Assessment and Future Directions of Nonlinear Model Predictive ...

Experiences with Nonlinear MPC in PolymerManufacturingKelvin Naidoo, John Guiver, Paul Turner, Mike Keenan, and Michael HarmseAspen Technology Inc., 2500 City West Blvd, Suite 1600, Houston,Texas 77042, USAjohn.guiver@aspentech.comSummary. This paper discusses the implementation of nonlinear model predictivecontrol on continuous industrial polymer manufacturing processes. Two examples ofsuch processes serve to highlight many of the practical issues faced and the technologicalsolutions that have been adopted. An outline is given of the various phases of deployingsuch a solution, and this serves as a framework for describing the relevant modelingchoices, controller structures, controller tuning, and other practical issues1 IntroductionStarting with a pilot implementation in 2001, Aspen Technology has gained alarge amount of experience in the field in implementing fully non-linear MPCon several different types of continuous polymer manufacturing processes. Thereare many benefits obtained by putting MPC on these industrial units, but oneof the main goals is to minimize production of off-spec material both in steadystate operation and when transitioning from one product grade to another. Priorto 2001, implementations using empirical models were typically done with someform of gain adaptation or gain scheduling, which, though suitable for steadystate operation, is sub-optimal for transitions. This is due to the fact that processgains often change by an order of magnitude or more over a relatively shortperiod of time, and the non-linearities involved interact in a multivariate mannerand cannot be removed by univariate transforms. Even if the gains are scheduledin a non-linear manner across the transition horizon [5], this does not take intoaccount these interactions, and no optimized path can be calculated. A breakthroughcame with the development of Bounded Derivative Network technology[6], which allowed the building of empirical, fast-executing, control-relevant modelsthat could be embedded directly in a nonlinear control law, removing the needfor gain scheduling completely.There are many practical matters that impact the success of a control projectincluding technology, process understanding, best-practice methodology, the useof reliable software with suitable functionality, and developing a deep understandingof practical operational requirements. The purpose of this paper is totouch on many of these issues and share best practice concepts around howR. Findeisen et al. (Eds.): Assessment and Future Directions, LNCIS 358, pp. 383–398, 2007.springerlink.com c○ Springer-Verlag Berlin Heidelberg 2007