MODELING AND CONTROL OF WASTEWATER TREATMENT EFFICIENCY WITH MAMDANI FUZZY INFERENCE SYSTEM

Abstract Efficient wastewater treatment is essential for sustainable environmental management, yet conventional mathematical models often fail to capture the nonlinear and uncertain dynamics of biological processes. This study develops a Mamdani-type Fuzzy Inference System (FIS) to model and control wastewater treatment efficiency using operational data. Nearly 15,000 data samples, obtained from a mathematical process model, were analyzed to identify the main variables influencing treatment performance. The fuzzy logic framework incorporates linguistic rules and membership functions, enabling the system to manage uncertainty and approximate expert decision-making in real time. Simulation results demonstrate that the Mamdani FIS achieves accurate predictions of treatment efficiency and provides robust control of critical process parameters under fluctuating conditions. The model enhances dissolved oxygen regulation, improves pollutant removal, and stabilizes bioreactor operation, ultimately leading to higher energy efficiency and better effluent quality. Overall, the findings highlight the effectiveness of Mamdani fuzzy logic as a decision-support tool for intelligent and adaptive wastewater treatment management.