Machine learning techniques for monitoring the sludge profile in a secondary settler tank
The aim of this chapter is to evaluate and compare the performance of two machine learning methods, Gaussian Process Regression (GPR) and Gauss-ian Mixture Models (GMM), as two possible methods for monitoring the sludge profile in a secondary settler tank (SST). In GPR the prediction of the response variable is given as a Gaussian probability density function, whereas in the GMM the probability density function is built as a weighted sum of Gaussian distributions. In both approaches, a residual is calculated and a fault detection criterion is implemented via a recursive decision rule. As case study, GMM and GPR were tested using real data from a sensor measuring the suspended solids concentration as a function of the SST level in a water resource recovery facility in Bromma, Sweden. Results suggest that GMM gives a faster response but is also more sensitive than GPR to changes during normal conditions.