Abstract
Distributed deployment of process systems can benefit from modularity and shared components across multiple variants, reducing both manufacturing costs and engineering effort. Process family design formalizes this idea by simultaneously optimizing a family of process variants while determining a shared platform of common components. This results in a large-scale mixed-integer nonlinear program (MINLP) that couples nonlinear process models with discrete platform-allocation decisions. In this work, we solve the process family design MINLP using a progressive hedging (PH)-based decomposition strategy that exploits its block-angular structure. To improve convergence for this nonconvex problem, we introduce dynamic gradient-based penalty updates, a decoupled primal-dual strategy via separate PH runs, and parallel optimization-based bounds tightening of first-stage variables. Computational results on a water desalination case study demonstrate that the proposed approach improves solution quality and reduces computational time compared to baseline PH, while achieving a lower total cost of the process family than the previous discretization-based MILP formulation [1]. These results highlight the effectiveness of tailored decomposition strategies for large-scale process family design problems.