Solving a dynamic separation problem using MINLP techniques

Emet, Stefan; Westerlund, Tapio

doi:10.1016/j.apnum.2007.01.023

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Both gradient-based non-linear programming (NLP) solvers (e.g., interior-point algorithms [30], sequential programming [29], or simplex [11]) and derivative-free approaches (e.g., genetic algorithm [31] and Gaussian process regression [32]) were used successfully for optimal process design. Moreover, mixed-integer non-linear programming (MINLP) was applied to general process optimization using extended cutting plane algorithms [33], as well as for structural decision variables using outer approximation [34], and evolutionary algorithms [35]. The examples show that many different approaches exist for optimizing chromatographic processes.…”

Section: Process Optimizationmentioning

confidence: 99%

A Modular Framework for the Modelling and Optimization of Advanced Chromatographic Processes

Schmölder

Kaspereit

2020

Processes

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A framework is introduced for the systematic development of preparative chromatographic processes. It is intended for the optimal design of conventional and advanced concepts that exploit strategies, such as recycling, side streams, bypasses, using single or multiple columns, and combinations thereof. The Python-based platform simplifies the implementation of new processes and design problems by decoupling design tasks into individual modules for modelling, simulation, assertion of cyclic stationarity, product fractionation, and optimization. Interfaces to external libraries provide flexibility regarding the choice of column model, solver, and optimizer. The current implementation, named CADET-Process, uses the software CADET for solving the model equations. The structure of the framework is discussed and its application for optimal design of existing and identification of new chromatographic operating concepts is demonstrated by case studies.

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