چکیده مقاله
Purpose: The objective of this study is to investigate the role of artificial intelligence AI in monitoring and optimizing the performance of industrial wastewater treatment systems The increasing complexity of industrial effluents and the limitations of conventional process control methods have highlighted the necessity of adopting modern, data driven technologies In this context, the present research aims to identify the nonlinear relationships among key parameters influencing the treatment process through machine learning models, and to propose a framework for improving effluent quality while reducing operational costs Methods: The study is applied in nature and follows a descriptive–analytical approach Real operational data were collected from several industrial wastewater treatment plants and, after cleaning and normalization, analyzed using three machine learning algorithms: Artificial Neural Network ANN , Support Vector Machine SVM , and Random Forest RF The performance of the developed models was evaluated using statistical indicators, including the coefficient of determination R² , root mean square error RMSE , and mean absolute error MAE Results: The results revealed that the Artificial Neural Network ANN model demonstrated the best performance, achieving coefficients of determination of 0 92 for COD and 0 89 for TSS Moreover, the sensitivity analysis indicated that influent flow rate, organic loading, and aeration rate were the most influential parameters affecting the effluent quality Conclusion: The application of artificial intelligence in managing industrial wastewater treatment plants can enhance process control accuracy while reducing energy consumption and operational costs From a scientific perspective, this achievement represents a step toward the advancement of data driven environmental models, and from a practical standpoint, it provides an effective tool for promoting green governance within industrial sectors
کلیدواژهها
نویسندگان
Behnam Mosahebi
MSc Student, Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran
شیوه ارجاع
Mosahebi, Behnam,1404,AI-Based Framework for Performance Monitoring and Optimization in Industrial Wastewater Treatment,The 3rd National Conference on Environmental Challenges: The role of industry, mining, and society in developing green governance,Tehran
ارائهشده در
مجموعه مقالات سومین کنفرانس و نمایشگاه ملی چالش های محیط زیستی: نقش صنعت، معدن و جامعه در گسترش حکمرانی سبز19 آبان 1404 · تهران