An introduction to the concept of transitional analysis.
Transitional Analysis (TA) is a non-intrusive, in-process method for evaluating packed-column performance. Instead of injecting a separate tracer, TA uses the natural step changes (transitions) that already occur during routine operation—most commonly buffer switches that alter conductivity or pH. By analyzing the shape of each transition as though it were a chromatographic peak, TA turns ordinary process data into real-time diagnostics of column health..
Traditionally, column‐resin integrity is verified with periodic offline tests that capture only snapshots of performance. A small tracer “salt-plug” pulse is injected to calculate plate count (HETP) and asymmetry, confirming packing uniformity but requiring production downtime and extra buffer. Typically these activities are performed immediately following column packing, and may only be repeated after a significant number of batches have been processed. This approach is limited by the need for manual intervention, and it does not provide continuous monitoring of column performance.
Transitional Analysis (TA) treats the natural buffer step-changes that occur during routine operation (e.g., switching from wash to strip) as built-in tracer experiments. The outlet signal (conductivity, pH, UV) forms an S-shaped curve; analyzing that curve—or its derivative—yields a peak-like residence-time distribution without injecting any extra sample. Transitional Analysis leverages the existing process data to provide real-time diagnostics of column health, allowing for continuous monitoring without interrupting production.
Larson et al. (2003) demonstrated that comprehensive column-integrity diagnostics can be extracted directly from routine in-process sensor traces, eliminating the need for dedicated tracer injections. By treating the conductivity and pH step-change profiles generated during buffer switches as surrogate peaks, the authors calculated temporal moments (mean residence time, variance, skewness), mid-slope gradients, and fixed-threshold widths to quantify efficiency and asymmetry under actual production conditions. Aggregating these descriptors into a multivariate “integrity score” and visualising the data in principal-component space, they showed that incipient packing defects, such as channel formation or bed settling, manifested as systematic drifts well in advance of deviations detected by conventional salt-plug HETP tests.
Direct Transition Analysis (DTA), first formalized by Cui et al. (2018), streamlines the moment-based monitoring concept by extracting efficiency and symmetry metrics directly from the raw buffer-transition curve, sidestepping the noise-amplifying derivative step. For each S-shaped conductivity or pH trace, DTA identifies fixed fractional thresholds and computes metrics that quantify column performance, mimicking the asymmetry factor and HETP calculations used in traditional salt-plug tests.
