The use of commercial flowsheeting programs enables straight-forward use of rigorous, but user hidden, mathematical formulations of chemical processes. The optimization of such black-box models is a challenging task due to nonconvexity, absence of accurate derivatives, and simulation convergence failures which can prevent classical optimization procedures from continuing the search. Here, we present an optimization framework based on the extended cutting plane algorithm with additional heuristic techniques and strategies designed to improve its practical performance for solving nonconvex simulation-based MINLP problems. The new algorithmic features include two approaches for dealing with nonconvexities; the first technique expands the search space to restore feasibility of the MILP subproblems, and the second is a restarting technique to avoid premature termination to non-optimal solutions. We also propose two approaches for handle simulation failures, based on no-good cuts and backtracking. The proposed optimization framework is successfully applied to four case studies dealing with the economic optimization of distillation processes.

使用商业流程图程序可以直接使用严格但用户隐藏的化学过程数学公式。这种黑盒模型的优化是一项具有挑战性的任务，因为非凸性、缺乏准确的导数以及模拟收敛失败会阻止经典优化程序继续搜索。在这里，我们提出了一个基于扩展切割平面算法的优化框架，以及额外的启发式技术和策略，旨在提高其解决基于非凸模拟的 MINLP 问题的实际性能。新的算法特征包括两种处理非凸性的方法；第一种技术扩展了搜索空间以恢复 MILP 子问题的可行性，第二个是重新启动技术，以避免过早终止非最佳解决方案。我们还提出了两种处理模拟失败的方法，基于无良切割和回溯。所提出的优化框架成功地应用于处理蒸馏过程经济优化的四个案例研究。