Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICIEA.2009.5138477
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dc.titleNonlinear model predictive control of a multistage evaporator system using recurrent neural networks
dc.contributor.authorAtuonwu, J.C.
dc.contributor.authorCao, Y.
dc.contributor.authorRangaiah, G.P.
dc.contributor.authorTadé, M.O.
dc.date.accessioned2014-10-09T07:07:32Z
dc.date.available2014-10-09T07:07:32Z
dc.date.issued2009
dc.identifier.citationAtuonwu, J.C.,Cao, Y.,Rangaiah, G.P.,Tadé, M.O. (2009). Nonlinear model predictive control of a multistage evaporator system using recurrent neural networks. 2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009 : 1662-1667. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/ICIEA.2009.5138477" target="_blank">https://doi.org/10.1109/ICIEA.2009.5138477</a>
dc.identifier.isbn9781424428007
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/90644
dc.description.abstractThe use of multistage evaporators, motivated by the energy economy from reusing the flashed steam is common in a wide range of process industries. Such evaporators however present several control problems which manifest in the form of strong interactions among the many process variables, significant dead times, tendency to open-loop instability and severe nonlinearities. In this paper, a nonlinear model predictive control (NMPC) scheme utilizing a proportional-integral (PI) controller in its inner loop is developed for a simulated industrial-scale five-stage evaporator using a continuous-time recurrent neural network in state space as its internal model. Input-output data obtained from closed-loop system identification experiments are used in training the network by the Levenberg-Marquardt algorithm with automatic differentiation. A similar approach is used in developing an optimal control law for the plant based on the model predictions. The effectiveness of this scheme is tested by simulating various control problem scenarios involving set-point tracking and disturbance rejection and comparing performance with that of decentralized PI controllers developed earlier. Results show significant improvements in control performance, particularly in terms of settling time. © 2009 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICIEA.2009.5138477
dc.sourceScopus
dc.subjectAutomatic differentiation
dc.subjectMultiple-effect evaporators
dc.subjectNonlinear model predictive control
dc.subjectNonlinear system identification
dc.subjectRecurrent neural networks
dc.typeConference Paper
dc.contributor.departmentCHEMICAL & BIOMOLECULAR ENGINEERING
dc.description.doi10.1109/ICIEA.2009.5138477
dc.description.sourcetitle2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009
dc.description.page1662-1667
dc.identifier.isiutNOT_IN_WOS
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