Anders Holmqvist

Multi-Objective Optimization of Chromatographic Rare Earth Element Separation

The importance of rare earth elements in modern technological industry grows, and as a result the interest for developing separation processes increases. This work is a part of developing chromatography as a rare earth element processing method. Process optimization is an important step in process development, and there are several competing objectives that need to be considered in a chromatographic separation process. Most studies are limited to evaluating the two competing objectives productivity and yield, and studies of scenarios with tri-objective optimizations are scarce. Tri-objective optimizations are much needed when evaluating the chromatographic separation of rare earth elements due to the importance of product pool concentration along with productivity and yield as process objectives. In this work, a multi-objective optimization strategy considering productivity, yield and pool concentration is proposed. This was carried out in the frame of a model based optimization study on a batch chromatography separation of the rare earth elements samarium, europium and gadolinium. The findings from the multi-objective optimization were used to provide with a general strategy for achieving desirable operation points, resulting in a productivity ranging between 0.61 and 0.75 kgEu/mcolumn(3), h(-1) and a pool concentration between 0.52 and 0.79 kgEu/m(3), while maintaining a purity above 99% and never falling below an 80% yield for the main target component europium.

Dynamic Multi-Objective Optimization of Batch Chromatographic Separation Processes

This contribution presents a novel offline dynamic multi-objective optimization framework for high-pressure liquid chromatographic (HPLC) processes in batch elution mode. The framework allows for optimization of general elution trajectories parametrized with piecewise constant control signals. It is based on a simultaneous method where both the control and state variables are fully discretized in the temporal domain, using orthogonal collocations on finite elements, and the state variables are discretized in the spatial domain, using a finite volume weighted essentially non-oscillatory (WENO) scheme. The resulting finite dimensional nonlinear program (NLP) is solved using a primal-dual interior point method and automatic differentiation techniques. The advantages of this open-loop optimal control methodology are highlighted through the solution of a challenging ternary complex mixture separation problem for a hydrophobic interaction chromatography (HIC) system. For a bi-objective optimization of the target component productivity and yield, subject to a purity constraint, the set of Pareto solutions generated with general elution trajectories showed considerable improvement in the productivity objective when compared to the Pareto set obtained using conventional linear elution trajectories.

Discretized multi-level elution trajectory: A proof-of-concept demonstration

Biomolecular and pharmaceutical downstream processing is dominated by chromatographic separation, which is associated with high product quality, low capacity and high costs. The separation can be optimized to minimize the costs while achieving a high purity. This paper presents an experimental validation of a discretized multi-level elution (DiME) trajectory, implemented on commercially available chromatography equipment. The tertiary protein separation of ribonuclease A, cytochrome C and lysozyme was used as a case study. A mechanistic model was calibrated using step and linear gradient experiments. The model was simulated together with the state sensitivities with respect to model parameters, which was used in the Levenberg-Marquardt algorithm to fit the model response to the experimental data. The model was used to solve the dynamic optimization problem of maximizing the yield of cytochrome C given a 95% purity requirement, 1000s processing time and 50 salt concentration levels in the elution trajectory. The model was spatially discretized using finite volumes and temporally discretized using direct collocation. The corresponding non-linear programming problem was solved with IPOPT. Once the optimal salt trajectory was found it was experimentally implemented on an ÄKTA Pure using an OPC interface. The optimal trajectory was analyzed in-line by UV absorbance measurements and off-line by analysis of collected fractions. The results presented in this study show the successful experimental realization of DiME trajectories and how to use model calibration, optimization and control to realize DiME trajectories for any chromatography separation problem.

A Generic PAT Software Interface for On-Line Monitoring and Control of Chromatographic Separation Systems

This contribution presents a novel process analytical technology (PAT) software interface for online monitoring and control of commercial high-pressure liquid chromatography (HPLC) systems. The developed interface is an add-on to chromatography control software and uses industry-standard bidirectional communication protocols to link sensor technologies with the individual HPLC system components in an overall automation framework that facilitates data acquisition, central operation and control of all instruments. The interface is encoded in the Python™ scripting language and supports versatile data transfer to chromatography control software using either OPC (OLE for process control) or COM (component object model) technologies, which are both based on client/server architectures. By these means, the interface utilizes the flexibility of the high-level programming language for formulating optimal control strategies and enables (semantic) interoperability between the chromatography control software and user defined scripts as well as third-party scientific libraries and numerical packages. The advantages and applicability of the developed interface are highlighted through the implementation of a model-based iterative learning control strategy, in order to assure batch-to-batch repeatability, and open-loop optimal controlled elution trajectories on a commercial HPLC separation system. It is, however, noteworthy that the software interface is completely generic and constitutes a novel framework for implementing any PID control schemes as well as sequential optimal experimental design and model predictive control strategies.

Development and Optimization of a Single Column Analog Model for a Multi-Column Counter-Current Solvent Gradient Purification Process

Abstract This contribution presents a modeling and optimization method for multi-column counter-current purification (MCSGP) processes. The model is based on conventional column models and by using the symmetric and cyclic steady state characteristics of the process a single column analog model was developed. The model have been used for dynamic optimization of the MCSGP process. The optimization was based on a simultaneous method where the control and state variables were discretized using a direct and local collocation method on finite elements in the temporal dimension and a finite volume weighted essentially non-oscillatory (WENO) scheme in the spatial dimension. The resulting nonlinear program (NLP) were solved using an interior point method. The case study presented shows that the zero-order hold elution trajectories, developed for single column batch operation, can be extended to semi-continuous multi-column chromatography with product recycling. The optimization results is composed of a set of operating conditions where the product was upgraded to 95% purity with 99% process yield.