The application of model predictive control of ammonia nitrogen in an activated sludge process

2011 ◽  
Vol 64 (5) ◽  
pp. 1115-1121 ◽  
Author(s):  
D. Vrečko ◽  
N. Hvala ◽  
M. Stražar
Keyword(s):  
Activated Sludge ◽  
Prediction Interval ◽  
Ammonia Nitrogen ◽  
Pi Controller ◽  
Ammonia Removal ◽  
Activated Sludge Process ◽  
Pi Controllers ◽  
Predictive Controller ◽  
Control Criteria ◽  
Improved Accuracy

In this paper a model predictive controller (MPC) for ammonia nitrogen is presented and evaluated in a real activated sludge process. A reduced nonlinear mathematical model based on mass balances is used to model the ammonia nitrogen in the activated sludge plant. An MPC algorithm that minimises only the control error at the end of the prediction interval is applied. The results of the ammonia MPC were compared with the results of the ammonia feedforward-PI and ammonia PI controllers from our previous study. The ammonia MPC and ammonia feedforward-PI controller give better results in terms of ammonia removal and aeration energy consumption than the ammonia PI controller because of the measurable disturbances used. On the other hand, with the ammonia MPC, comparable or even slightly poorer results than with the ammonia feedforward-PI controller are obtained. Further improvements to the MPC could be possible with an improved accuracy of the nonlinear reduced model of the ammonia nitrogen, more sophisticated control criteria used inside the controller and the extension of the problem from univariable ammonia to multivariable total nitrogen control.

Improvement of ammonia removal in activated sludge process with feedforward-feedback aeration controllers

2006 ◽  
Vol 53 (4-5) ◽  
pp. 125-132 ◽  
Author(s):  
Darko Vrečko ◽  
Nadja Hvala ◽  
Aljaž Stare ◽  
Olga Burica ◽  
Marjeta Stražar ◽  
...  
Keyword(s):  
Activated Sludge ◽  
Pilot Plant ◽  
Pi Controller ◽  
Ammonia Removal ◽  
Activated Sludge Process ◽  
Aerobic Reactor

In the paper three linear aeration controllers that can be easily implemented are presented and evaluated on the activated sludge process pilot plant. Controllers differ according to the information that is used about the process, which can be oxygen in the last aerobic reactor, ammonia in the last aerobic reactor and ammonia in the influent. The aeration controllers that are addressed are: oxygen cascade PI controller, ammonia cascade PI controller and ammonia feedforward-cascade PI controller. Experiments show that, in comparison with the oxygen cascade PI controller, the ammonia cascade PI controller allows better control of effluent ammonia and airflow savings of around 23%, while the ammonia feedforward-cascade PI controller gives the best reduction of ammonia peaks and can save up to 45% of the airflow.

Automatic control for DO and pH in the activated sludge process in a coke wastewater treatment plant

1998 ◽  
Vol 37 (12) ◽  
pp. 141-148 ◽  
Author(s):  
B. K. Lee ◽  
S. W. Sung ◽  
H. D. Chun ◽  
J. K. Koo
Keyword(s):  
Wastewater Treatment ◽  
Control System ◽  
Activated Sludge ◽  
Automatic Control ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Pi Controller ◽  
Activated Sludge Process ◽  
Set Point ◽  
Coke Wastewater

The objective of this study is to develop an automatic control system for dissolved oxygen (DO) and pH of the activated sludge process in a coke wastewater treatment plant. A discrete type autotuned proportional-integral (PI) controller using an auto-regressive exogenous (ARX) model as a process model was developed to maintain the DO concentration in aerators by controlling the speed of surface aerators. Also a nonlinear pH controller using the titration curve was used to control the pH of influent wastewater. This control system was tested in a pilot scale plant. During this pilot plant experiment, there was small deviation of pH and the electric power consumption of surface aerators was reduced up to 70% with respect to the full operation when the DO set point was 2 mg/l. For real plant operation with this system, the discrete PI controller showed good tracking for set point change. The electricity saving was more than 40% of the electricity consumption when considering surface aerators. As a result of maintaining the DO constantly at the set point by the automatic control system, the fluctuation of effluent quality was decreased and overall improvement of the effluent water quality was achieved.

Unknown Inputs Observer-Based Output Feedback Predictive Controller for an Activated Sludge Process

2018 ◽  
Vol 66 (4) ◽  
pp. 556-568
Author(s):  
Feten Smida ◽  
Taoufik Ladhari ◽  
Salim Hadj Saïd ◽  
Faouzi M'sahli
Keyword(s):  
Activated Sludge ◽  
Output Feedback ◽  
Activated Sludge Process ◽  
Unknown Inputs ◽  
Predictive Controller

Study on Simultaneous Nitrification and Denitrification with Biofilm for Chemical Wastewater

2011 ◽  
Vol 396-398 ◽  
pp. 1944-1949
Author(s):  
Cai Yan He ◽  
Yan Xu ◽  
Yan Yan Chen ◽  
Sheng Yu ◽  
Jing Ming Liu
Keyword(s):  
Activated Sludge ◽  
Removal Efficiency ◽  
Treatment Plant ◽  
Ammonia Nitrogen ◽  
Simultaneous Nitrification And Denitrification ◽  
Activated Sludge Process ◽  
Chemical Company ◽  
Nitrification And Denitrification ◽  
Biofilm Process ◽  
Made In

Aiming at the problem of lower removal efficiency of ammonia nitrogen in the activated sludge process for wastewater treatment plant of Jilin Chemical Company, a few tests were studied such as filler selection, biological hydrolysis-acidification process, nitrobacteria cultivation, activated sludge process and biofilm process, the feasibility of simultaneous nitrification and denitrification(SND) was developed with biofilm treatment for chemical poor-biological wastewater. The results showed that: SND occurred in the aerobic section, effluent total basicity (TB) in biofilm process was 2mmol / L or more, and was higher than activated sludge process, while the effluent TB in activated sludge process was about 0.1mmol / L,and nitrate produced by activated sludge process was 30mg/L higher than that of biofilm process. Nitrogen removal efficiency using SJ-1-type filler made in Jiangsu Suzhou Purification Group Co., Ltd was 15.01% more than that using combined filler.

Ammonia-based aeration control with optimal SRT control: improved performance and lower energy consumption

2019 ◽  
Vol 79 (1) ◽  
pp. 63-72 ◽  
Author(s):  
Oliver Schraa ◽  
Leiv Rieger ◽  
Jens Alex ◽  
Ivan Miletić
Keyword(s):  
Energy Consumption ◽  
Dissolved Oxygen ◽  
Activated Sludge ◽  
Ammonia Nitrogen ◽  
Rate Of Change ◽  
Activated Sludge Process ◽  
Total Ammonia Nitrogen ◽  
Supervisory Controller ◽  
Total Ammonia ◽  
Aeration Control

Abstract Ammonia-based aeration control (ABAC) is a cascade control concept for controlling total ammonia nitrogen (NHx-N) in the activated sludge process. Its main goals are to tailor the aeration intensity to the NHx-N loading and to maintain consistent nitrification, to meet effluent limits but minimize energy consumption. One limitation to ABAC is that the solids retention time (SRT) control strategy used at a water resource recovery facility (WRRF) may not be consistent with the goals of ABAC. ABAC-SRT control is a strategy for aligning the goals of ammonia-based aeration control and SRT control. A supervisory controller is used to ensure that the SRT is always optimal for ABAC. The methodology has the potential to reduce aeration energy consumption by over 30% as compared to traditional dissolved oxygen (DO) control. Practical implementation aspects are highlighted for implementation at full scale, such as proper selection of the set point for the supervisory controller, proper calculation of the rate of change in sludge inventory, using a mixed liquor suspended solids (MLSS) controller, and tuning of the controllers. In conclusion, ABAC-SRT is a promising approach for coordinated control of SRT, total ammonia nitrogen, and dissolved oxygen in the activated sludge process that balances both treatment performance and energy savings.

Improvement of Activated Sludge Process Using Enhanced Nonlinear PI Controller

2014 ◽  
Vol 39 (8) ◽  
pp. 6575-6586 ◽  
Author(s):  
S. I. Samsudin ◽  
M. F. Rahmat ◽  
N. A. Wahab ◽  
M. C. Razali ◽  
M. S. Gaya ◽  
...  
Keyword(s):  
Activated Sludge ◽  
Pi Controller ◽  
Activated Sludge Process

Design of a generalized predictive controller for a biological wastewater treatment plant

2016 ◽  
Vol 73 (8) ◽  
pp. 1986-2006 ◽  
Author(s):  
M. Sadeghassadi ◽  
C. J. B. Macnab ◽  
D. Westwick
Keyword(s):  
Wastewater Treatment ◽  
Dissolved Oxygen ◽  
Activated Sludge ◽  
Control Strategy ◽  
Treatment Plant ◽  
Effective Control ◽  
Activated Sludge Process ◽  
Measured Variable ◽  
Penalty Term ◽  
Predictive Controller

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 also suggests how a constraint on the measured variable can be added as a penalty term to the GPC framework which leads to improved control of the dissolved oxygen concentration in the presence of dynamic input disturbance.

Modelling, control and supervisory optimization of generalized predictive control in catalytic cracking reactor

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Mythily Mani ◽  
Manamalli Deivasigamani ◽  
Rames Chandra Panda ◽  
Raja Nandhini Ramasami
Keyword(s):  
Predictive Control ◽  
Catalytic Cracking ◽  
Pi Controller ◽  
Generalized Predictive Control ◽  
Yield Model ◽  
Yield Optimization ◽  
Gasoline Demand ◽  
Fluidized Catalytic Cracking ◽  
Pi Controllers ◽  
Predictive Controller

Abstract As gasoline demand increases, the efficiency of operation of Fluidized Catalytic Cracking Unit (FCCU) becomes paramount importance. In this paper, a dynamic model for FCCU is simulated and integrated with yield model in order to estimate the yield of products namely gasoline, light gases and coke. Conventional PI controllers are designed for the control of reactor and regenerator temperature. Since, the complete reaction occurs in a very short duration, the controllers are tuned so as to achieve shorter settling time and minimum overshot. Further in order to increase the yield, optimization of FCCU using Generalized Predictive Controller (GPC) at supervisory level is attempted. Through optimization of objective function, the GPC will provide optimized set point for the PI controller in order to maintain maximum gasoline yield.

scholarly journals Data-driven adaptive predictive control for an activated sludge process

2020 ◽  
Vol 9 (5) ◽  
pp. 1827-1834
Author(s):  
Mashitah C. Razali ◽  
Norhaliza Abdul Wahab ◽  
Syahira Ibrahim ◽  
Azavitra Zainal ◽  
M. F. Rahmat ◽  
...  
Keyword(s):  
Activated Sludge ◽  
Predictive Control ◽  
Adaptive Strategy ◽  
Operating Conditions ◽  
Data Driven ◽  
Activated Sludge Process ◽  
Direct Identification ◽  
Identification Technique ◽  
Predictive Controller ◽  
Adaptive Model Predictive Control

Data-driven control requires no information of the mathematical model of the controlled process. This paper proposes the direct identification of controller parameters of activated sludge process. This class of data-driven control calculates the predictive controller parameters directly using subspace identification technique. By updating input-output data using receding window mechanism, the adaptive strategy can be achieved. The robustness test and stability analysis of direct adaptive model predictive control are discussed to realize the effectiveness of this adaptive control scheme. The applicability of the controller algorithm to adapt into varying kinetic parameters and operating conditions is evaluated. Simulation results show that by a proper and effective excitation of direct identification of controller parameters, the convergence and stability of the implicit predictive model can be achieved.

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