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Driving a process to optimal conditions under various uncertainties is a key issue for meeting objectives of productivity and quality of batch or fed-batch product. To overcome a limitation of two-step approaches unable to cope with nonparametric or large uncertainty, several gradient based iterative optimization methods have been proposed. Among these, latent variable model based approaches have...
In order to improve the performance of nonlinear model predictive control (NMPC) in the presence of disturbances or model uncertainties, an approximate dynamic programming (ADP) control scheme is proposed. Namely, the Bellman's optimality principle is employed to determine the input based on the approximate value function constructed from the historical operation data. In addition, the support vector...
Computational methods for designing an optimal catalyst have recently been gaining more popularity in the fields of catalysis and reaction engineering of energy systems. However, in general, the problem in these approaches is that uncertainties present in process models should be handled correctly to achieve a robust design. To find the optimal design under these uncertainties, a stochastic optimization...
This paper presents a novel strategy for speeding-up the classical Benders decomposition for large-scale mixed integer linear programming problems. This method is particularly useful for the cases where the optimality cut is difficult to obtain. The distances between the selected feasible points and feasibility cutting planes, as a metric, determine the tighter constraint, thus improving the convergence...
A new methodology is presented in this paper which incorporates the marginalized likelihood ratio (MLR) test for online fault detection and isolation. The proposed methodology reduces the number of optimization problems required for isolating the fault by means of a simple integration scheme. Moreover, the dependency on the accuracy of the statistical fault detection and confirmation tests is relaxed...
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