The design and optimization of reactor networks using the conventional concept of attainable region is based on technological (conversion, selectivity, etc.), rather than, economical criteria (cost, profit, etc.). The solution from the economical point of view when the operating and investment costs cannot be neglected may not be optimal, not even in regard to the structure. In order to circumvent this deficiency, the conventional Concentration Attainable Region (CAR) is transformed into an Economic Region (ER) using economic optimization criteria. A novel concept for ER construction is proposed for multi-D problems. One-parametric NLP or MINLP optimizations with reactor volume as a varying parameter are performed to construct trajectories in the ER. In this way the ER is aided by mathematical programming for 3D or more D problems in order to handle dimensions higher than 2 as degrees of freedom for the economic objective function. ER is attainable (EAR) only when the economic objective function is linear. The more the objective function is discontinuous, discrete, nonlinear and non-convex and the more its cost coefficients vary with time. the less the principles of AR can be applied to ER. However, economically optimal reactor systems always lie at the borders of ERs. Two important insights have been gained during the construction of different time-dependent Elks, which have been used in upgrading the MINLP approach to the synthesis of reactor networks in overall process schemes: (i) A reactor network superstructure has been reestablished based on economical optimization criteria and the principles of ER. (ii) Over a longer period, the performing of a stochastic multi-period MINLP synthesis is recommended, in order to consider the time variability and uncertainties of economic parameters. A special multi-period strategy is proposed to upgrade the efficiency of the MINLP synthesis. (c) 2006 Elsevier Ltd. All rights reserved.