Assessment and Future Directions of Nonlinear Model Predictive ...

376 R.D. Bartusiak• Can transfer applications from one unit to another, or modify applicationsafter facilities expansion projects without much effort. It is a straightforwardmatter to revise equipment-related parameters. Also, we’ve been ableto transfer kinetic parameter values, although clearly caution is advised andone must always plan to refit data.• Deliver the NLMPC applications close to start-up of new facilities.• Precise and rapid detection of instrument faults via insights gained from largeresidual errors in individual model equations.5 Concluding RemarksThis section provides a brief outlook on industrial practice for extending existingmodel predictive control technology to nonlinear systems, a summary ofthe NLMPC work described in the paper, and outlook remarks regarding theevolution of NLMPC.5.1 Extensions of Existing MPC and Real-Time OptimizationTechnologiesVarious techniques have been used or are emerging in industrial practice toextend the applicability of LMPC technologies to nonlinear systems.For more than a decade, small-scale nonlinearities such as flow rate to valveposition effects or composition dependencies in distillation towers have beeneffectively handled with mathematical transforms.Within the past 5 to 10 years, LMPC’s applicability in the face of nonlinearitieshas been extended via gain updating or gain scheduling approaches. Infrequent,event-driven gain update approaches are more common than continual gainscheduling. Gain updating is typically used for feed-flexible plants where there arerelatively few permutations of feed types that affect the process response.Current continuous improvement efforts on the model identification task, e.g.,automated plant test tools [11] [18], may increase the applicability of LMPC further,at least for regulatory control. The state of the art is moving in the directionof human-supervised adaptation. However, it is unlikely that adaptation – supervisedor not – will further enable LMPC technology to solve the servo (gradetransition) problem for nonlinear systems.What industry calls real time optimization (RTO) represents another meansof implementing nonlinear compensation in constrained multivariable control.Specifically, RTO implies the use of a steady state model and a nonlinear optimizationprogram to set output (and possibly input) targets for an underlyinglayer of LMPC’s or other regulators. Introducing RTO into this discussion isrelevant because of its architectural similarity to current ideas for implementingNLMPC that involve separating the target setting function from the regulatorfunction [19] [20].RTO is an old idea – arguably the driving force for computer process controlalmost 50 years ago [21]. The abiding motivation is to achieve market price driven