Optimal Process Design with Model Parameter Uncertainty and Process Variability

Optimal design under unknown information is a key task in process systems engineering. This study considers formulations that incorporate two types of unknown input parameters, uncertain model parameters, and ®ariable process parameters. In the former case, a process must be designed that is feasible o®er the entire domain of uncertain parameters, while in the latter case, control ®ariables can be adjusted during process operation to compensate for ®ariable process parameters. To address this problem we extend the two-stage formulation for design under uncertainty and deri®e new formulations for the multiperiod and feasibility problems. Moreo®er, to simplify the feasibility problem in the two-stage algorithm, we also introduce a KS constraint aggregation function and deri®e a single, smooth nonlinear program that approximates the feasibility problem. Three case studies are presented to demonstrate the proposed approach.