Volume 12 Number 4
August 2015
Article Contents
Michael J. Tippett and Jie Bao. Distributed Control of Chemical Process Networks. International Journal of Automation and Computing, vol. 12, no. 4, pp. 368-381, 2015. doi: 10.1007/s11633-015-0895-9
Cite as: Michael J. Tippett and Jie Bao. Distributed Control of Chemical Process Networks. International Journal of Automation and Computing, vol. 12, no. 4, pp. 368-381, 2015. doi: 10.1007/s11633-015-0895-9

Distributed Control of Chemical Process Networks

  • Received: 2014-12-01
Fund Project:

This work was supported by Australian Research Council (ARC) Discovery Project (No.DP130103330)

  • In this paper, we present a review of the current literature on distributed (or partially decentralized) control of chemical process networks. In particular, we focus on recent developments in distributed model predictive control, in the context of the specific challenges faced in the control of chemical process networks. The paper is concluded with some open problems and some possible future research directions in the area.
  • [1] A. M. Lenhoff, M. Morari. Design of resilient processing plants-I. Process design under consideration of dynamic aspects. Chemical Engineering Science, vol. 37, no. 2, pp. 245-258, 1982.
    [2] P. A. Bahri, J. A. Bandoni, J. A. Romagnoli. Integrated flexibility and controllability analysis in design of chemical processes. American Institute of Chemical Engineers Journal, vol. 43, no. 4, pp. 997-1015, 1997.
    [3] J. D. Perkins, S. P. K. Walsh. Optimization as a tool for design/control integration. Computers and Chemical Engineering, vol. 20, no. 4, pp. 315-323, 1996.
    [4] A. Kumar, P. Daoutidis. Nonlinear dynamics and control of process systems with recycle. Journal of Process Control, vol. 12, no. 4, pp. 475-484, 2002.
    [5] P. D. Christofides, R. Scattolini, D. Muñoz de la Peña, J. F. Liu. Distributed model predictive control: A tutorial review and future research directions. Computers and Chemical Engineering, vol. 51, pp. 21-41, 2013.
    [6] S. Skogestad, M. Morari. Robust performance of decentralized control systems. Automatica, vol. 25, no. 1, pp. 119-125, 1989.
    [7] A. Swarnakar, H. J. Marquez, T. W. Chen. Multi-loop control synthesis for unstable systems and its application: An approach based on μ interaction measure. International Journal of Robust and Nonlinear Control, vol. 19, no. 15, pp. 1721-1744, 2009.
    [8] S. Skogestad. Control structure design for complete chemical plants. Computers and Chemical Engineering, vol. 28, no. 1-2, pp. 219-234, 2004.
    [9] Y. Ho. On centralized optimal control. IEEE Transactions on Automatic Control, vol. 50, no. 4, pp. 537-538, 2005.
    [10] T. Larsson, S. Skogestad. Plantwide control-a review and a new design procedure. Modeling, Identification and Control, vol. 21, no. 4, pp. 209-240, 2000.
    [11] J. F. Liu, X. Z. Chen, D. Muñoz de la Peña, P. D. Christofides. Sequential and iterative architectures for distributed model predictive control of nonlinear process systems. American Institute of Chemical Engineers Journal, vol. 56, no. 8, pp. 2137-2149, 2010.
    [12] R. Scattolini. Architectures for distributed and hierarchical model predictive control-a review. Journal of Process Control, vol. 19, no. 5, pp. 723-731, 2009.
    [13] W. L. Luyben, B. D. Tyréus, M. L. Luyben. Plantwide Process Control, New York, USA: McGraw-Hill, 1998.
    [14] D. Hioe, J. Bao, B. E. Ydstie. Dissipativity analysis for networks of process systems. Computers and Chemical Engineering, vol. 50, pp. 207-219, 2013.
    [15] W. L. Luyben. Snowball effects in reactor separator processes with recycle. Industrial & Engineering Chemistry Research, vol. 33, no. 2, pp. 299-305, 1994.
    [16] S. Skogestad. Plantwide control: The search for the selfoptimizing control structure. Journal of Process Control, vol. 10, no. 5, pp. 487-507, 2000.
    [17] Y. Cao, Z. J. Yang. Multiobjective process controllability analysis. Computers and Chemical Engineering, vol. 28, no. 1-2, pp. 83-90, 2004.
    [18] Y. Cao. Direct and indirect gradient control for static optimisation. International Journal of Automation and Computing, vol. 2, no. 1, pp. 60-66, 2006.
    [19] R. D’Andrea, G. E. Dullerud. Distributed control design for spatially interconnected systems. IEEE Transactions on Automatic Control, vol. 48, no. 9, pp. 1478-1495, 2003.
    [20] N. Hudon, J. Bao. Dissipativity-based decentralized control of interconnected nonlinear chemical processes. Computers and Chemical Engineering, vol. 45, pp. 84-101, 2012.
    [21] B. T. Stewart, S. J. Wright, J. B. Rawlings. Cooperative distributed model predictive control for nonlinear systems. Journal of Process Control, vol. 21, no. 5, pp. 698-704, 2011.
    [22] C. Langbort, V. Gupta. Minimal interconnection topology in distributed control design. SIAM Journal on Control and Optimization, vol. 48, no. 1, pp. 397-413, 2009.
    [23] C. Peng, Y. C. Tian, M. Tadé. State feedback controller design of networked control systems with interval time-varying delay and nonlinearity. International Journal of Robust and Nonlinear Control, vol. 18, no. 12, pp. 1285-1301, 2008.
    [24] J. F. Liu, D. Muñoz de la Peña, B. J. Ohran, P. D. Christofides, J. F. Davis. A two-tier architecture for networked process control. Chemical Engineering Science, vol. 63, no. 22, pp. 5394-5409, 2008.
    [25] L. Acar. Boundaries of the receding horizon control for interconnected systems. Journal of Optimization Theory and Applications, vol. 84, no. 2, pp. 251-271, 1995.
    [26] P. D. Christofides, J. F. Liu, D. Muñoz de la Peña. Networked and Distributed Predictive Control: Methods and Nonlinear Process Network Applications, New York, USA: Springer, 2011.
    [27] M. Farina, R. Scattolini. Distributed noncooperative MPC with neighbor-to-neighbor communication. In Proceedings of the 18th IFAC World Congress, IFAC, Milano, Italy, pp. 404-409, 2011.
    [28] M. Farina, R. Scattolini. Distributed predictive control: A non-cooperative algorithm with neighbor-to-neighbor communication for linear systems. Automatica, vol. 48, no. 6, pp. 1088-1096, 2012.
    [29] G. Betti, M. Farina, R. Scattolini. Realization issues, tuning, and testing of a distributed predictive control algorithm. Journal of Process Control, vol. 24, no. 4, pp. 424-434, 2014.
    [30] W. B. Dunbar. Distributed receding horizon control of dynamically coupled nonlinear systems. IEEE Transactions on Automatic Control, vol. 52, no. 7, pp. 1249-1263, 2007.
    [31] W. B. Dunbar, S. Desa. Distributed nonlinear model predictive control for dynamic supply chain management. In Proceedings of the International Workshop on Assessment and Future Directions on NMPC, Freudenstadt-Lauterbad, Germany, 2005.
    [32] G. Y. Zhu, M. A. Henson. Model predictive control of interconnected linear and nonlinear processes. Industrial & Engineering Chemistry Research, vol. 41, no. 4, pp. 801-816, 2002.
    [33] P. Mhaskar, N. H. El-Farra, P. D. Christofides. Predictive control of switched nonlinear systems with scheduled mode transitions. IEEE Transactions on Automatic Control, vol. 50, no. 11, pp. 1670-1680, 2005.
    [34] P. Mhaskar, N. H. El-Farra, P. D. Christofides. Stabilization of nonlinear systems with state and control constraints using Lyapunov-based predictive control. Systems & Control Letters, vol. 55, no. 8, pp. 650-659, 2006.
    [35] M. Ellis, M. Heidarinejad, P. D. Christofides. Economic model predictive control of nonlinear two-time-scale systems. In Proceedings of the 21st Mediterranean Conference on Control and Automation, IEEE, Platanias-Chania, Crete, Greece, pp. 323-328, 2013.
    [36] M. Ellis, J. Zhang, J. F. Liu, P. D. Christofides. Robust moving horizon estimation based output feedback economic model predictive control. System & Control Letters, vol. 68, pp. 101-109, 2014.
    [37] J. Zhang, S. Liu, J. F. Liu. Economic model predictive control with triggered evaluations: State and output feedback. Journal of Process Control, vol. 24, no. 8, pp. 1197-1206, 2014.
    [38] Y. J. Wang, J. B. Rawlings. A new robust model predictive control method I: Theory and computation. Journal of Process Control, vol. 14, no. 3, pp. 231-247, 2004.
    [39] P. O. M. Scokaert, D. Q. Mayne. Min-max feedback model predictive control for constrained linear systems. IEEE Transactions on Automatic Control, vol. 43, no. 8, pp. 1136-1142, 1998.
    [40] H. Genceli, M. Nikolaou. Robust stability analysis of constrained l1-norm model predictive control. American Institute of Chemical Engineers Journal, vol. 39, no. 12, pp. 1954-1965, 1993.
    [41] D. Jia, B. H. Krogh. Min-max feedback model predictive control for distributed control with communication. In Proceedings of the American Control Conference, IEEE, Anchorage, USA, pp. 4507-4512, 2002.
    [42] A. Richards, J. P. How. Robust distributed model predictive control. International Journal of Control, vol. 80, no. 9, pp. 1517-1531, 2007.
    [43] D. Q. Mayne, J. B. Rawlings, C. V. Rao, P. O. M. Scokaert. Constrained predictive control: Stability and optimality. Automatica, vol. 36, no. 6, pp. 789-814, 2000.
    [44] M. J. Tippett, J. Bao. Plant-wide dissipative model predictive control. AIChE Journal, vol. 59, no. 3 pp. 787-804, 2013.
    [45] M. J. Tippett, J. Bao. A unified approach to plant-wide dissipative model predictive control. In Proceedings of the International Symposium on Advanced Control of Chemical Processes, IFAC, Singapore, pp. 420-425, 2012.
    [46] T. Raff, C. Ebenbauer, P. Allg¨ower. Nonlinear model predictive control: A passivity-based approach. In Proceedings of the International Workshop on Assessment and Future Directions of Nonlinear Model Predictive Control, Freudenstadt-Lauterbad, Germany, pp. 151-162, 2005.
    [47] M. J. Tippett, J. Bao. Distributed dissipative model predictive control for process networks with imperfect communication. AIChE Journal, vol. 60, no. 5, pp. 1682-1699, 2014.
    [48] C. X. Zheng, M. J. Tippett, J. Bao, J. F. Liu. Multirate dissipativity-based distributed MPC. In Proceedings of the 3rd Australian Control Conference, IEEE, Perth, Australia, pp. 325-330, 2013.
    [49] C. Kojima, K. Takaba. A generalized Lyapunov stability theorem for discrete-time systems based on quadratic difference forms. In Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, IEEE, Seville, Spain, pp. 2911-2916, 2005.
    [50] C. Kojima, K. Takaba. An LMI condition for asymptotic stability of discrete-time system based on quadratic difference forms. In Proceedings of IEEE Conference on Computer Aided Control Systems Design, IEEE, Munich, Germany, pp. 1139-1143, 2006.
    [51] J. B. Rawlings, B. T. Stewart. Coordinating multiple optimization-based controllers: New opportunities and challenges. Journal of Process Control, vol. 18, no. 9, pp. 839-845, 2008.
    [52] B. T. Stewart, A. N. Venkat, J. B. Rawlings, S. J. Wright, G. Pannocchia. Cooperative distributed model predictive control. Systems & Control Letters, vol. 59, no. 8, pp. 460-469, 2010.
    [53] A. N. Venkat, J. B. Rawlings, S. J. Wright. Stability and optimality of distributed model predictive control. In Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, IEEE, Seville, Spain, pp. 6680-6685, 2005.
    [54] A. N. Venkat, J. B. Rawlings, S. J. Wright. Implementable distributed model predictive control with guaranteed performance properties. In Proceedings of the American Control Conference, IEEE, Minneapolis, USA, 2006.
    [55] P. O. M. Scokaert, D. Q. Mayne, J. B. Rawlings. Suboptimal model predictive control (feasibility implies stability). IEEE Transactions on Automatic Control, vol. 44, no. 3, pp. 648-654, 1999.
    [56] A. Ferramosca, D. Limon, I. Alvarado, E. F. Camacho. Cooperative distributed MPC for tracking. Automatica, vol. 49, no. 4, pp. 906-914, 2013.
    [57] H. Scheu, J. Busch, W. Marquardt. Nonlinear distributed dynamic optimization based on first order sensitivities. In Proceedings of the American Control Conference, IEEE, Baltimore, USA, pp. 1574-1579, 2010.
    [58] H. Scheu, W. Marquardt. Sensitivity-based coordination in distributed model predictive control. Journal of Process Control, vol. 21, no. 5, pp. 715-728, 2011.
    [59] J. M. Maestre, D. Muñoz de la Peña, E. F. Camacho. Distributed model predictive control based on a cooperative game. Optimal Control Applications and Methods, vol. 32, no. 2, pp. 153-176, 2011.
    [60] J. M. Maestre, D. Muñoz de la Peña, E. F. Camacho, T. Alamo. Distributed model predictive control based on agent negotiation. Journal of Process Control, vol. 21, no. 5, pp. 685-697, 2011.
    [61] J. F. Liu, D. Muñoz de la Peña, P. D. Christofides. Distributed model predictive control of nonlinear process systems. American Institute of Chemical Engineers Journal, vol. 55, no. 5, pp. 1171-1184, 2009.
    [62] J. F. Liu, D. Muñoz de la Peña, P. D. Christofides. Distributed model predictive control of nonlinear systems subject to asynchronous and delayed measurements. Automatica, vol. 46, no. 1, pp. 52-61, 2010.
    [63] M. Heidarinejad, J. F. Liu, D. Muñoz de la Peña, J. F. Davis, P. D. Christofides. Multirate Lyapunovbased distributed model predictive control of nonlinear uncertain systems. Journal of Process Control, vol. 21, no. 9, pp. 1231-1242, 2011.
    [64] X. Z. Chen, M. Heidarinejad, J. F. Liu, D. Muñoz de la Peña, P. D. Christofides. Model predictive control of nonlinear singularly perturbed systems: Application to a largescale process network. Journal of Process Control, vol. 21, no. 9, pp. 1296-1305, 2011.
    [65] M. Heidarinejad, J. F. Liu, D. Muñoz de la Peña, J. F. Davis, P. D. Christofides. Handling communication disruptions in distributed model predictive control. Journal of Process Control, vol. 21, no. 1, pp. 173-181, 2011.
    [66] X. Z. Chen, M. Heidarinejad, J. F. Liu, P. D. Christofides. Distributed economic MPC: Application to a nonlinear chemical process network. Journal of Process Control, vol. 22, no. 4, pp. 689-699, 2012.
    [67] R. M. Hermans, A. Joki´c, M. Lazar, A. Alessio, P. P. J. van den Bosch, I. A. Hiskens, A. Bemporad. Assessment of non-centralised model predictive control techniques for electrical power networks. International Journal of Control, vol. 85, no. 8, pp. 1162-1177, 2012.
    [68] E. Camponogara, H. F. Scherer. Distributed optimization for model predictive control of linear dynamic networks with Controlinput and output constraints. IEEE Transactions on Automation Science and Engineering, vol. 8, no. 1, pp. 233-242, 2011.
    [69] I. Necoara, V. Nedelcu, I. Dumitrache. Parallel and distributed optimization methods for estimation and control in networks. Journal of Process Control, vol. 21, no. 5, pp. 756-766, 2011.
    [70] Y. Zheng, S. Y. Li, N. Li. Distributed model predictive control over network information exchange for large-scale systems. Control Engineering Practice, vol. 19, no. 7, pp. 757-769, 2011.
    [71] X. Cai, M. J. Tippett, L. Xie, J. Bao. Fast distributed MPC based on active set method. Computers and Chemical Engineering, vol. 71, pp. 158-170, 2014.
    [72] P. Giselsson, M. D. Doan, T. Keviczky, B. de Schutter, A. Rantzer. Accelerated gradient methods and dual decomposition in distributed model predictive control. Automatica, vol. 49, no. 3, pp. 829-833, 2013.
    [73] I. Necoara, D. Doan, J. A. K. Suykens. Application of the proximal center decomposition method to distributed model predictive control. In Proceedings of the 47th IEEE conference on Decision and Control, IEEE, Cancun, Mexico, pp. 2900-2905, 2008.
    [74] S. D. Cairano, M. Brand, S. A. Bortoff. Projection-free parallel quadratic programming for linear model predictive control. International Journal of Control, vol. 86, no. 8, pp. 1367-1385, 2013.
    [75] A. Bemporad, M. Morari, V. Dua, E. N. Pistikopoulos. The explicit linear quadratic regulator for constrained systems. Automatica, vol. 38, no. 1, pp. 3-20, 2002.
    [76] T. A. Johansson, I. Petersen, O. Slupphaug. Explicit Suboptimal linear quadratic regulation with state and input constraints. Automatica, vol. 38, no. 7, pp. 1099-1111, 2002.
    [77] J. C. Willems. Dissipative dynamical systems, Part I: General theory. Archive for Rational Mechanics and Analysis, vol. 45, no. 5, pp. 321-351, 1972.
    [78] J. C. Willems. Dissipative dynamical systems, Part II: Linear systems with quadratic supply rates. Archive for Rational Mechanics and Analysis, vol. 45, no. 5, pp. 352-393, 1972.
    [79] D. J. Hill, P. Moylan. The stability of nonlinear dissipative systems. IEEE Transactions on Automatic Control, vol. 21, no. 5, pp. 708-711, 1976.
    [80] D. J. Hill, P. J. Moylan. Stability results for nonlinear feedback systems. Automatica, vol. 13, no. 4, pp. 377-382, 1977.
    [81] P. J. Moylan, D. J. Hill. Stability criteria for large-scale systems. IEEE Transactions on Automatic Control, vol. 23, no. 2 pp. 143-149, 1978.
    [82] A. A. Alonso, B. E. Ydstie. Process systems, passivity and the second law of thermodynamics. Computers and Chemical Engineering, vol. 20, no. S2, pp. S1119-S1124, 1996.
    [83] A. A. Alonso, B. E. Ydstie. Stabilization of distributed systems using irreversible thermodynamics. Automatica, vol. 37, no. 11, pp. 1739-1755, 2001.
    [84] K. M. Hangos, A. A. Alonso, J. D. Perkins, B. E. Ydstie. Thermodynamic approach to the structural stability of process plants. American Institute of Chemical Engineers Journal, vol. 45, no. 4, pp. 802-816, 1999.
    [85] A. A. Alonso, C. V. Fernandez, J. R. Banga. Dissipative systems: From physics to robust 2nonlinear control. International Journal of Robust and Nonlinear Control, vol. 14, no. 2, pp. 157-179, 2004.
    [86] M. Baldea, N. H. El-Farra, B. E. Ydstie. Dynamics and control of chemical process networks: Integrating physics, communication and computation. Computers and Chemical Engineering, vol. 51, pp. 42-54, 2013.
    [87] B. E. Ydstie, A. A. Alonso. Process systems and passivity via the Clausius-Planck inequality. Systems & Control Letters, vol. 30, no. 5, pp. 253-264, 1997.
    [88] K. R. Jillson, B. E. Ydstie. Process networks with decentralized inventory and flow control. Journal of Process Control, vol. 17, no. 5, pp. 399-413, 2007.
    [89] R. Setiawan, J. Bao. Analysis of interaction effects on plantwide operability. Industrial & Engineering Chemistry Research, vol. 50, no. 14, pp. 8585-8602, 2011.
    [90] R. Setiawan, J. Bao. Plantwide operability assessment for nonlinear processes using a microscopic level network analysis. Chemical Engineering Research and Design, vol. 90, no. 1, pp. 119-128, 2012.
    [91] O. J. Rojas, R. Setiawan, J. Bao, P. L. Lee. Dynamic operability analysis of nonlinear process networks based on dissipativity. AIChE Journal, vol. 55, no. 4, pp. 963-982, 2009.
    [92] G. Scorletti, G. Duc. A convex approach to decentralized H∞ control. In Proceedings of the American Control Conference, IEEE, Albuquerque, New Mexico, pp. 2390-2394, 1997.
    [93] G. Scorletti, G. Duc. An LMI approach to decentralized H∞ control. International Journal of Control, vol. 74, no. 3 pp. 211-224, 2001.
    [94] S. C. Xu, J. Bao. Distributed control of plantwide chemical processes. Journal of Process Control, vol. 19, no. 10, pp. 1671-1687, 2009.
    [95] S. C. Xu, J. Bao. Control of chemical processes via output feedback controller networks. Industrial & Engineering Chemistry Research, vol. 49, no. 16, pp. 7421-7445, 2010.
    [96] M. J. Tippett, J. Bao. Control of plant-wide systems using dynamic supply rates. Automatica, vol. 50, no. 1, pp. 44-52, 2014.
    [97] M. J. Tippett, J. Bao. Dissipativity based distributed control synthesis. Journal of Process Control, vol. 23, no. 5, pp. 755-766, 2013.
    [98] C. Langbort, R. S. Chandra, R. D Andrea. Distributed control design for systems interconnected over an arbitrary graph. IEEE Transactions on Automatic Control, vol. 49, no. 9, pp. 1502-1519, 2004.
    [99] C. Schizas, F. J. Evans. A graph theoretic approach to multivariable control system design. Automatica, vol. 17, no. 2, pp. 371-377, 1981.
    [100] M. Vidyasagar. Input-output Analysis of Large-scale Interconnected Systems, Berlin, Grermany: Springer-Verlag, 1981.
    [101] C. Langbort, V. Gupta. Minimal interconnection topology in distributed control design. In Proceedings of the American Control Conference, IEEE, Minnepolis, USA, pp. 845-850, 2006.
    [102] K. M. Hangos, Z. Tuza. Optimal control structure selection for process systems. Computers & Chemical Engineering, vol. 25, no. 11-12, pp. 1521-1536, 2001.
    [103] P. Lin, Y. M. Jia, L. Li. Distributed robust H∞ consensus control in directed networks of agents with time-delay. Systems & Control Letters, vol. 57, no. 8, pp. 643-653, 2008.
    [104] R. Vadigepalli, F. J. Doyle. A distributed state estimation and control algorithm for plantwide processes. IEEE Transactions on Control Systems Technology, vol. 11, no. 1, pp. 119-127, 2003.
    [105] R. Vadigepalli, F. J. Doyle. Structural analysis of largescale systems for distributed state estimation and control applications. Control Engineering Practice, vol. 11, no. 8, pp. 895-905, 2003.
    [106] N. Abdel-Jabbar, C. Kravaris, B. Carnahan. A partially decentralized state observer and its parallel computer implementation. Industrial & Engineering Chemistry Research, vol. 37, no. 7, pp. 2741-2760, 1998.
    [107] D. R. Ding, Z. D.Wang, H. L. Dong, H. S. Shu. Distributed H1 state estimation with stochastic parameters and nonlinearities through sensor networks: The finite horizon case. Automatica, vol. 48, no. 8, pp. 1575-1585, 2012.
    [108] V. Ugrinovskii, E. Fridman. A round-robin type protocol for distributed estimation with H∞ consensus. Systems & Control Letters, vol. 69, pp. 103-110, 2014.
    [109] P. Mill´an, L. Orihuela, C. Vivas, F. R. Rubio. Distributed consensus-based estimation considering network induced delays and dropouts. Automatica, vol. 48, no. 10, pp. 2726-2729, 2012.
    [110] E. L. Haseltine, J. B. Rawlings. Critical evaluation of extended kalman filtering and moving-horizon estimation. Industrial & Engineering Chemistry Research, vol. 44, no. 8, pp. 2451-2460, 2005.
    [111] C. V. Rao, J. B. Rawlings, J. H. Lee. Constrained linear state estimation-a moving horizon approach. Automatica, vol. 37, no. 10, pp. 1619-1628, 2001.
    [112] C. V. Rao, J. B. Rawlings, D. Q. Mayne. Constrained state estimation for nonlinear discrete-time systems: Stability and moving horizon approximations. IEEE Transactions on Automatic Control, vol. 48, no. 2, pp. 246-258, 2003.
    [113] J. Busch, D. Elixmann, P. K¨uhl, C. Gerkens, J. P. Sch¨oder, H. G. Bock, W. Marquardt. State estimation for largescale wastewater treatment plants. Water Research, vol. 47, no. 13, pp. 4774-4787, 2013.
    [114] J. F. Liu. Moving horizon state estimation for nonlinear systems with bounded uncertainties. Chemical Engineering Science, vol. 93, pp. 376-386, 2013.
    [115] J. Zhang, J. F. Liu. Observer-enhanced distributed moving horizon state estimation subject to communication delays. Journal of Process Control, vol. 24, no. 5, pp. 672-686, 2014.
    [116] J. Zhang, J. F. Liu. Two triggered information transmission algorithms for distributed moving horizon state estimation. Systems & Control Letters, vol. 65, pp. 1-12, 2014.
    [117] M. Farina, G. Ferrari-Trecate, R. Scattolini. Distributed moving horizon estimation for linear constrained systems. IEEE Transactions on Automatic Control, vol. 55, no. 11, pp. 2462-2475, 2010.
    [118] M. Farina, G. Ferrari-Trecate, R. Scattolini. Movinghorizon partition-based state estimation of large-scale systems. Automatica, vol. 46, no. 5, pp. 910-918, 2010.
    [119] M. Farina, G. Ferrari-Trecate, C. Romani, R. Scattolini. Moving horizon estimation for distributed nonlinear systems with application to cascade river reaches. Journal of Process Control, vol. 21, no. 5, pp. 767-774, 2011.
    [120] M. Farina, G. Ferrari-Trecate, R. Scattolini. Distributed moving horizon estimation for nonlinear constrained systems. International Journal of Robust and Nonlinear Control, vol. 22, no. 2, pp. 123-143, 2012.
    [121] N. Shah. Process industry supply chains: Advances and challenges. Computers & Chemical Engineering, vol. 29, no. 6, pp. 1225-1235, 2005.
    [122] N. N. Chokshi, D. C. McFarlane. A Distributed Coordination Approach to Reconfigurable Process Control, London: Springer-Verlag, 2008.
    [123] G. E. Keller, P. F. Bryan. Process engineering: Moving in new directions. Chemical Engineering Progress, vol. 96, no. 1, pp. 41-50, 2000.
    [124] T.Wauters, K. Verbeeck, P. Verstraete, G. Vanden Berghe, P. De Causmaecker. Real-world production scheduling for the food industry: An integrated approach. Engineering Applications of Artificial Intelligence, vol. 25, no. 2, pp. 222-228, 2012.
    [125] E. Chac´on, I. Besembel, J. C. Hennet. Coordination and optimization in oil and gas production complexes. Computers in Industry, vol. 53, no. 1, pp. 17-37, 2004.
    [126] S. Salomons, R. E. Hayes, M. Poirier, H. Sapoundjiev. Modelling a reverse flow reactor for the catalytic combustion of fugitive methane emissions. Computers & Chemical Engineering, vol. 28, no. 9, pp. 1599-1610, 2004.
    [127] N. Chokshi, D. C. McFarlane. A distributed architecture for reconfigurable control of continuous process operations. Journal of Intelligent Manufacturing, vol. 19, no. 2, pp. 215-232, 2008.
    [128] P. Christofides, J. Davis, N. H. El-Farra, D. Clark, H. K., K. R. D. Harris, J. N. Gipson. Smart plant operations: Vision, progress and challenges. American Institute of Chemical Engineers Journal, vol. 53, no. 11, pp. 2734-2741, 2007.
    [129] B. Erik Ydstie. New vistas for process control: Integrating physics and communication networks. AIChE Journal, vol. 48, no. 3, pp. 422-426, 2002.
    [130] D. Angeli, R. Amrit, J. B. Rawlings. On average performance and stability of economic model predictive control. IEEE Transactions on Automatic Control, vol. 57, no. 7, pp. 1615-1626, 2012.
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Distributed Control of Chemical Process Networks

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This work was supported by Australian Research Council (ARC) Discovery Project (No.DP130103330)

Abstract: In this paper, we present a review of the current literature on distributed (or partially decentralized) control of chemical process networks. In particular, we focus on recent developments in distributed model predictive control, in the context of the specific challenges faced in the control of chemical process networks. The paper is concluded with some open problems and some possible future research directions in the area.

Michael J. Tippett and Jie Bao. Distributed Control of Chemical Process Networks. International Journal of Automation and Computing, vol. 12, no. 4, pp. 368-381, 2015. doi: 10.1007/s11633-015-0895-9
Citation: Michael J. Tippett and Jie Bao. Distributed Control of Chemical Process Networks. International Journal of Automation and Computing, vol. 12, no. 4, pp. 368-381, 2015. doi: 10.1007/s11633-015-0895-9
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