ARTIFICIAL NEURAL NETWORK SIMULATION OF ADVANCED BIOLOGICAL WASTEWATER TREATMENT PLANT PERFORMANCE

DEMİR S.

SIGMA JOURNAL OF ENGINEERING AND NATURAL SCIENCES-SIGMA MUHENDISLIK VE FEN BILIMLERI DERGISI, cilt.38, sa.4, ss.1713-1728, 2020 (ESCI) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 38 Sayı: 4
  • Basım Tarihi: 2020
  • Dergi Adı: SIGMA JOURNAL OF ENGINEERING AND NATURAL SCIENCES-SIGMA MUHENDISLIK VE FEN BILIMLERI DERGISI
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Academic Search Premier, Directory of Open Access Journals
  • Sayfa Sayıları: ss.1713-1728
  • Anahtar Kelimeler: Wastewater treatment, biological nutrient removal, treatment performance, artificial neural networks, PREDICTION
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

Artificial neural network (ANN) simulation of chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) removal efficiencies of an advanced biological wastewater treatment process is presented in this study. Seven input parameters (predictors) were used: influent COD, TN, and TP concentrations, internal recycle (IR) and return activated sludge (RAS) ratios, wastewater temperature, and total hydraulic retention time (HRT) of process reactors. Results showed that open-source ANN tools can easily be employed for quick and reliable simulation results. ANN with the logistic, the sinc, and the Elliot functions can be confidently employed for predicting COD, TN, and TP removal efficiencies. Mean square errors were 5.54*10(-7), 2.06*10(-4), and 2.26*10(-3), respectively, for COD, TN, and TP removal efficiencies. Besides, wastewater temperature was found to be the major factor that determines the performance of a wastewater treatment system while RAS ratio, HRT, and influent wastewater characteristics are also effective on the performance.