Abstract
In this paper, the influence of measurement noise on batch-end quality prediction by Partial Least Squares (PLS) is discussed. Realistic computer-generated data of an industrial process for penicillin production are used to investigate the influence of both input and output noise on model input and model order selection, and online and offline prediction of the final penicillin concentration. Techniques based on PLS show a large potential in assisting human operators in their decisions, especially for batch processes where close monitoring is required to achieve satisfactory product quality. However, many (bio)chemical companies are still reluctant to implement these monitoring techniques since, among other things, little is known about the influence of measurement noise characteristics on their performance. The results of this study indicate that PLS predictions are only slightly worsened by the presence of measurement noise. Moreover, for the considered case study, model predictions are better than offline quality measurements.
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Vanlaer, J., Van den Kerkhof, P., Gins, G., Van Impe, J.F.M. (2012). The Influence of Input and Output Measurement Noise on Batch-End Quality Prediction with Partial Least Squares. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2012. Lecture Notes in Computer Science(), vol 7377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31488-9_11
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DOI: https://doi.org/10.1007/978-3-642-31488-9_11
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