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.

中文翻译：

---

用于化学工艺族设计的混合整数线性规划方法

快速、广泛部署新技术来解决气候变化目标（例如，经济范围内的深度脱碳）的需要为推进模块化设计策略提供了新的机遇。传统的工程方法侧重于每次安装的独特设计，而错过了制造标准化的机会。扩展来自汽车行业的见解，我们优化了通用单元模块设计平台，同时设计了利用该平台的整个工艺变体系列。这减少了工程工作量、部署时间和制造成本。我们提出了一种非线性广义析取规划公式，并通过设计空间的离散化将其转换为高效的混合整数线性规划（MILP）公式。我们利用 IDAES 的成本计算在 Pyomo 中制定优化方案，并展示了来自 PARETO 框架的水处理海水淡化系统的计算性能和解决方案质量以及作为 CCSI2 一部分的 Aspen Plus® 中构建的碳捕获系统。