Control en Estaciones Depuradoras de Aguas Residuales: Estado actual y perspectivas

Vilanova, Ramón; Santín, Ignacio; Pedret, Carles

doi:10.1016/j.riai.2017.09.001

Cited by 22 publications

(4 citation statements)

References 120 publications

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“…Wastewater treatment plants (WWTPs) are used around the world to guarantee the quality of water in the receiving environment, to maintain aquatic biodiversity. , Many of the water quality parameters are generally kept by the WWTPs within the legally established limits. However, the reduction of nutrients is one of the most complicated objectives and requires the application of advanced control strategies to achieve it.…”

Section: Introductionmentioning

confidence: 99%

Dissolved Oxygen Control in Biological Wastewater Treatments with Non-Ideal Sensors and Actuators

Santín

Barbu

Pedret

et al. 2019

Ind. Eng. Chem. Res.

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The improvement of the dissolved oxygen control is one of the main objectives in the research works on control of wastewater treatment plants. In the research literature, most of the works are based on benchmark simulation models, where ideal sensors and ideal actuators are commonly considered. However, it is important to note that the main difficulty of the dissolved oxygen control is due to noise and delay in the sensors and actuators. These are taken into account in this article with the aim of dissolved oxygen control improvement using the benchmark simulation model no. 1. The main purpose of this work is to highlight the need to take them into account and conduct a first step in analyzing how they affect the usually considered dissolved oxygen control approaches. The work proposes an approach for dissolved oxygen control improvement within non-ideal sensors and actuators using the benchmark simulation model no. 1, where a precise catalog and characterization of sensors and actuators are also provided (although not used). Filters are used to reduce the noise of the sensors. Artificial neural networks are designed to predict the value of dissolved oxygen, to compensate the delay produced by filters and sensors, as well as to anticipate the time needed by the actuator to obtain the desired value. The artificial neural networks take into account the microorganisms present in the wastewater, as well as their food and energy source, to predict the value of dissolved oxygen. The article shows different options of artificial neural networks for dry weather, rain, storm, and variable set-point. The results show meaningful integral of square error improvements, around 80% in dry weather and greater than 50% with rain and storm influents, as well as a significant reduction of abrupt changes in the actuator.

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Section: Introductionmentioning

confidence: 99%

Dissolved Oxygen Control in Biological Wastewater Treatments with Non-Ideal Sensors and Actuators

Santín

Barbu

Pedret

et al. 2019

Ind. Eng. Chem. Res.

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“…A diagram of the initial aeration system is shown in Figure 1. For process control, the San Pedro del Pinatar WWTP used classic on/off and proportional, integral and derivative (PID) methods [13]. When trying to control a WWTP that had complex and variable dynamics over time, with controls of this type, reacting to the error already produced, the result was unstable and not satisfactory in regulating the critical variables of the process, which were generally difficult to control.…”

Section: Initial Operating Conditions Of the Control Systems Regulatimentioning

confidence: 99%

“…When trying to control a WWTP that had complex and variable dynamics over time, with controls of this type, reacting to the error already produced, the result was unstable and not satisfactory in regulating the critical variables of the process, which were generally difficult to control. Such control was useful in many cases, but in others its performance For process control, the San Pedro del Pinatar WWTP used classic on/off and proportional, integral and derivative (PID) methods [13]. When trying to control a WWTP that had complex and variable dynamics over time, with controls of this type, reacting to the error already produced, the result was unstable and not satisfactory in regulating the critical variables of the process, which were generally difficult to control.…”

Section: Initial Operating Conditions Of the Control Systems Regulatimentioning

confidence: 99%

Methodology for Energy Optimization in Wastewater Treatment Plants. Phase III: Implementation of an Integral Control System for the Aeration Stage in the Biological Process of Activated Sludge and the Membrane Biological Reactor

Avilés¹,

Velázquez

Riquelme

2020

Sensors

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The proposed methodology for optimizing energy efficiency, based on good management of the aeration process through the implementation of an appropriate control strategy, achieved reductions of more than 40% in energy consumption at the San Pedro del Pinatar Wastewater Treatment Plant (WWTP) (Murcia, Spain). Phases I and II of this methodology managed to reduce the oxygen needs of the microorganisms in the biological system, optimize the efficiency of oxygen transfer to the biological reactor and redesign the installation to correct abnormal energy loss situations. In addition, we established the basis for Phase III, which implemented a control strategy to achieve stable values close to the setpoints of the fundamental operating parameters of the aeration process. The control system is based on the measurements recorded by strategically installed sensors and mathematical algorithms based on models, achieving an expert adaptive-predictive system that regulates aeration both in the biological stage by activated sludge and the aeration of the installed ultrafiltration membrane system. The objectives were: (i) to achieve automatic execution of the best management strategy; (ii) to reduce the energy demand; (iii) to improve the operation and stability of the process; (iv) to reduce operating costs; and (v) to contribute to the fulfillment of the sustainable development objectives.

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“…Proportional-integral-derivative (PID) control has been widely used because the control structure is simple and the role of tuning parameters is clear. See [1] as example of complex industrial application. The epoch-making rule of PID control has been investigated as the Ziegler and Nichols (ZN) method [2] and has been studied for more than half a century [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Optimal Robust PID Control for First- and Second-Order Plus Dead-Time Processes

et al. 2019

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The present study proposes a new design method for a proportional-integral-derivative (PID) control system for first-order plus dead-time (FOPDT) and over-damped second-order plus dead-time (SOPDT) systems. What is presented is an optimal PID tuning constrained to robust stability. The optimal tuning is defined for each one of the two operation modes the control system may operate in: servo (reference tracking) and regulation (disturbance rejection). The optimization problem is stated for a normalized second-order plant that unifies FOPDT and SOPDT process models. Different robustness levels are considered and for each one of them, the set of optimal controller parameters is obtained. In a second step, suitable formulas are found that provide continuous values for the controller parameters. Finally, the effectiveness of the proposed method is confirmed through numerical examples.

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Control en Estaciones Depuradoras de Aguas Residuales: Estado actual y perspectivas

Cited by 22 publications

References 120 publications

Dissolved Oxygen Control in Biological Wastewater Treatments with Non-Ideal Sensors and Actuators

Dissolved Oxygen Control in Biological Wastewater Treatments with Non-Ideal Sensors and Actuators

Methodology for Energy Optimization in Wastewater Treatment Plants. Phase III: Implementation of an Integral Control System for the Aeration Stage in the Biological Process of Activated Sludge and the Membrane Biological Reactor

Optimal Robust PID Control for First- and Second-Order Plus Dead-Time Processes

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