A mixed integer linear programming approach for the design of chemical process families

Georgia Stinchfield, Joshua C. Morgan, Sakshi Naik, Lorenz T. Biegler, John C. Eslick, Clas Jacobson, David C. Miller, John D. Siirola, Miguel Zamarripa, Chen Zhang, Qi Zhang, Carl D. Laird

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


The need for rapid and widespread deployment of new technologies to address climate change goals (e.g., deep, economy-wide decarbonization) presents new opportunities for advancing modular design strategies. Conventional engineering approaches focus on unique designs for each installation, while missing opportunities for manufacturing standardization. Extending insights from the automotive industry, we optimize a platform of common unit module designs while simultaneously designing an entire family of process variants that make use of that platform. This reduces engineering effort, deployment timelines, and manufacturing costs. We propose a nonlinear generalized disjunctive programming formulation and convert this to an efficient mixed-integer linear programming (MILP) formulation through discretization of the design space. We formulate our optimization in Pyomo with costing from IDAES, and we demonstrate the computational performance and solution quality on a water treatment desalination system from the PARETO framework and a carbon capture system built in Aspen Plus® as part of CCSI2.

Original languageEnglish (US)
Article number108620
JournalComputers and Chemical Engineering
StatePublished - Apr 2024

Bibliographical note

Publisher Copyright:
© 2024


  • Carbon capture
  • Manufacturing
  • Mixed-integer linear programming
  • Optimization
  • Process Family Design


Dive into the research topics of 'A mixed integer linear programming approach for the design of chemical process families'. Together they form a unique fingerprint.

Cite this