoz de la Pena, and <span>P. D. Christofides</span>.
 
Handling communication disruptions in distributed model predictive control of nonlinear systems.
 
In ''Proceedings of the 9th IFAC Symposium on Dynamics and Control of Process Systems'',
 
pages 282-287, Leuven, Belgium, 2010.
 
</li><li>
 
W. Qi, J. Liu, and <span>P. D. Christofides</span>.
 
Supervisory predictive control of an integrated wind/solar energy generation and water desalination system.
 
In ''Proceedings of the 9th IFAC Symposium on Dynamics and Control of Process Systems'',
 
pages 821-826, Leuven, Belgium, 2010.
 
</li><li>
 
D. Chilin, <span>J. Liu</span>, D. Munoz de la Pena, P. D. Christofides, and J. F. Davis.
 
Monitoring and handling of actuator faults in a distributed model predictive control system.
 
In ''Proceedings of the American Control Conference'',
 
pages 2847-2854, Baltimore, Maryland, 2010.
 
</li><li><span >J. Liu</span>, X. Chen, D. Munoz de la Pena, and P. D. Christofides.
 
Sequential and iterative architectures for distributed model predictive control of nonlinear process systems. Part I: Theory.
 
In ''Proceedings of the American Control Conference'',
 
pages 3148-3155, Baltimore, Maryland, 2010.
 
</li><li><span>J. Liu</span>, X. Chen, D. Munoz de la Pena, and P. D. Christofides.
 
Sequential and iterative architectures for distributed model predictive control of nonlinear process systems. Part II: Application to a catalytic
 
alkylation of benzene process.
 
In ''Proceedings of the American Control Conference'',
 
pages 3156-3161, Baltimore, Maryland, 2010.
 
</li><li><span>J. Liu</span>, D. Munoz de la Pena, and P. D. Christofides.
 
Distributed model predictive control of nonlinear systems subject to delayed measurements.
 
In ''Proceedings of the 48th IEEE Conference on Decision and Control'',
 
pages 7105-7112, Shanghai, China, December 2009.
 
</li><li>
 
B. Ohran, <span>J. Liu</span>, P. D. Christofides, D. Munoz de la Pena, and J. F. Davis.
 
Networked monitoring and fault-tolerant control of nonlinear process systems.
 
In ''Proceedings of the 48th IEEE Conference on Decision and Control'',
 
pages 4117-4124, Shanghai, China, December 2009.
 
</li><li>
 
B. Ohran, J. Liu, <span>P. D. Christofides</span>, D. Munoz de la Pena, and J. F. Davis.
 
Data-based fault detection and isolation using output feedback control.
 
In ''Proceedings of IFAC International Symposium on Advanced Control of Chemical Processes'',
 
paper 107, 6 pages, Instabul, Turkey, 2009.
 
</li><li>
 
J. Liu, D. Munoz de la Pena, and <span>P. D. Christofides</span>.
 
Distributed model predictive control of nonlinear process systems using asynchronous measurements.
 
In ''Proceedings of IFAC International Symposium on Advanced Control of Chemical Processes'',
 
paper 111, 6 pages, Istanbul, Turkey, 2009.
 
</li><li><span>J. Liu</span>, D. Munoz de la Pena, and P. D. Christofides.
 
Distributed model predictive control of nonlinear systems with input constraints.
 
In ''Proceedings of the American Control Conference'',
 
pages 2319-2326, St. Louis, Missouri, 2009.
 
</li><li><span>J. Liu</span>, D. Munoz de la Pena, B. J. Ohran, P. D. Christofides, and J. F. Davis.
 
A two-tier control architecture for nonlinear process systems with continuous/asynchronous feedback.
 
In ''Proceedings of the American Control Conference'',
 
pages 133-140, St. Louis, Missouri, 2009.
 
</li><li>
 
J. Liu, <span>D. Munoz de la Pena</span>, and P. D. Christofides.
 
Distributed control system design using Lyapunov-based model predictive control.
 
In ''Proceedings of International Workshop on Assessment and Future Directions of Nonlinear Model Predictive Control'',
 
12 pages, Pavia, Italy, 2008.
 
</li><li><span>J. Liu</span>, D. Munoz de la Pena, P. D. Christofides, and J. F. Davis.
 
Lyapunov-based predictive control of particulate processes subject to asynchronous measurements.
 
In ''Proceedings of the American Control Conference'',
 
pages 2233-2240, Seattle, Washington, 2008.
 
</li><li><span>J. Liu</span>, D. Munoz de la Pena, P. D. Christofides, and J. F. Davis.
 
Lyapunov-based model predictive control of nonlinear systems subject to time-varying measurement delays.
 
In ''Proceedings of the 47th IEEE Conference on Decision and Control'',
 
pages 4632-4639, Cancun, Mexico, 2008.
 
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Latest revision as of 20:27, 6 January 2021


We work in the general area of process systems and control engineering. Our primary research objective is to develop computing technologies for smart and sustainable manufacturing and production using systems, computing and engineering principles. We are actively working on the following projects:

Smart closed-loop agriculture irrigation

Water is essential for our daily life and is at the core of sustainable development. While about 70% of the Earth’s surface is covered by water, less than 2.5% of it is freshwater. The global water needs of the fast growing world population are already exceeding the currently available fresh water supply. The largest use of fresh water is in agriculture, which accounts for about 70% of the global fresh water consumption. As population growth continues, 60% more food will be needed to satisfy the demand of more than 9 billion people worldwide by 2050. However, in many regions (even the ones in water-rich countries such as USA and Canada), water allocated to irrigation is largely capped or will be capped in the near future and any further increase in irrigated acres needs to come essentially from improvements in water-use efficiency. The irrigation water-use efficiency worldwide is currently very low (around 50% to 60%). It is clear that policies related to water conservation and new technologies for more efficient water consumption need to be developed.

In the current practice, the amount of water to be irrigated and the time to apply the irrigation are determined in advance based on the irrigator’s (typically the farmer’s) knowledge. The actual conditions in the field are generally not considered in determining the irrigation amount and time. Thus from a process systems engineering (PSE) perspective, the current irrigation practice is an open-loop decision making process. It is well recognized in process control that open-loop control is quite imprecise. It is indeed one of the main causes of the low irrigation water-use efficiency.

To meet the challenges of water sustainability, the key is to close the decision-support loop to form a closed-loop system for precision irrigation. In the closed-loop system, sensing instruments (e.g., soil moisture and nutrient sensors, evapotranspiration (ET) gauge, thermal cameras on drones or satellite imagery data) can be used to collect various real-time field information regularly. The various field information can then be fused together to get estimates of the entire field’s conditions. The estimated field conditions could then be fed back to an adaptive or self-learning control system. The self-learning control system would then calculate the best irrigation commands for the next few hours or day(s) based on a field model, the estimated field conditions, local weather forecast as well as other pre-specified irrigation requirements. Due to significant nonlinearities, uncertainties, and large scale of agricultural fields, there are great challenges in modeling, sensing, estimation and control algorithm development that need to be addressed. We have been actively developing agro- hydrological models, remote sensing and sensor fusing algorithms, optimal control algorithms to realize this revolutionary closed-loop smart irrigation vision in collaboration with different partners (including sensing instruments provider, sprinkler manufacturer, farmers, and government agency).

Advanced process control framework for smart manufacturing

The process industry is in the transition from the traditional process operations paradigm to the smart manufacturing paradigm. Smart manufacturing is the dramatically intensified and pervasive application of manufacturing intelligence throughout the manufacturing and supply chain enterprise. In smart manufacturing, different function modules are tightly integrated together through real-time communication and respond as a coordinated and performance-oriented enterprise, accounting for economic performance, environmental sustainability, health and safety. This transition to smart manufacturing poses significant challenges for process control and operations. We have been working on advanced process control techniques that enable the transition to smart manufacturing.


Cooperative predictive control framework. Model predictive control (MPC) is the most widely used advanced process control technique and is in the category of core manufacturing intelligence. Traditionally, independently designed MPCs are used to control the important operating units in a plant in a decentralized fashion and there is no or limited coordination between these MPCs. In our work, we have developed cooperative distributed MPC framework in which individual MPCs communicate with each other and coordinate their actions to achieve significantly improved plant-wide control performance. We have also developed economic MPC algorithms that can handle very general control objective (e.g., maximize economic performance) directly. Process nonlinearity, stability, plant-wide performance as well as potential communication issues are all carefully taken into account in our designs. Our developed cooperative distributed MPC and economic MPC framework is well fitted to smart manufacturing and can be used for coordinated process control or act as a distributed advanced decision support system.

Distributed estimation and process monitoring. Process monitoring has played a critical role in the traditional process operations paradigm. It will continue to be one critical component in smart manufacturing. In our work, we have developed distributed state estimation algorithms that are suitable for process monitoring in smart manufacturing. In our developed algorithms, there are more than one estimators that can communicate to exchange information and work collaboratively to estimate the state of the entire process network. We have developed a very general distributed state estimation framework for process monitoring purpose. The developed distributed estimation framework is unique in that it does not require the local subsystem estimators to be of the same type. It may be used to transform existing decentralised estimators to a distributed state estimator network, which could be very useful as we transforming existing facilities to smart plants.

Process networks decomposition. As we move towards smart manufacturing, complex and tightly integrated process networks are becoming more and more common in manufacturing industries due to their economic efficiency. A typical process network consists of several operating units, which are connected with each other via material, energy and information flows. When considering cooperative distributed predictive control of these process networks, how to decompose the process network for distributed control system design is very important and is a fundamental problem. Improper subsystem decomposition may lead to the increase of communication and computation burdens, reduced control performance, or even instability of the entire system. We have investigated this fundamental problem from different perspectives including time-scale multiplicity, physical closeness and sensitivity between different variables, and community structure detection based on representing a system as a graph.

Applications to energy systems, wastewater treatment, and anemia management

We have applied our developed advanced process control techniques to optimize the operations of various energy systems including wind-solar systems, deep cone thickener and froth flotation process in coal beneficiation, primary separation vessel in oilsands separation, steam assisted gravity drainage oil recovery processes, post-combustion CO2 capture plants, and coal-fired boiler-turbine systems. We have also developed model reduction, decomposition, and optimal control algorithms for wastewater treatment plants. Features of these systems are explicitly taken into account in our algorithms.

We have also applied advanced process control techniques to develop modeling and control algorithms for anemia management in chronic kidney disease. Chronic kidney disease affects millions of people throughout the world today. In this work, we have developed an economic zone MPC algorithm to optimally recommend erythropoietin (EPO) dosing amounts to the physician to maintain a patient’ hemoglobin within a target range. We have also developed a high fidelity patient simulator based on pharmacokinetics and pharmacodynamics model and identified patient parameters for control perfor- mance evaluation. The developed algorithms are currently implemented in the commercial product of our industrial partner.