A reduced hessian strategy for sensitivity analysis of optimal flowsheets

An efficient and rigorous strategy is presented for evaluating the first-order sensitivity of the optimal solution to changes in process parameters or process models. An algorithm that constructs a reduced Hessian in the null space of the equality constraints is used to solve the sensitivity equations; the resulting effort to solve these equations depends only on the space of the decision (independent) variables. Consequently, large computational savings can be realized because the solution procedure eliminates the need for obtaining second partial derivatives with respect to tear (dependent) variables explicitly. The method is applied to several flowsheeting examples in order to determine efficiently the sensitivity of the optimal solution to parametric and physical property model changes.