An Evaluation of a Multi-method Tool for Real-Time Implementation of Two-layer Optimization

Abstract In this work an optimization tool based on Sequential Quadratic Programming (SQP), Levenberg-Marquardt (LM) and Genetic Algorithm (GA) is presented. For the matter of possible alternative computational platforms, it is convenient to have an open toll easily implemented with softwares at low costs. The tool evaluation is carried out in real-time optimization with the concept of two-layer approach. The tool is applied to a threephase catalytic slurry reactor, represented by a deterministic dynamic heterogeneous mathematical model. The kinetic law considers the hydrogenation reaction of o-cresol to obtain 2-methyl-cyclo-hexanol, in the presence of the catalyst Ni/SiO 2 . The advanced controller, which is based on the Dynamic Matrix Control with constraints (QDMC), is used. The present implementation aims to maintain the conversion at the exit of the reactor and to maximize the conversion. The challenge is then to conciliate better results of the optimization and less effort and computational time in the real-time process integration. The results presented showed that LM, SQP (local deterministic methods) and GA (stochastic method) algorithms were able to optimize the process both for the case of maintaining and maximizing o-cresol conversion, when perturbations are introduced into the process. The simulations showed that GA could optimize the process after perturbations were inserted but demanded a CPU time not applicable in real-ime optimizations. LM and SQP, on the other hand, optimized successfully the process, both in terms of achieved conversion and CPU time, presenting potential to be used in realtime applications for the studied three-phase catalytic reactor.