Stochastic Back-Off Approach for Integration of Design and Control Under Uncertainty

The aim of this study is to present a stochastic back-off methodology for integration of design and control using probabilistic-based descriptions in the uncertainty. A challenging task in simultaneous design and control is the specification of process designs that can accommodate stochastic descriptions of uncertainty parameters and disturbances. The key idea is to represent the confidence interval of process constraints using power series expansion (PSE)-based functions. The proposed back-off approach has the flexibility to assign different confidence levels to each constraint depending on their relevance or significance. Monte Carlo (MC) sampling is employed to design the PSE-based functions. A wastewater treatment plant was used to test the proposed approach. The results have shown that this method has the potential to determine an optimal process design that can maintain the dynamic operability of the system in the presence of stochastic realizations in the uncertainties and disturbances.