Enhancing Pharmaceutical Batch Processes Monitoring with Predictive LSTM-Based Framework

Daniele Antonucci, Daniele Antonucci, Davide Bonanni, Domenico Palumberi, Luca Consolini, Gianluigi Ferrari

2025

Abstract

Monitoring industrial processes and understanding deviations is critical in ensuring product quality, process efficiency, and early detection of anomalies. Traditional methods for dimensionality reduction and anomaly detection, such as Principal Component Analysis (PCA) or Partial Least Squares (PLS), often struggle to capture the complex and dynamic nature of batch data. In this study, we propose a novel approach that combines an AutoEncoder (AE), based on Long Short-Term Memory (LSTM) layers, with a rolling threshold for anomaly evaluation. Unlike conventional threshold methods that rely on global statistical parameters, the applied threshold leverages rolling median and rolling Median Absolute Deviation (MAD) to adaptively detect deviations, making it more resilient to outliers and distribution shifts. The LSTM-AE demonstrates superior performance in anomaly detection with respect to PCA and more recent model approaches, specifically for the reference dataset, obtained from a GlaxoSmithKline (GSK) production plant. Additionally, an LSTM regression model is employed to forecast future data points, which are then fed into the LSTM-AE to enable a predictive approach. This framework leverages the temporal dependencies captured by LSTM layers and reconstruction efficiency of the AE, facilitating a predictive anomaly detection in real-world applications.

Paper Citation

in Harvard Style

Antonucci D., Bonanni D., Palumberi D., Consolini L. and Ferrari G. (2025). Enhancing Pharmaceutical Batch Processes Monitoring with Predictive LSTM-Based Framework. In Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-770-2, SciTePress, pages 15-24. DOI: 10.5220/0013665500003982

in Bibtex Style

@conference{icinco25,
author={Daniele Antonucci and Davide Bonanni and Domenico Palumberi and Luca Consolini and Gianluigi Ferrari},
title={Enhancing Pharmaceutical Batch Processes Monitoring with Predictive LSTM-Based Framework},
booktitle={Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2025},
pages={15-24},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013665500003982},
isbn={978-989-758-770-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Enhancing Pharmaceutical Batch Processes Monitoring with Predictive LSTM-Based Framework
SN - 978-989-758-770-2
AU - Antonucci D.
AU - Bonanni D.
AU - Palumberi D.
AU - Consolini L.
AU - Ferrari G.
PY - 2025
SP - 15
EP - 24
DO - 10.5220/0013665500003982
PB - SciTePress