Assessment and Future Directions of Nonlinear Model Predictive ...

NLMPC: A Platform for Optimal Control 377economic optimization of the process on a large scale, e.g. an entire olefins plant.Original motivations as a multivariable regulator have been made obsolete byLMPC. While it may be an old idea, RTO’s impact has increased during the pastdecade as a result of enormous improvements in the software used, and by theaccumulation of practical engineering experience. As described in [22], enterprisewideoptimization, including RTO, continues to be an important goal for industry.In general, RTO is used for continuous or infrequently changed semicontinuousprocesses – not for the polymerization processes discussed in Section4. Where RTO is used, it is most common that it only supplies targets toLMPC’s or other linear regulators that are not compensated for process nonlinearities.Less common is the case where gain updating is used to compensate theLMPC’s, as discussed in the preceding section. Possible with the current stateof the art, but even more rare, is the case where the nonlinear RTO model isused to continually update the gains in the LMPC’s.It is beyond the scope of this paper to comment further on RTO. However, itis important to acknowledge the following three contributions from RTO workthat were significant in enabling ExxonMobil Chemical’s reduction of nonlinearmodel predictive control theory to industrial practice: (1) developments of onlinenonlinear programming technology, (2) hardware and software tools, and (3) thepractical experience we gained doing RTO applications [23].5.2 NLMPC Summary and OutlookNLMPC using fundamental models and online solution of the nonlinear optimizationproblem across the prediction horizon is practical in industrial service.The technology is being used to automate state (product grade) transitions(servo control) and to achieve operating point-independent regulatory control ofprocesses highly nonlinear for LMPC.The size of the applications implemented so far is small by input/output countcompared to current practices for LMPC applications. The scope and size of theNLMPC applications is increasing as the technology matures and the experiencebase grows.AlgorithmThe objective function used in this work combines the minimization of a net operatingcost, output errors against reference trajectories, and cost of incrementalmanipulated variable movement. This objective function is different from LQRparticularly regarding the use of the 1-norm for the output error and the use of areference trajectory over the prediction horizon as a primary means of specifyingclosed loop performance.The solution method used in this work is to simultaneously solve the simulationand optimization problem using an SQP code.On the general question of the NLMPC algorithm, one key technical questionis whether it is necessary to retain the nonlinear relationships across the predictionhorizon, or is linearization at one or more points satisfactory. Even for the