Downstream processing

Process: Weak cation exchange chromatography

Product: Aminoglycoside antibiotic (Neomycin)

Challenge: A seemingly minor raw-material change in ion-exchange driven by supply, cost or sustainability pressures can unexpectedly disrupt separation performance and resin capacity, making deeper insight through smart experimentation and mechanistic modeling essential.

 Achievements: Using a series of well-targeted experiments, a predictive mechanistic model was developed to describe the purification of Neomycin and the separation of its two isomers (B and C). The model accurately captured ion exchange mechanisms and acid–base equilibria, explaining two critical observations: the unexpectedly low resin capacity for isomer B and the strong influence of counterions on separation performance.

Comparing experimental and simulated breakthrough curves (Fig. 1), the model demonstrated excellent predictive capability across different feed compositions and operating conditions. It rationalized why the resin capacity for isomer B was four times lower than expected and predicted the failure of separation when switching from hydroxide to sulfate form (Fig. 2).

Fig. 1: Comparison between breakthrough curve experiments (symbols) and simulations (lines) on the impact of the concentration of Isomer B in the feed.

Fig. 2: Comparison between purification experiments (symbols) and simulations (lines) on the impact of the concentration of NH3 in the feed on the separation efficiency.

By leveraging this model, a wide range of scenarios could be assessed in silico, enabling optimization of operating conditions without extensive laboratory trials. This approach provided significant savings in time and material while ensuring robust process understanding.

Key message: Mechanistic modeling allows prediction of complex ion-exchange behaviors, reducing experimental burden and securing process performance for challenging separations.

Challenge:  Industrial-scale separation of two sugars by multicolumn chromatography at high flow rates and feed concentrations suffers from poor efficiency, leading to excessive water consumption. Optimizing the operation conditions is critical to reduce resource consumption and overall costs.

Achievements: A web-based digital simulator was developed to make process optimization and operator training simple and accessible across the factory. Built on first principles, thermodynamics, kinetics, and fluid mechanics, the tool predicts process behavior and provides actionable insights without costly trials.

Key features:

• Real-time display of the process state in a dynamic flow sheet
• Real-time visualization of observable quantities like extract and raffinate purity and concentration and detailed flow compositions at specific points in the process
• Visualization of non-observable quantities like concentration profiles inside the chromatographic columns
• Automatic calculation of water use and KPIs such as yield and productivity

This easy-to-use platform empowers teams to explore “what-if” scenarios, identify optimal conditions, and improve efficiency. And the best is, it runs directly in your browser

Key message: A web-based simulator transforms multicolumn chromatography sugar separations into a transparent, controllable process, reducing resource consumption and boosting performance.

Challenge:  A cosmetic active ingredient manufacturing process faced stricter specifications for the residual concentration of an odorous co-product. The objective was to meet these new requirements without compromising efficiency.

Methods:

• Identification of suitable technologies for removing the odorous co-product.
• Support for the customer in designing and executing an experimental program.
• Process simulations using mechanistic modeling to predict performance and optimize conditions.

Results:

• Successful design of an ion-exchange process and proof of concept with results exceeding expectations.
• Feasibility study completed for process scale-up.
• Technical and economic evaluation provided to guide implementation.

Key message: Mechanistic modeling combined with targeted experimentation enables efficient removal of challenging impurities and supports robust scale-up.

Sometimes you have to change a raw material for various reasons (lack of availability, economic pressure, find alternative sustainable raw material), but this little change can impact significantly your process.

In the below article, you’ll see how some well-designed experiments and mechanistic modeling enable a tricky change of raw material in ion-exchange.

Changing the counter-anion on the raw material of this cation exchange process was expected to have very little influence on the separation. Yet, major issues were observed, including the loss of resin capacity.

Method: To address this challenge, a very complex system of 2 isomers with 6 pKas each was studied, rationalized, and modeled:

• With a set of smart experiments, the physico-chemical phenomena at stakes were understood and key parameters were measured
• The system was represented with a mechanistic ion-exchange model and simulated in our software Ypso-Ionic®.

Results: A new and efficient operating point was identified thanks to a rational scientific approach.  A fully predictive IEX model was established.

This example illustrates the strength of using rigorous ion-exchange models, as opposed to the often-used classical adsorption models, to describe ion-exchange chromatography.

Further reading: Using mechanistic modeling for understanding antibiotics purification with ion-exchange chromatography