Welz, C.Srinivasan, B.Bonvin, D.2004-11-262004-11-26200810.1016/j.jprocont.2007.10.005https://infoscience.epfl.ch/handle/20.500.14299/177077WOS:00025510710001312495The context of this paper is the use of process measurements to optimize batch processes in the presence of uncertainty. The optimal solution consists of (i) keeping certain path and terminal constraints active and (ii) driving the sensitivities to zero. In particular, the problem of meeting the active terminal constraints in each run is considered here, which is important when these constraints have a larger bearing on the cost than the sensitivities. A two- time-scale methodology is proposed, whereby the task of meeting the active terminal constraints is addressed on-line using trajectory following, while pushing the sensitivities to zero is implemented on a run-to-run basis. The proposed methodology is illustrated via the simulation of a batch distillation systemDynamic optimizationMeasurement-based optimizationNecessary conditions of optimalityIterative learning controlRun-to-run controlBatch processesBatch distillationtext::journal::journal article::research article