@ARTICLE{Alnajjar_Hussein_Y.H._Performance_2023,
 author={Alnajjar, Hussein Y.H. and Üçüncü, Osman},
 volume={vol. 49},
 number={No 2},
 pages={16-29},
 journal={Archives of Environmental Protection},
 howpublished={online},
 year={2023},
 publisher={Polish Academy of Sciences},
 abstract={Biological treatment in wastewater treatment plants appears to be one of the most crucial factors in water quality management and planning. Though, measuring this important factor is challenging, and obtaining reliable results requires signifi can`t effort. However, the use of artificial neural network (ANN) modeling can help to more reliably and cost-effectively monitor the pollutant characteristics of wastewater treatment plants and regulate the processing of these pollutants. To create an artificial neural network model, a study of the Samsun Eastern Advanced Biological WWTP was carried out. It provides a laboratory simulation and prediction option for flexible treatment process simulations. The models were created to forecast influent features that would affect effluent quality metrics. For ANN models, the correlation coefficients RTRAINING and RALL are more than 0.8080. The MSE, RMSE, and MAPE were less than 0.8704. The model’s results showed compliance with the permitted wastewater quality standards set forth in the Turkish water pollution control law for the environment where the treated wastewater is discharged. This is a useful tool for plant management to enhance the quality of the treatment while enhancing the facility’s dependability and efficiency.},
 type={Article},
 title={Performance prediction and control for wastewater treatment plants using artificial neural network modeling of mechanical and biological treatment},
 URL={http://journals.pan.pl/Content/127293/PDF/Archives%20vol%2049%20no%202pp16_29.pdf},
 doi={10.24425/aep.2023.145893},
 keywords={artificial neural network, wastewater treatment, total phosphorus, total nitrogen, biological oxygen demand},
}