We present a hybrid approach using both mathematical programming methods and attainable region (AR) concepts to extend reactor network synthesis techniques to include model parameter uncertainty. First, a revised mixed-integer nonlinear programming (MINLP) reactor network synthesis model is presented that allows for more general reactor networks to be constructed. A complicated reactor network synthesis problem is served using the revised formulation. Next, we combine AR theory with multiperiod optimization concepts to extend the MINLP model to include model parameter uncertainty. By examining the Karush-Kuhn-Tucker optimality conditions together with AR theory, we show that reactor networks designed under uncertainty, in general do not follow AR properties. Thus, more general reactor types may be needed to solve the reactor network synthesis problem under uncertainty. However, AR theory, can be used to find performance bounds on multiperiod reactor network synthesis problems. These bounds are very useful for screening candidate reactor networks and to initialize the 'MINLP problem. Two example problems are presented to demonstrate the proposed multiperiod approach.