Five Steps to Enable Self-Optimizing Process Purification

Self-optimization represents a significant advancement for bioprocessing purification systems, but its full potential remains unrealized due to integration gaps and unclear utilization of advanced technologies. At the operational level, self-optimized purification integrates process analytical technology, sensors, digital twins, digital shadows, physics-informed modeling, and real-time optimization.

However, the industry struggles with linking measurement, state estimation, model updating, decision support, and closed-loop actions for various purification components like membranes, adsorption, chromatography, and gas separations. Crucially, the main obstacle is incomplete integration of measurement design, hidden-state estimation, updating, uncertainty, control authority, governance, and economic justification in response to drift and scale changes.

Vasileios M. Pappas, a senior researcher, identifies these issues as the primary barriers to achieving trustworthy self-optimizing purification. To address these challenges, Pappas proposes five critical steps: prioritize sensors and sampling strategies to identify critical hidden states; explicitly define model-updating, recalibration, and invalidation rules; incorporate factors like drift, delay, sensor failure, disturbances, cleaning history, and scale transfer into validation; distinguish advisory, supervisory, and autonomous authority, including fallback logic; and report economic, regulatory, cybersecurity, and data-governance constraints alongside predictive accuracy.

Implementing these steps can lead to a more integrated purification process, fostering "trustworthy self-optimizing purification" by ensuring process state, model confidence, and operating authority remain aligned under real-world disturbances. The first crucial step is conceptual precision, emphasizing the importance of accurate terminology.

A digital regressor trained on historical data is not sufficient to function as a digital twin without proper conceptual precision. Properly specifying digital layers minimizes misunderstandings, increasing the likelihood that initial concepts translate into final operational designs. Otherwise, systems may fall back to traditional monitoring or become digital shadows, operating as updated models rather than the desired bidirectional digital twins.