Predictive control of an activated sludge process: An application to the Viikinmäki wastewater treatment plant

Mulas, Michela; Tronci, Stefania; Corona, Francesco; Haimi, Henri; Lindell, Paula; Heinonen, Mari; Vahala, Riku; Baratti, Roberto

doi:10.1016/j.jprocont.2015.08.005

Cited by 52 publications

(17 citation statements)

References 15 publications

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“…This task is addressed using the simulator to carry on step response tests from which the dynamic matrix F is obtained [5]. The simulator model is excited by varying the input corresponding to the manipulated variables, starting from the reference condition and considering step changes of different sizes [13,14]. It is worth noting that the simulator does not model the variations of ingredients quality (composition and rheological behavior).…”

Section: Control Algorithmmentioning

confidence: 99%

Multivariable Real-Time Control of Viscosity Curve for a Continuous Production Process of a Non-Newtonian Fluid

et al. 2018

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Abstract:The application of a multivariable predictive controller to the mixing process for the production of a non-Newtonian fluid is discussed in this work. A data-driven model has been developed to describe the dynamic behaviour of the rheological properties of the fluid as a function of the operating conditions using experimental data collected in a pilot plant. The developed model provides a realistic process representation and it is used to test and verify the multivariable controller, which has been designed to maintain viscosity curves of the non-Newtonian fluid within a given region of the viscosity-vs-shear rate plane in presence of process disturbances occurring in the mixing process.

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Section: Control Algorithmmentioning

confidence: 99%

Multivariable Real-Time Control of Viscosity Curve for a Continuous Production Process of a Non-Newtonian Fluid

et al. 2018

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“…The system consists of a constant volume reactor cooled by a single coolant stream, and the objective is to control the effluent concentration, 1 , by manipulating the coolant flow rate, 1 . The model equations are reported as follows:…”

Section: Casementioning

confidence: 99%

“…The system has one natural input, which is the coolant flow rate, 1 . In order to provide more information on the state of the reactor, another input, 2 , is selected as energy balance around the cooling system.…”

Section: Casementioning

confidence: 99%

“…This solution can be justified by the fact that nonlinear processes can be approximated with a linear model as they approach steady state; therefore, in case of close regulatory control, the use of a linear control is adequate [1]. Nevertheless, for those nonlinear processes whose nonlinearities are strong and large changes of the operating conditions occur during the operation, linear controller designs are inadequate and more effective alternatives should be considered [2].…”

Section: Introductionmentioning

confidence: 99%

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A Gain-Scheduling PI Control Based on Neural Networks

Tronci¹,

Baratti²

2017

Complexity

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This paper presents a gain-scheduling design technique that relies upon neural models to approximate plant behaviour. The controller design is based on generic model control (GMC) formalisms and linearization of the neural model of the process. As a result, a PI controller action is obtained, where the gain depends on the state of the system and is adapted instantaneously on-line. The algorithm is tested on a nonisothermal continuous stirred tank reactor (CSTR), considering both single-input single-output (SISO) and multi-input multi-output (MIMO) control problems. Simulation results show that the proposed controller provides satisfactory performance during set-point changes and disturbance rejection.

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“…The proposed control strategy was designed based on the real data records and was validated by simulation. Other control algorithms using predictive control to control the DO concentration were used [8][9][10]. In 2018, the optimal variable setpoint and a setpoint tracking control system for the DO concentration were designed [11].…”

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confidence: 99%

Improving SBR Performance Alongside with Cost Reduction through Optimizing Biological Processes and Dissolved Oxygen Concentration Trajectory

2019

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Authors of this paper take under investigation the optimization of biological processes during the wastewater treatment in sequencing batch reactor (SBR) plant. A designed optimizing supervisory controller generates the dissolved oxygen (DO) trajectory for the lower level parts of the hierarchical control system. Proper adjustment of this element has an essential impact on the efficiency of the wastewater treatment process as well as on the costs generated by the plant, especially by the aeration system. The main goal of the presented solution is to reduce the plant energy consumption and to maintain the quality of effluent in compliance with the water-law permit. Since the optimization is nonlinear and includes variations of different types of variables, to solve the given problem the authors performed simulation tests and decided to implement a hybrid of two different optimization algorithms: artificial bee colony (ABC) and direct search algorithm (DSA). Simulation tests for the wastewater treatment plant case study are presented.

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Predictive control of an activated sludge process: An application to the Viikinmäki wastewater treatment plant

Cited by 52 publications

References 15 publications

Multivariable Real-Time Control of Viscosity Curve for a Continuous Production Process of a Non-Newtonian Fluid

Multivariable Real-Time Control of Viscosity Curve for a Continuous Production Process of a Non-Newtonian Fluid

A Gain-Scheduling PI Control Based on Neural Networks

Improving SBR Performance Alongside with Cost Reduction through Optimizing Biological Processes and Dissolved Oxygen Concentration Trajectory

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