Forecasting time-to-cyclic steady state in periodic bioprocesses via a multi-feature k-Nearest Neighbours framework
File(s)
Published version
Author(s)
Algoufily, Yasser
Michalopoulou, Foteini
Papathanasiou, Maria M
Mercangöz, Mehmet
Type
Conference Paper
Abstract
Early and reliable prediction of convergence to cyclic steady state (CSS) is increasingly important in periodic downstream bioprocessing, where switching and cut decisions are tuned for a repeatable cyclic regime. This work addresses time-to-CSS (TCSS) forecasting and CSS-existence classification for multicolumn countercurrent solvent gradient purification (MCSGP) systems under run-to-run feed variability. We propose a Multi-Feature k-Nearest Neighbours (MF-kNN) framework that performs long-horizon one-shot trajectory forecasting from an early run segment. CSS outcomes are inferred by reapplying a peak-based convergence rule to the predicted trajectory, while CSS existence is predicted via neighbour-label voting. The approach uses multivariate, standardised features, run-level splits, and a windowed neighbour search to reduce computation. Hyperparameters are tuned with a CSS-oriented objective function that balances trajectory fidelity, TCSS error, and misclassification penalties. On an in-silico MCSGP dataset (98 runs; ?t = 0.2 s; 6800 steps/run) with varying initial modifier concentration, MF-kNN produces accurate full-run forecasts from early data and enables operationally useful early go/no-go decisions. Across outlets, results support accurate CSS timing inference and high CSS-existence classification accuracy (up to 100% on selected outlets), indicating MF-kNN as a transparent and deployment-ready complement to cycle-to-cycle CSS monitoring and control.
Date Issued
2026-06-12
Date Acceptance
2026-06-12
Citation
Systems and Control Transactions, 2026, 6, pp.2449-2456
ISSN
2818-4734
Publisher
PSE Press
Start Page
2449
End Page
2456
Journal / Book Title
Systems and Control Transactions
Volume
6
Copyright Statement
© 2026 by the authors. Licensed to PSEcommunity.org and PSE Press. This is an open access article under the creative com mons CC-BY-SA licensing terms. Credit must be given to creator and adaptations must be shared under the same terms. See https://creativecommons.org/licenses/by-sa/4.0/
License URL
Identifier
https://psecommunity.org/LAPSE:2026.0509
Source
The 36th European Symposium on Computer Aided Process Engineering
Place of Publication
Sheffield, UK
Publication Status
Published
Start Date
2026-06-21
Finish Date
2026-06-24
Coverage Spatial
Sheffield, UK
Date Publish Online
2026-06-12