RESEARCH PAPER
A New Approach to Designing Control of Dissolved Oxygen and Aeration System in Sequencing Batch Reactor by Applied Backstepping Control Algorithm
 
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1
Faculty of Electrical and Control Engineering, Gdańsk University of Technology, G. Narutowicza 11/12, 80-233 , Gdańsk, Poland
 
2
Digital Technologies Center, Gdańsk University of Technology, G. Narutowicza 11/12, 80-233 , Gdańsk, Poland
 
 
Submission date: 2023-01-25
 
 
Acceptance date: 2023-06-16
 
 
Online publication date: 2023-12-30
 
 
Publication date: 2023-12-01
 
 
Acta Mechanica et Automatica 2023;17(4):605-612
 
KEYWORDS
ABSTRACT
The wastewater treatment plant (WWTP) is a complex system due to its non-linearity, time-variance and multiple time scales in its dynamics among others. The most important control parameter in a WWTP is the dissolved oxygen (DO) concentration. The tracking problem of the DO concentration is one of the most fundamental issues in biological wastewater treatment. Proper control of DO concentration is necessary to achieve adequate biological conditions for microorganisms in the WWTP. Aeration is an important process to achieve those conditions, but it is expensive. It was performed using an aeration system, which includes blowers, pipelines and diffusers. This paper presents a new approach to designing a non-linear control system for controlling DO concentration using an adaptive backstepping algorithm. A model of biological processes and aeration system were applied in designing the control system. Simulation tests of the control system were performed and very good results on control were obtained. The proposed solution has proved to be effective and computationally efficient.
REFERENCES (20)
1.
Riffat R, Husnain T. Fundamentals of Wastewater Treatment and Engineering. 2nd ed. London: CRC Press; 2022.
 
2.
Khan AH, Rudayni HA, Chaudhary AA, Imran M, Vambol S. Modern use of modified Sequencing Batch Reactor in wastewater Treatment. EQ [Internet]. 2022 Jul. 11 [cited 2023 Jan. 25];33(4):1-23.
 
3.
Pittoors E, Guo Y, Van Hulle SWH. Modeling Dissolved Oxygen Concentration for Optimizing Aeration Systems and Reducing Oxygen Consumption in Activated Sludge Processes: A Review. Chemical Engineering Communications. 2014;201(8): 983-1002.
 
4.
Olsson G, Rundqwist L, Eriksson L, Hall L. Self-Tuning Control of the Dissolved Oxygen Concentration in Activated Sludge Systems. Instrumentation and Control of Water and Wastewater Treatment and Transport Systems: Proceedings of the 4th IAWPRC Workshop Held in Houston and Denver, U.S.A., 27 April – 4 May 1985. Pergamon Press Ltd. 1985:473-480.
 
5.
Holmberg U. Adaptive Dissolved Oxygen Control and On-Line Estimation of Oxygen Transfer and Respiration Rates. Department of Automatic Control. Lund Institute of Technology; 1987.
 
6.
Nejjari F, Khoury B, PuigV, Quevedo J, PascualJ, de Campos S. Economic Linear Parameter Varying Model Predictive Control of the Aeration System of a Wastewater Treatment Plant. Sensors. 2022;22(16):6008.
 
7.
Sheik AG, Seepana MM, Ambati SR. Supervisory control configurations design for nitrogen and phosphorus removal in wastewater treatment plants. Water Environ Res. 2021;93(8):1289-1302.
 
8.
Piotrowski R. Supervisory fuzzy control system for biological processes in sequencing wastewater batch reactor. Urban Water Journal. 2020;17:325-32.
 
9.
Man Y, Shen WH, Chen XQ, Long Z, Corriou, JP. Dissolved oxygen control strategies for the industrial sequencing batch reactor of the wastewater treatment process in the papermaking industry. Environmental Science-Water Research & Technology. 2018;4(5):654–662.
 
10.
Du X, Wang J, Jegatheesan V, Shi G. Dissolved Oxygen Control in Activated Sludge Process Using a Neural Network-Based Adaptive PID Algorithm. Applied Sciences. 2018;8(2):261.
 
11.
Nguyen A, Taniguchi T, Eciolaza L, Campos V, Palhares R, Sugeno M. Fuzzy Control Systems: Past, Present and Future. IEEE Computational Intelligence Magazine. 2019; 14(1): 56-68.
 
12.
Khatri N, Khatri KK, Sharma A. Enhanced Energy Saving in Wastewater Treatment Plant using Dissolved Oxygen Control and Hydrocyclone. Environmental Technology & Innovation. 2020; 18: 100678.
 
13.
Bai K, Jiang G, Jiang G, Liu Z.Based on fuzzy-approximation adaptive backstepping control method for dual-arm of humanoid robot with trajectory tracking. International Journal of Advanced Robotic Systems.2019;16(3).
 
14.
Chaabane H, Djalal Eddine K, Salim C. Sensorless back stepping control using a Luenberger observer for double-star induction motor. Archives of Electrical Engineering. 2019;69(1):101-116.
 
15.
Henze M, Gujer W, Mino T, Matsuo T, Wentzel MC, Marais GVR, Van Loosdrecht MC. Activated sludge model no. 2d, ASM2d. Water science and technology. 1999;39(1):165-182.
 
16.
IFAK Technology, Simba. user’s Guide. https://www.ifak.eu/en/produkt.... Accessed: 2023-01-10.
 
17.
Olsson G, Newell R. Wastewater treatment systems. Modelling, diagnosis and control. London: IWA Publishing; 1999.
 
18.
Piotrowski R, Brdyś MA, Konarczak K, Duzinkiewicz K, Chotkowski W. Hierarchical dissolved oxygen control for activated sludge processes. Control Engineering Practice. 2008;16(1):114-131.
 
19.
Piotrowski R, Ujazdowski T. Model of aeration system at biological wastewater treatment plant for control design purposes. Proc. of the 20th Polish Control Conference – KKA’2020. October 14-29, 2020, Łódź, Poland, (in:) Bartoszewicz A., Kabziński J., Kacprzyk J. (eds). Advanced, Contemporary Control. Advances in Intelligent Systems and Computing. Springer; 2020;1196:349-359.
 
20.
Witkowska A, Tomera M, Śmierzchalski R. A Backstepping Approach to Ship Course Control. International Journal of Applied Mathematics and Computer Science. 2007;17(1):73-85.
 
eISSN:2300-5319
ISSN:1898-4088
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