Near-Infrared Spectroscopic Monitoring of a Series of Industrial Batch Processes Using a Bilinear Grey Model
Abstract:A good process understanding is the foundation for process optimization, process monitoring, end-point detection, and estimation of the end-product quality. Performing good process measurements and the construction of process models will contribute to a better process understanding. To improve the process knowledge it is common to build process models. These models are often based on first principles such as kinetic rates or mass balances. These types of models are also known as hard or white models. White models are characterized by being generally applicable but often having only a reasonable fit to real process data. Other commonly used types of models are empirical or black-box models such as regression and neural nets. Black-box models are characterized by having a good data fit but they lack a chemically meaningful model interpretation. Alternative models are grey models, which are combinations of white models and black models. The aim of a grey model is to combine the advantages of both black-box models and white models. In a qualitative case study of monitoring industrial batches using near-infrared (NIR) spectroscopy, it is shown that grey models are a good tool for detecting batch-to-batch variations and an excellent tool for process diagnosis compared to common spectroscopic monitoring tools.
Document Type: Research Article
Publication date: August 1, 2003
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- The Society publishes the internationally recognized, peer reviewed journal, Applied Spectroscopy, which is available both in print and online. Subscriptions are included with membership or can be purchased by institutional or corporate organizations. Abstracts may be viewed free of charge. Previously published as Bulletin (Society for Applied Spectroscopy)
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