Multi-objective optimization of chromatographic rare earth element separation

Knutson, Hans-Kristian; Holmqvist, Anders; Nilsson, Bernt

doi:10.1016/j.chroma.2015.09.010

Cited by 10 publications

(6 citation statements)

References 16 publications

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“…The multi-objecitve optimization problem formulation in this work resembles that of a previous study [18], with the difference of that this work only considers the two competing objectives yield and productivity. This is in order to benchmark the proposed robust optimization method with a previous bi-objective robust optimization method [30].…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

“…The chromatography model in this study has been presented in a previous publication [18], and is reproduced here for the purpose of clarity. The column separation was described through a kinetic dispersive model [34] with a Langmuir mobile phase modulator isotherm [13] …”

Section: Chromatography Modelmentioning

confidence: 99%

“…A MOO method is needed when competing optimization objectives, such as productivity and yield, are considered. The MOO strategy in this work, as presented in [18], provides with an intact optimization objective for all levels of the bi-level optimization and firm objective values when evaluating the multi-objective optimization problem (MOP). However, the nominal solution for a MOP is not necessarily robust, and even small process disturbances may cause process failure.…”

Section: Introductionmentioning

confidence: 99%

“…This study is intended as a contribution to the work of developing chromatography as an REE separation method, and focuses on preparative chromatographic batch separation of the middle REE group, samarium (Sm), europium (Eu) and gadolinium (Gd), where Eu is the target product due to its' higher commercial value. It is a continuation of an experimental optimization study [11], which was followed by a model based multi-objective optimization (MOO) study [18] where a process optimization strategy was presented. The current work complements the previous studies by introducing robust multi-objective optimization.…”

Section: Introductionmentioning

confidence: 99%

“…In this context, an experimentally validated process model from a previous study [18] was used to generate the process system response. The results from the robust multi-objective optimization were used to assess the robustness of the system, chart the process parameter changes when robustness was introduced, and evaluate the expected loss of performance when robustness was applied.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Robust Multi-Objective Optimization of Chromatographic Rare Earth Element Separation

Knutson¹,

Holmqvist²,

Andersson³

et al. 2017

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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.

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Section: Multi-objective Optimizationmentioning

confidence: 99%