Abstract
Artificial neural networks in the multivariable control of chlorine dosing in the post-chlorination stage in a water treatment plant in the Greater Sao Paulo, Brazil, are analyzed. The plant has constant fluctuations in chlorine demand caused by natural influences related to raw water from surface source. Modeling and computer simulation were implemented in MATLAB/Simulink ® environment, according to the physical and operational characteristics of the water treatment plant. Moreover, a Proportional-Integral (PI) controller was incorporated to provide better stability. Simulation results showed improved stability of free residual chlorine when compared to method currently employed, i.e. Proportional-Integral-Derivative (PID) controller that would reduce chlorine consumption in water treatment process.
Highlights
Water treatment eliminates or decreases suspended materials, microorganisms and other chemical compounds to safeguard public health (Tabesh, Azadi, & Roozbahani, 2011; Juntunen, Liukkonen, Lehtola, & Hiltunen, 2013; Plappally &Lienhard, 2013)
The most common type of water treatment is the conventional one, comprising pretreatment, coagulation, flocculation, sedimentation, filtration, final pH correction and free chlorine residual. The latter is important for water disinfection
The performance of the artificial neural network is measured by the mean squared error (MSE) and mean absolute error (MAE), respectively shown in Equation 1 and 2 (Ayodele & Auta, 2012; Zenooz et al, 2017):
Summary
Water treatment eliminates or decreases suspended materials, microorganisms and other chemical compounds to safeguard public health (Tabesh, Azadi, & Roozbahani, 2011; Juntunen, Liukkonen, Lehtola, & Hiltunen, 2013; Plappally &Lienhard, 2013). The most common type of water treatment is the conventional one, comprising pretreatment, coagulation, flocculation, sedimentation, filtration, final pH correction and free chlorine residual. The latter is important for water disinfection In many WTPs, the process has been automated and the electronic equipment corrects the dosers to maintain the free residual chlorine established (Soyupak, Kilic, Karadirek, & Muhammetoglu, 2011; Hong, Lee, Lee, Park, & Lee, 2012; Liu, Rong, Xu, & Zhang, 2013). Automation processes of a WTP continually seek water quality by optimizing the uptake of chemical products (Lee, Shin, Hong, Choi, & Chun, 2016)
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