2016
DOI: 10.2166/wst.2016.050
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Design of a generalized predictive controller for a biological wastewater treatment plant

Abstract: This paper presents a generalized predictive control (GPC) technique to regulate the activated sludge process found in a bioreactor used in wastewater treatment. The control strategy can track dissolved oxygen setpoint changes quickly, adapting to the system uncertainties and disturbances. Tests occur on an Activated Sludge Model No. 1 benchmark of an activated sludge process. A T filter added to the GPC framework results in an effective control strategy in the presence of coloured measurement noise. This work… Show more

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Cited by 14 publications
(6 citation statements)
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“…Therefore, to compensate for complexity in modeling and tuning with NARX models, a linear approximation of the UV/H2O2 process using the ARX model is adequate if its implementation will be followed by this specific set-up and an acclimated biological treatment system or other H2O2 elimination processes. Although this study has demonstrated some feasibility of the control scheme developed, it is suggested that similar scenarios should be considered in future design, validation, and performance evaluation of the entire controlled UV/H2O2 photo-reactor, especially for improving controller robustness by implementing an adaptive control, linear or non-linear predictive control schemes (Francisco et al, 2015(Francisco et al, , 2010Jacob and Dhib, 2011;Liu and Yoo, 2016;Sadeghassadi et al, 2016).…”
Section: Discussionmentioning
confidence: 99%
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“…Therefore, to compensate for complexity in modeling and tuning with NARX models, a linear approximation of the UV/H2O2 process using the ARX model is adequate if its implementation will be followed by this specific set-up and an acclimated biological treatment system or other H2O2 elimination processes. Although this study has demonstrated some feasibility of the control scheme developed, it is suggested that similar scenarios should be considered in future design, validation, and performance evaluation of the entire controlled UV/H2O2 photo-reactor, especially for improving controller robustness by implementing an adaptive control, linear or non-linear predictive control schemes (Francisco et al, 2015(Francisco et al, , 2010Jacob and Dhib, 2011;Liu and Yoo, 2016;Sadeghassadi et al, 2016).…”
Section: Discussionmentioning
confidence: 99%
“…Among all linear regression methods, the ARX model has a much simpler and more suitable model structure and lower requirements for data, making it suitable for studying a SISO system similar to this study. Based on the ARX algorithm, a linear model of a SISO system is (Sadeghassadi et al, 2016;Shahwan et al, 2021):…”
Section: Linear Arxmentioning
confidence: 99%
“…In comparison to the NARX, the HW only utilizes a linear block with a discrete transfer function to describe the dynamics of the process . The structure of the HW model is shown in Figure , where h is the nonlinear function that transforms system input data ( x ) to the input for the linear block ( u ) at u = h ( x ); B / A is the linear transfer function that relates its transfer function output ( v ) to input ( u ); and p is a nonlinear function that maps the output of the linear block ( v ) to the system output ( y ) at y = p ( v ). , Hence, the process output is related to process x by the expression When the nonlinearity of input/output structure is unknown, the process nonlinearity can be defined by the HW model as piecewise linear functions, similar to the piecewise function utilized in the NARX model with the tree partition network. The linearized input/output is computed using the input and output nonlinearity functions, and the linear block is determined in the same fashion as that of the linear ARX model fitting.…”
Section: Materials and Methodsmentioning
confidence: 99%
“…In recent years, various advanced and intelligent control methods [5][6][7][8][9][10][11][12] have been proposed to improve the control performance of WWTP, where the typical methods include model predictive control (MPC), fuzzy control and neural network control. Holenda et al [5] employed the MPC method to realize DO tracking control.…”
Section: Introductionmentioning
confidence: 99%