OPTIMIZATION ACCOUNTING FOR PROPERTY PREDICTION UNCERTAINTY IN POLYMER DESIGN

In this paper the problem of designing a polymer repeat unit with fine -tuned or optimized thermophysical and mechanical properties is addres sed. The values of these properties are estimated using group contribu tion methods based on the number and type of the molecular groups part icipating in the polymer repeat unit. By exploiting the prevailing mat hematical features of the structure-property relations the following t wo research objectives are addressed: (i) How to efficiently locate a ranked list of the best polymer repeat unit architectures with mathema tical certainty; and (ii) how to quantify the effect of imprecision in property estimation in the optimal design or polymers. A blend of mix ed-integer linear optimization, chance-constrained programming, and mu ltilinear regression is utilized to answer these questions. The propos ed methodology is highlighted with a illustrative example. Preliminary results (see also Maranas (1996a,b)) demonstrate that the proposed fr amework identifies the the mathematically best molecular design and qu antifies the profound effect that property prediction uncertainty may have in optimal molecular design.