Multiperiod reactor network synthesis

We present a hybrid approach using both mathematical programming methods an d attainable region (AR) concepts to extend reactor network synthesis techn iques to include model parameter uncertainty. First, a revised mixed-intege r nonlinear programming (MINLP) reactor network synthesis model is presente d that allows for more general reactor networks to be constructed. A compli cated reactor network synthesis problem is served using the revised formula tion. 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 s how that reactor networks designed under uncertainty, in general do not fol low 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 syn thesis problems. These bounds are very useful for screening candidate react or networks and to initialize the 'MINLP problem. Two example problems are presented to demonstrate the proposed multiperiod approach. (C) 2000 Elsevi er Science Ltd. All rights reserved.