@article{discovery10064600,
            note = {Copyright {\copyright} 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).},
          volume = {6},
          number = {11},
           month = {November},
         journal = {Processes},
       publisher = {MDPI},
           title = {Fault Detection in Wastewater Treatment Systems Using Multiparametric Programming},
            year = {2018},
        keywords = {wastewater treatment; fault detection; parameter estimation; multiparametric programming},
            issn = {2227-9717},
          author = {Mid, EC and Dua, V},
        abstract = {In this work, a methodology for fault detection in wastewater treatment systems, based on
parameter estimation, using multiparametric programming is presented. The main idea is to detect
faults by estimating model parameters, and monitoring the changes in residuals of model parameters.
In the proposed methodology, a nonlinear dynamic model of wastewater treatment was discretized
to algebraic equations using Euler's method. A parameter estimation problem was then formulated
and transformed into a square system of parametric nonlinear algebraic equations by writing the
optimality conditions. The parametric nonlinear algebraic equations were then solved symbolically to
obtain the concentration of substrate in the inflow, Scin , inhibition coefficient, Ki
, and specific growth
rate, uo, as an explicit function of state variables (concentration of biomass, X; concentration of organic
matter, Sc; concentration of dissolved oxygen, So; and volume, V). The estimated model parameter
values were compared with values from the normal operation. If the residual of model parameters
exceeds a certain threshold value, a fault is detected. The application demonstrates the viability of
the approach, and highlights its ability to detect faults in wastewater treatment systems by providing
quick and accurate parameter estimates using the evaluation of explicit parametric functions.},
             url = {doi:10.3390/pr6110231}
}