Journal
COMPUTERS & CHEMICAL ENGINEERING
Volume 124, Issue -, Pages 253-269Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compchemeng.2018.12.001
Keywords
GMP; Noise Filtering; Language recognition; Supervised machine learning; Semi-supervised machine learning; Ishikawa fishbone diagram
Funding
- Japan Society for the Promotion of Science [17H04964]
- Nagai Foundation Tokyo
- Grants-in-Aid for Scientific Research [17H04964] Funding Source: KAKEN
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The quality of data plays a crucial role in providing a reliable decision-making process when improving processes and operations under uncertainty. We present a data mining-based algorithm for robustly preprocessing the manufacturing records of biopharmaceutical batch processes. The algorithm can identify the time intervals in which the process is in commercial operation, and can characterize process failures automatically. An approximate string-matching algorithm, a decision tree classifier and a constrained clustering is applied to sequence the raw data, to classify the noise and identify each single batches; finally process failure are characterized. The algorithm was applied to the records of the process named as cleaning-and sterilizing-in-place, which is an essential process in manufacturing environment, in a case study. The algorithm was training on state of the art manual pre-processing outcome and was applied reducing the execution time of the activity down to 11.7% while maintaining high data quality and integrity. (C) 2018 Elsevier Ltd. All rights reserved.
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