Abstract
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.
Highlights
Chemical process simulators or flowsheeting programs have reached a level of maturity that they have become an everyday tool for chemical engineers in industry and academic institutions
While chemical process simulators are widely used in analysis, sizing, or cost estimation, they have traditionally only been used in the final stage of process synthesis and design as a validation tool
Researchers have studied the advantages of using chemical process simulators in the first stage of chemical process synthesis and they have proposed different approaches to deal with the closed modular structure of the commercial process simulators
Summary
Chemical process simulators or flowsheeting programs have reached a level of maturity that they have become an everyday tool for chemical engineers in industry and academic institutions. MINLP problems are in general difficult to solve, and the simulation-based optimization problems considered here involve the following challenges: nonconvex functions, nonsmooth functions, inaccurate derivatives, and convergence issues of the simulator. We develop a framework for dealing with simulator convergence issues to enable the optimization procedure to continue; first we try to automatically converge the simulator and secondly, we derive cutting planes through backtracking and exclude variable combinations resulting in convergence failure Together these techniques allow us to find high-quality solutions to our case studies on optimizing distillation processes.
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