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Article citations


Knutson, H.K., Holmqvist, A. and Nilsson, B. (2015) Multi-Objective Optimization of Chromatographic Rare Earth Element Separation. Journal of Chromatography A, 1416, 57-63.

has been cited by the following article:

  • TITLE: Robust Multi-Objective Optimization of Chromatographic Rare Earth Element Separation

    AUTHORS: Hans-Kristian Knutson, Anders Holmqvist, Niklas Andersson, Bernt Nilsson

    KEYWORDS: Rare Earth Elements, Chromatography, Multi-Objective Optimization, Robust Optimization

    JOURNAL NAME: Advances in Chemical Engineering and Science, Vol.7 No.4, October 31, 2017

    ABSTRACT: Rare earth elements are strategic commodities in many countries, and an important resource for the growing modern technology industry. As such, there is an increasing interest for development of rare earth element processing, and this work is a part of further development of chromatography as a rare earth element separation process method. Process optimization is pivotal for process development, and it is common that several competing objectives must be regarded. Chromatographic separation processes often consider competing objectives, such as productivity, yield, pool concentration and modifier consumption, which leads to Pareto optimal solutions. Adding robustness to a process is of great importance to account for process disturbances and uncertainties but generally comes with reduced performance of the other process objectives as a trade off. In this study, a model-based robust multi-objective optimization was carried out for batch-wise chromatographic separation of the rare earth elements samarium, europium and gadolinium, which was considered highly un-robust due to the neighbouring peaks proximity to the product pooling horizon. The results from the robust optimization were used to chart the required operation point changes for keeping the amount of failed batches at an acceptable level when a certain level of process disturbance was introduced. The loss of process performance due to the gained robustness was found to be in the range of 10% - 20% reduced productivity when comparing the robust and un-robust Pareto solutions at Pareto points with identical yield. The methodology presented shows how to increase robustness to a highly un-robust system while still keeping multiple objectives at their optima.