Chromatography processes are the downstream purification workhorses for many formulated products industries such as biopharmaceuticals (monoclonal antibodies, vaccines, blood plasma derived products), pharmaceuticals (peptides, oligonucleotides), food (food proteins) and speciality chemicals. Downstream processes are the most expensive sections of the manufacturing process and are often the processing bottleneck. Process development is a significant task due to the large design space to explore, limited material availability and constricted timelines. Inherent variability in feed streams, high purity constraints, and maintenance issues (resin fouling and cleaning in place) pose additional challenges.

Mechanistic models, or science-based digital twins, use process understanding to describe and predict the quantitative behavior of processes for process design and optimisation, risk assessment, design space exploration and scale up/tech transfer. The formulated products industries described above are increasingly using mechanistic models for chromatography to achieve these predictive goals and bring value to their businesses across R&D, engineering, and manufacturing functions.

Examples of common challenges and questions we aim to address through using chromatography digital twins include:

  • I am working on scale up and optimisation. How does modelling speed up process development whilst providing a more fundamental understanding of the behavior of a process?
  • I am assessing early-stage chromatography process feasibility and I am limited by the amount of material available to run experiments. How can I assess this efficiently and effectively?
  • My organization has frequent issues with resin aging such as fouling and ligand leaching. How can I derisk its impact and ensure robustness over the process lifecycle?
  • My organization is interested in periodic counter current (PCC) multi column chromatography for continuous production to increase productivity – how can modelling help to switch from batch to continuous?
  • My organization has no/very little prior experience in mechanistic modelling. How can we harness the benefits of science-based digital twins without prior experience?

In this webinar, we will overview the industrial challenges in chromatography and discuss PSE’s vision on how model-based solutions can support addressing them. We will then focus on several industrial case studies where model-based solutions have provided value to industry. In these case studies we will examine the methodologies used to deliver value, examples of quantification of this value, and the considerations made to select the model appropriate solution to solve the challenges presented.

What this webinar covers

  • An overview of industrial challenges in chromatography and PSE’s vision on how model-based solutions can support addressing them.
  • Case studies showing where model-based solutions for chromatography and the methodologies employed provide value to industry, including:
    • Enabling R&D efficiency in chromatography process design and optimisation
    • De-risking moving from batch-to-continuous processes, technology transfer and scale-up of chromatography processes
  • PSE’s vision on how chromatography model-based solutions can bring value in manufacturing via the deployment of digital twin solutions.

Who should attend?

Those with an interest in chromatographic purification from any industry, including:

  • Non-modellers through to expert modellers in technical roles in R&D, engineering and operations.
  • Decision makers with an interest in learning about how chromatography science based digital twins can bring value to their businesses across R&D, engineering, and manufacturing functions.


Dr Edward Close
Dr Edward Close, Process Systems Enterprise

Dr Edward Close (Edd) is the Practice Director for Bioprocessing based in the Formulated Products business unit at Siemens Process Systems Engineering (PSE). He obtained his undergraduate degree in chemical engineering in 2009 and his EngD in biochemical engineering in 2013, both at University College London. His EngD was in collaboration with Pfizer, and focused on developing mechanistic models of industrial purification processes to assist in process design and operation for the robust production of therapeutic proteins. Edd joined PSE in 2013 and focuses on developing and applying PSE’s solutions for upstream and downstream bioprocessing to deliver value to users across the Formulated Products industries.