Christian Kunde

A Reformulation Strategy for Deterministic Global Optimization of Ideal Multi-component Distillation Processes

Abstract This paper deals with deterministic global optimization of ideal multi-component distillation. A model reformulation based on a linear transformation of concentration variables is discussed, and the impact of the reformulation on the computational effort for solving the underlying mixed-integer non-linear optimization problems is demonstrated by several numerical examples.

Towards global optimization of combined distillation-crystallization processes for the separation of closely boiling mixtures

This paper deals with global optimization of hybrid distillation/melt crystallization processes. For this purpose, improved algorithms are presented taking the problem specific structure into account. We use recent results to derive improved convex relaxations for terms occurring in the material balance equations and propose a constraint propagation strategy that enables us to exclude several process configurations very fast. Computations are presented demonstrating the usefulness of our methods. It is shown that the speed of global optimization can be improved by orders of magnitude.

Deterministic global optimization of multistage melt crystallization processes in hydroformylation

Abstract This paper deals with the deterministic global optimization of multistage melt crystallization processes with application to mixtures of n-/iso-aldehydes resulting from hydroformylation. For this purpose we adopt a process model including experimentally determined parameters from the literature. We extend that model from commonly used countercurrent cascades to arbitrary process configurations and reformulate it for better computational performance. The resulting model is a MINLP due to operational and structural degrees of freedom. Global optima for the process costs with respect to an economical cost function are determined using BARON/GAMS. The results show that there are new process configurations outperforming the conventional countercurrent cascade, depending on parameter values.

Global optimization of distillation columns using surrogate models

Surrogate-based optimization of distillation columns using an iterative Kriging approach is investigated. Focus is on deterministic global optimization to avoid suboptimal local minima. The determination of optimal setups and operating conditions for ideal and non-ideal distillation columns, leading to mixed-integer nonlinear programming problems, serve as case studies. It is found that the optimization using the adapted Kriging approach yields similar results compared to the direct global optimization of the original problem in the ideal case, while it leads to a huge improvement compared to a multistart local optimization approach in the non-ideal case.

Efficient global optimization of a novel hydroformylation process

Abstract A novel process for the hydroformylation of long-chain alkenes in a thermomorphic solvent system is investigated. Mathematical optimization is applied to determine optimal operating conditions. Different model modifications are done and their effects on the computational effort are evaluated step by step. It is shown that with these modifications the problem can be solved to global optimality within a few minutes using standard software for deterministic global MINLP optimization.