Machine learning techniques for monitoring the sludge profile in a secondary settler tank

Machine learning techniques for monitoring the sludge profile in a secondary settler tank

Abstract The aim of this paper is to evaluate and compare the performance of two machine learning methods, Gaussian process regression (GPR) and Gaussian mixture models (GMMs), as two possible methods for monitoring the sludge profile in a secondary settler tank (SST). In GPR, the prediction of the...

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Bibliographic Details
Main Authors: Jesús Zambrano, Oscar Samuelsson, Bengt Carlsson
Format: Article
Language:English
Published: SpringerOpen 2019-07-01
Series:Applied Water Science
Subjects:
Covariance function
Fault detection
Gaussian mixture models
Gaussian process regression
Monitoring
Online Access:http://link.springer.com/article/10.1007/s13201-019-1018-5

Internet

http://link.springer.com/article/10.1007/s13201-019-1018-5

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