On Computing and Pricing of Adjustable Robust Chemical Process Designs

TL;DR

This paper develops an adaptive multi-objective robust optimization method for chemical process design that balances performance and uncertainty, reducing computational effort and quantifying the cost of robustness.

Contribution

It introduces an adaptive scenario selection scheme and a novel approach to quantify the robustness cost in chemical process design.

Findings

Efficient scenario identification reduces computational burden.

The method effectively balances robustness and performance.

Case study demonstrates practical applicability.

Abstract

Model-based process simulation can be used to derive designs and operating conditions of chemical processes that optimally balance multiple objectives, such as quality, costs, or environmental impacts. This work focuses on identifying designs that hedge against uncertainties in model parameters to ensure feasibility, taking the possibility to adjust operating conditions into account. An adaptive scheme is proposed to pinpoint the relevant scenarios in a discretized uncertainty space; these scenarios are then fed into a multi-objective adjustable robust optimization framework reducing the computational burden compared to the consideration of all potential scenarios. Furthermore, we propose a method to quantify the cost or price of robustness, i.e., the compromise which has to be made in comparison to the nominal design case in order to hedge against uncertainty. The conceptual findings are illustrated with an industrially relevant case study.