Generalized Benders Decomposition is a procedure to solve certain types of NLP and MINLP problems. The use of this procedure has been recently suggested as a tool for solving process design problems. This paper analyzes the solution of nonconvex problems through different implementations of the Generalized Benders Decomposition. It is demonstrated that in certain cases only local minima may be found, whereas in other cases not even convergence to local optima can be achieved. A criterion to identify whether the converged value is a candidate for being a local minimum is provided. It is also shown that in the presence of a dual gap, a particular implementation of the Generalized Benders Decomposition may provide upper and lower bounds on the global optimum.
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