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 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 wastewater treatment plant in Bromma, Sweden. Results suggest that GMM gives a faster response but is also more sensitive than GPR to changes during normal conditions.

Simulation of discharge coefficient of side weirs placed on convergent canals using modern self-adaptive extreme learning machine

“…Recently, artificial intelligence techniques such as the artificial neural network and fuzzy logic have been utilized as powerful tools in modeling, pattern-cognition and solving nonlinear complex problems (Zambrano et al 2019;Gholami et al 2019;Parsaie and Haghiabi 2019). Emiroglu et al (2010) proposed an equation for calculating the discharge coefficient of triangular labyrinth side weirs placed on a rectangular straight channel in subcritical flow conditions using ANFIS.…”