Process monitoring and fault detection on a hot-melt extrusion process using in-line Raman spectroscopy and a hybrid soft sensor

Furqan Tahir, Muhammad T. Islam, John Mack, John Robertson, David Lovett

Research output: Contribution to journalArticle

5 Citations (Scopus)
2 Downloads (Pure)

Abstract

We propose a real-time process monitoring and fault detection scheme for a pharmaceutical hot-melt extrusion process producing Paracetamol-Affinisol extrudate. The scheme involves prediction of Paracetamol concentration from two independent sources: a hybrid soft sensor and a Raman-based Partial Least Squares (PLS) calibration model. Both these predictions are used by the developed PCA (Principal Component Analysis) and SPC (Statistical Process Control) monitors to detect process faults and raise alarms. Through real-time extrusion results, it is shown that this two-sensor approach enables the detection of various common process faults which would otherwise remain undetected with a single-sensor monitoring scheme.
Original languageEnglish
Pages (from-to)400-414
Number of pages15
JournalComputers and Chemical Engineering
Volume125
Early online date25 Mar 2019
DOIs
Publication statusPublished - 9 Jun 2019

Keywords

  • process monitoring
  • fault detection
  • hot melt extrusion
  • PCA
  • PLS
  • calibration model
  • hybrid soft sensor
  • Affinisol 15LV
  • HPMC

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