(186h) Alternative Two-Layer Optimization Approach of a Three Phase Catalytic Slurry Reactor by Evolutionary Optimization with Genetic Algorithms | AIChE

(186h) Alternative Two-Layer Optimization Approach of a Three Phase Catalytic Slurry Reactor by Evolutionary Optimization with Genetic Algorithms


Melo, D. N. C. - Presenter, State University of Campinas (Unicamp)
Vasco de Toledo, E. C. - Presenter, State University of Campinas (Unicamp)
Santos, M. M. - Presenter, State University of Campinas (Unicamp)
Mariano, A. P. - Presenter, State University of Campinas (Unicamp)
Maciel Filho, R. - Presenter, University of Campinas, UNICAMP

Nowadays there is a significant incentive to develop optimization strategies, especially those related to real time process integration. In fact, large scale process, in which high production rates are usual, even very few improvements in process performance may bring competitive advantage among production units. The present work introduces an investigation study of the optimization of a three-phase catalytic slurry reactor with phase changes to determine the optimal operational conditions and, in a second stage, advanced control algorithms are evaluated. The idea is to define the optimal operational range or the set-points in the optimization layer and then to use them in the advanced control layer. A dynamic heterogeneous mathematical model formulation was used, which basically consists on mass and energy balance equations for the reactants as well as for the catalyst particles involving phase change of both reactants and coolant (Vasco de Toledo et al., 2001 and Mariano et al. 2005). The kinetic law considers the hydrogenation reaction of o-cresol to obtain the 2-methil-cyclo-hexanol, in the presence of the catalyst Ni/SiO2. The process integration is carried out with the two-layer approach. In this approach, the control is set in a hierarchical structure, where an optimization layer calculates the set-points to the advanced controller, which is based on the Dynamic Matrix Control with constraints (QDMC) procedure. The optimization layer is composed of an objective function and a model for the process. In this work was used a genetic algorithm (GA) that solves a nonlinear quadratic problem subject to bounds on the variables and constraints. In recent years, stochastic search optimizations algorithms such as evolutionary algorithms (EA) have been developed for solving several optimization problems in Chemical engineering (Deb, 2001, Deb et al., 2002). The present study had as a central point to verify optimization conflicting objectives such as to maximize the profit and to minimize the consumption of energy inside the reactor, for instance, when disturbances occur in the feed of the reactor, leading to a new set-point to the controller. The decision variables (or manipulated) used for the optimization of the reactor must be selected from among the operating conditions. These also constitute the control variables. The selection of the variables it was made by design of experiments (Hasan et al. 2005), evaluating the effect of each of these variables on the objective functions and finally, the decisions variables were chosen and used on the optimization. The objective of optimization of any process can usually be varied in a narrow range called the ?operability range? because of limitations and stability of the process operation. The challenge is then to conciliate better results of the optimization and less effort computation in the real time process integration. Thus, the first objective was to maximize the profit at the exit of the reactor. Another objective to be achieved in this work is energy cost minimization or to minimize the consuming of the energy at the exit of the reactor. It is presented in this paper the real time process integration involving the optimization and control of the process as may be observed in Figure 1 (Melo et al.; 2005), in this case, with the two-layer approach. This is a hierarchical control structure where an optimization layer calculates the set-points to the advanced controller, which is based on the Dynamic Matrix Control (DMC) procedure. Numerical results show that the proposed approach is well suited for the large scale problem as for the case considered in this work.

Figure 1 ? Two-layer Approach.

The proposed two-layer approach for real time process integration, based on the genetic algorithm method as the optimization algorithm showed to be very efficient to control the reactor even when significant changes on the operational conditions take place.

References Deb,K., (2001). Multiobjetive Optimization Using Evolutionary Algorithms. Wiley, Chichester, UK).

Deb,K., Pratap, A., Agarwal, S. and Meyarivan, T., (2002). Fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolutionary Comp., 6, 182-197.

Hasan, S. D. M.; Melo, D. N. C.; Maciel Filho, R.(2005) Simulation and Response Surface Analysis for the Optimization of a Three-Phase Catalytic Slurry Reactor. Chemical Engineering and Processing, Island, v. 44, n. 3, p. 335-343.

Mariano, A. P., Vasco de Toledo, E.C., Silva, J.M.F., Wolf-Maciel, M. R., Maciel Filho, R. , (2005). Development of Software for Simulation Analysis of the Phenomenon of Phase Change of Three-Phase Catalytic Slurry Reactor. Computers and Chemical Engineering , 29, 6, pp.1369-1378.

Melo, D. N. C.; Santos, M. M. Vasco de Toledo, E.C.; Hasan, S. D. M. ; Wolf- Maciel, M. R.; Maciel Filho, R. (2005). Off-line Optimization and Control for Real Time Integration of a Three-Phase Hydrogenation Catalytic Reactor. Computers and Chemical Engineering, v. 29, 11-12, pp. 2485-2493.

Vasco de Toledo, E.C., Santana, P.L., Wolf Maciel, M.R., Maciel Filho, R. Dynamic modeling of a three-phase catalytic slurry reactor. (2001). Chemical Engineering Science, 56, 21-22, p. 6055-6061.