Abstract
The high non-linearity, serious time-variability and uncertainty result in a number of very challenging problems in working on the monitoring and control of biological processes. Many important variables are difficult to measure during monitoring and control. Software sensors can give estimation to unmeasured state variables according to the measured information provided by online measuring instruments available in the system. This offers an alternative feasible program for online measurement. A hybrid soft measurement model that combines principal component analysis with artificial neural networks is applied to monitor the sequencing batch reactor (SBR) process. Simulation results show that the most unmeasured variables can be predicted and the method can capture the main trend of the data.
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© 2007 Springer Berlin Heidelberg
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Fan, L., Xu, Y. (2007). A PCA-Combined Neural Network Software Sensor for SBR Processes. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_123
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DOI: https://doi.org/10.1007/978-3-540-72393-6_123
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72392-9
Online ISBN: 978-3-540-72393-6
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