Adjustable robust superstructure optimization with mixed-integer recourse for the continuous rufinamide manufacturing

To accelerate the applied research pace for the continuous synthesis of high value-added rufinamide aiming at industrial applications, this work proposes a two-stage adjustable robust optimization framework with mixed-integer recourse to identify the optimal one from more than 500 possible alternative continuous synthetic routes. The overall rufinamide manufacturing process can be divided into three major processing steps, namely the halogenation of the precursor, azidation, and cycloaddition. A mixed-integer nonlinear programming (MINLP) model is formulated under multiple uncertainties. Considering the numbering-up of micro-reactors for adjusting the production capacity, the mixed-integer recourse leads to an intractable optimization problem. Hence, a tailored solution strategy based on the nested column-and-constraint generation (C&CG) is established to efficiently solve the resulting adjustable robust counterpart. Compared to the deterministic model, the benefits of implementing adjustable robust optimization (ARO) are fully demonstrated by evaluating the objective among sampled scenarios, where a cost reduction of up to 3% can be achieved.