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A novel approach to wastewater treatment control: A self-organizing fuzzy sliding mode controller

IAES International Journal of Artificial Intelligence · Vol. 13 No. 3 (2024) · hlm. 2796–2807

DOI
10.11591/ijai.v13.i3.pp2796-2807
ISSN
2252-8938, 2089-4872
Terbit
2024-09-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

The treatment of wastewater plays a crucial role in protecting the environment and ensuring the sustainable use of resources. This research paper presents a new methodology for managing wastewater treatment operations, utilising Self-Organizing Fuzzy Sliding Mode Controller (SOFSMC) to enhance the efficiency of treatment procedures. MATLAB Simulink functions as a simulation tool that facilitates meticulous analysis. SOFSMC presents a control strategy that is both adaptive and robust. This strategy effectively regulates crucial parameters, including dissolved oxygen levels, pH levels, and flow rates. It achieves this within the challenging and complex framework of wastewater treatment, which is characterised by dynamic and nonlinear dynamics. Using a SOFSMC for wastewater treatment control is novel approach. This novel technique creates a self-learning, dynamic system using fuzzy logic (FL) and sliding mode control (SMC). This unique approach can autonomously adapt to wastewater treatment processes' complex and nonlinear dynamics, improving efficiency, resource optimisation, and system dependability. The results emphasise the potential of SOFSMC as a revolutionary approach for wastewater treatment. This approach can improve treatment effectiveness, conserve resources, and protect the environment. The proposed method SOFSMC, exhibits commendable outcomes, with an integrated absolute error of 0.082 mg/L, an integrated square differential error of 0.091 mg/L, and a response time of 1.85 seconds This study offers a substantial advancement in the field of wastewater treatment regulation, highlighting its significance in the context of sustainable water management and environmental conservation.

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  • Jain University
  • Sahyadri College of Engineering & Management
  • Nitte University
  • KLS Gogte Institute of Technology
  • AISSMS Institute of Information Technology
  • SRM Institute of Science and Technology
  • M. Kumarasamy College of Engineering
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Rekan penulisOrganisasiArtikel bersama
Abdul KareemAfiliasi tidak diketahui4
Sanjana PoojaryAfiliasi tidak diketahui1
T ThilagrajAfiliasi tidak diketahui1
Utkrishna Udesh NaikAfiliasi tidak diketahui1
R BharathAfiliasi tidak diketahui1
Melwin D SouzaSahyadri College of Engineering & Management1
Ananth Prabhu GurpurAfiliasi tidak diketahui1
Ranjith BhatJain University1
Rajashree NambiarNitte University1
Akshatha NaikAfiliasi tidak diketahui1
Ashwini AshwiniAfiliasi tidak diketahui1
Navilgone Krishna VaishnaviAfiliasi tidak diketahui1
Ruchitha Kamath SubhashchandraAfiliasi tidak diketahui1
Trapthi TrapthiAfiliasi tidak diketahui1
Ms P. ShruthiJain University1
Chandra Shekar S. M.Afiliasi tidak diketahui1
Altaf Osman MulaniAISSMS Institute of Information Technology1
Swapna SiddamsettiKLS Gogte Institute of Technology1
Melwin D SouzaAfiliasi tidak diketahui1
S SubbaiahSRM Institute of Science and Technology1
S. RamyaM. Kumarasamy College of Engineering1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">A novel approach to wastewater treatment control: A self-organizing fuzzy sliding mode controller</dc:title>
	<dc:creator>Kumara, Varuna</dc:creator>
	<dc:creator>Ganesan, Ezhilarasan</dc:creator>
	<dc:subject xml:lang="en-US">Fuzzy Logic; Self- Organizing Fuzzy Sliding Mode Controller</dc:subject>
	<dc:subject xml:lang="en-US">Dissolved oxygen; Effluents; Fuzzy logic; Sliding mode controller; Wastewater treatment</dc:subject>
	<dc:description xml:lang="en-US">The treatment of wastewater plays a crucial role in protecting the environment and ensuring the sustainable use of resources. This research paper presents a new methodology for managing wastewater treatment operations, utilising Self-Organizing Fuzzy Sliding Mode Controller (SOFSMC) to enhance the efficiency of treatment procedures. MATLAB Simulink functions as a simulation tool that facilitates meticulous analysis. SOFSMC presents a control strategy that is both adaptive and robust. This strategy effectively regulates crucial parameters, including dissolved oxygen levels, pH levels, and flow rates. It achieves this within the challenging and complex framework of wastewater treatment, which is characterised by dynamic and nonlinear dynamics. Using a SOFSMC for wastewater treatment control is novel approach. This novel technique creates a self-learning, dynamic system using fuzzy logic (FL) and sliding mode control (SMC). This unique approach can autonomously adapt to wastewater treatment processes' complex and nonlinear dynamics, improving efficiency, resource optimisation, and system dependability. The results emphasise the potential of SOFSMC as a revolutionary approach for wastewater treatment. This approach can improve treatment effectiveness, conserve resources, and protect the environment. The proposed method SOFSMC, exhibits commendable outcomes, with an integrated absolute error of 0.082 mg/L, an integrated square differential error of 0.091 mg/L, and a response time of 1.85 seconds This study offers a substantial advancement in the field of wastewater treatment regulation, highlighting its significance in the context of sustainable water management and environmental conservation.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">Moodlakatte Institute of Technology, Kundapura</dc:contributor>
	<dc:date>2024-09-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/24485</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i3.pp2796-2807</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 3: September 2024; 2796-2807</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v13.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/24485/14088</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>