Inclusion of information costs in process design optimization under uncertainty

Recent developments in process design have focused on establishing optimiza tion-based approaches to support decision-making under uncertainty, but few efforts have been made to study and consider how information regarding thi s uncertainty affects optimal decisions. In this paper we develop an optima l design framework that, besides integrating process profitability, robustn ess and quality issues, allows one to decide how much it is worth to spend in research and experimentation for selectively reducing parameter uncertai nties and guiding R&D activities. The design problem is thus formulated as a stochastic optimization problem, whose objective function includes an inf ormation cost term, leading to the identification of optimal parameter unce rtainty levels one should end up with, as well as the corresponding amounts to be spent in R&D. A case study comprising a reactor and heat exchanger s ystem is introduced and provides an illustrative application for the sugges ted methodology. (C) 2000 Elsevier Science Ltd. All rights reserved.