Application of Fuzzy Sugeno Model to Constructing a Digital Twin of a Wastewater Treatment Bioreactor
Year: 2025
Type: article
Abstract: This article discusses the application of Sugeno-type fuzzy logic for constructing a digital twin of a bioreactor used in municipal wastewater treatment processes. A fuzzy model was implemented based on six input variables that represent key operational parameters of the bioreactor, using the Sugeno inference system. The model was developed in the MATLAB Fuzzy Logic Designer environment, employing product (prod) for "And" operations, minimum (min) for implication, maximum (max) for aggregation, and weighted average for defuzzification. The proposed system generates three output signals that reflect the behavior of the bioreactor under various operational conditions. The results demonstrate high accuracy and adaptability of the model in predicting system responses, making it an effective tool for developing digital twins in environmental and engineering applications. The fuzzy inference system was developed using MATLAB’s Fuzzy Logic Designer, employing the Sugeno model structure due to its suitability for dynamic systems and real-time applications. Logical operations within the model were implemented using the product method for conjunctions, minimum for implication, maximum for aggregation, and weighted average for defuzzification. These settings ensure precise rule evaluation and output calculation. The simulation results show that the constructed fuzzy model provides accurate and robust predictions of the bioreactor’s output behavior, even in the presence of nonlinearities and uncertainties inherent in biological wastewater treatment processes. The system exhibits strong generalization capabilities and can be easily adapted for other types of reactors or environmental systems. The research confirms the effectiveness of soft computing approaches in the digital transformation of wastewater treatment infrastructure and underscores the potential of fuzzy logic as a core component in the design of intelligent digital twins for environmental engineering applications.
Author: Mammadazada K.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7083464101
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