Performance prediction and control for wastewater treatment plants using artificial neural network modeling of mechanical and biological treatment

被引:0
作者
Alnajjar, Hussein Y. H. [1 ]
Karadeniz, Osman Uecuencue [1 ]
机构
[1] Karadeniz Tech Univ, Civil Engn Fac, Hydraul Dept, Trabzon, Turkiye
基金
英国科研创新办公室;
关键词
artificial neural network; wastewater treatment; total phosphorus; total nitrogen; biological oxygen demand; EFFLUENT QUALITY;
D O I
10.24425/aep.2023.145893
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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 cant eff ort. However, the use of artifi cial neural network (ANN) modeling can help to more reliably and cost-eff ectively monitor the pollutant characteristics of wastewater treatment plants and regulate the processing of these pollutants. To create an artifi cial 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 infl uent features that would aff ect effl uent quality metrics. For ANN models, the correlation coeffi cients 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 effi ciency.
引用
收藏
页码:16 / 29
页数:14
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