Five Steps to Enable Self-Optimizing Process Purification

Self-optimization is the next evolutionary step for bioprocessing purification systems and, although that step is near, it currently is hindered by missing integrations among its many parts and by loose usage of precise concepts.

At an operational level, self-optimized purification is supported by process analytical technology, hard and soft sensors, digital twins and digital shadows, physics-informed modeling, and real-time optimization capabilities. In that milieu, biopharmaceutical processing is the most advanced. What it lacks, however, is operational comparability linking measurement, state estimation, model updating, decision support, and closed-loop actions for purification platforms and components such as membranes, adsorption and cyclic gas separations, chromatography, and integrated purification trains.

“The limiting factor across domains…is the incomplete integration of measurement design, hidden-state estimation, updating, uncertainty, control authority, governance, and economic justification under realistic drift and scale change,” Vasileios M. Pappas, PhD, post-doctoral senior researcher, University of Thessaly in Greece, explains in a recent review.

To remedy this, Pappas recommends:

1. Prioritizing sensors and sampling strategies that identify the critical hidden states of each purification platform
2. Reporting model-updating, recalibration and invalidation rules explicitly, “rather than treating them as implementation details”
3. Including “drift, delay, sensor failure, feed disturbance, cleaning/regeneration history, and scale transfer [data] as part of the validation process, in addition to nominal operating data
4. “Distinguish[ing] advisory, supervisory, and autonomous authority as well as fallback logic when model confidence is insufficient”
5. Reporting economic, regulatory, cybersecurity, and data-governance constraints alongside predictive accuracy

For biomanufacturers, these steps enable more thorough integration throughout the purification process. They enable “trustworthy self-optimizing purification…by demonstrating that process state, model confidence, and operating authority remain linked under disturbances that matter industrially,” Pappas elaborates. “The central test is whether the system can infer the hidden state early enough, update itself responsibly, and support or execute an operating action that protects purity, recovery, productivity, safety, and robustness.”

The path to self-optimization

Before implementing those suggestions, however, Pappas stresses the importance of “conceptual precision” as a starting point, noting loose usage of the defining terms. “A regressor trained on historical campaigns is not a twin simply because it runs online,” he notes, by way of illustration.

“Conceptional precision matters because the operational value of a digital layer depends on what it can infer, how it stays calibrated, how uncertainty is treated, and whether its output can support a qualified operating decision,” he points out. Using the correct specifications for the digital layers minimizes misunderstandings, thereby increasing the chances that the initial concept and the final design agree.

Otherwise, Pappas notes, the system may revert to monitoring and offline optimization or becomes a digital shadow—a model updated from processing data—rather than the operationally-significant bidirectional model known as a digital twin.

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