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Toward Fast Dynamic Optimization: An Indirect Algorithm That Uses Parsimonious Input Parameterization
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DOI：10.1021/acs.iecr.8b02109.png)
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Abstract
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Dynamic optimization plays an important role toward improving the operation of chemical systems, such as batch and semibatch processes. The preferred strategy to solve constrained nonlinear dynamic optimization problems is to use a so-called direct approach. Nevertheless, based on the problem at hand and the solution algorithm used, direct approaches may lead to large computational times. Indirect approaches based on Pontryagin's Minimum Principle (PMP) represent an efficient alternative for the optimization of batch and semibatch processes. This paper details the combination of an indirect solution scheme together with a parsimonious input parametrization. The idea is to parametrize the sensitivity-seeking inputs in a parsimonious way so as to decrease the computational load of constrained nonlinear dynamic optimization problems. In addition, this article discusses structural differences between direct and indirect approaches. The proposed method is tested on both a batch binary distillation column with terminal purity constraints and a two-phase semibatch hydroformylation reactor with a complex path constraint. The performance of the proposed indirect parsimonious solution scheme is compared with those of a fully parametrized PMP-based method and a direct simultaneous method. It is observed that the combination of the indirect approach with parsimonious input parametrization can lead to significant reduction in computational time.
Keywords:
MODEL-PREDICTIVE CONTROL
PONTRYAGINS MINIMUM PRINCIPLE
REAL-TIME OPTIMIZATION
SIMULTANEOUS STRATEGIES
SWITCHING STRUCTURE
SHOOTING METHODS
IMPLEMENTATION
VALIDATION
REACTORS
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