Nonisothermal reactor networks optimization using metaheuristics in a bi-level approach

In the present article, an optimization approach for the synthesis of reactor networks is proposed. A stage-wise superstructure containing a continuous stirred tank reactor (CSTR) and a plug flow reactor (PFR) is used to formulate the mathematical model without the use of differential equations and with a mixed-integer nonlinear programming (MINLP) formulation. To solve the optimization problem a bi-level meta-heuristic approach combining simulated annealing (SA) and particle swarm optimization (PSO) is used. Two case studies involving complex nonisothermal homogeneous reaction systems were used to test the applicability of this technique, and a methodology based on temperature profiles was developed to guide the search for operating temperatures during the optimization. The results achieved improvements in the objective function when compared to the majority of results reported in the literature, which demonstrates that the developed methodology is suitable to perform the reactor network synthesis in homogeneous systems.

Automated summary

In this article, an optimization approach for the synthesis of reactor networks is proposed. A stage-wise superstructure is used to formulate the mathematical model, and a mixed-integer nonlinear programming method is used. A bi-level meta-heuristic approach combining simulated annealing and particle swarm optimization is employed to solve the optimization problem. The results demonstrate the effectiveness of this method in improving the objective function in comparison to previous approaches.