We propose a novel iterative procedure to generate hybrid models (HMs) within an optimization framework to solve design problems. HMs are based on first principle and surrogate models (SMs) and they may represent potential plant units embedded within a superstructure. We generate initial SMs with simple algebraic regression models and refine them by adding Gaussian Radial Basis Functions in three steps: initial SM refinement, domain exploration, and, after solving the optimal design problem, further domain exploitation, until the convergence criterion is fulfilled. The superstructure optimization problem is formulated with Generalized Disjunctive Programming and solved with the Logic-based Outer Approximation algorithm. We addressed methanol synthesis and propylene plant design problems. Compared to rigorous model-based optimal design, the proposed HMs gave the same configuration, objective function and decision variables with maximum relative differences of 1 and 7 %, respectively. A sensitivity analysis shows that the proposed strategy reduced CPU time by 33 %.

我们提出了一种新颖的迭代程序，以在优化框架内生成混合模型 (HM) 以解决设计问题。HM 基于第一原理和代理模型 (SM)，它们可能代表嵌入上层建筑的潜在工厂单元。我们使用简单的代数回归模型生成初始 SM，并通过在三个步骤中添加高斯径向基函数来改进它们：初始 SM 细化、领域探索，以及在解决优化设计问题后，进一步领域开发，直到满足收敛标准。上层结构优化问题用广义析取规划公式化，并用基于逻辑的外逼近算法求解。我们解决了甲醇合成和丙烯装置设计问题。与严格的基于模型的优化设计相比，提出的 HMs 给出了相同的配置、目标函数和决策变量，最大相对差异分别为 1% 和 7%。敏感性分析表明，所提出的策略将 CPU 时间减少了 33%。