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Membrane Water Treatment Volume 9, Number 6, November 2018 , pages 455-462 DOI: https://doi.org/10.12989/mwt.2018.9.6.455 |
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Estimation of BOD in wastewater treatment plant by using different ANN algorithms |
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Osman Tugrul BAKİ and Egemen ARAS
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| Abstract | ||
| The measurement and monitoring of the biochemical oxygen demand (BOD) play an important role in the planning and operation of wastewater treatment plants. The most basic method for determining biochemical oxygen demand is direct measurement. However, this method is both expensive and takes a long time. A five-day period is required to determine the biochemical oxygen demand. This study has been carried out in a wastewater treatment plant in Turkey (Hurma WWTP) in order to estimate the biochemical oxygen demand a shorter time and with a lower cost. Estimation was performed using artificial neural network (ANN) method. There are three different methods in the training of artificial neural networks, respectively, multi-layered (ML-ANN), teaching learning based algorithm (TLBO-ANN) and artificial bee colony algorithm (ABC-ANN). The input flow (Q), wastewater temperature (t), pH, chemical oxygen demand (COD), suspended sediment (SS), total phosphorus (tP), total nitrogen (tN), and electrical conductivity of wastewater (EC) are used as the input parameters to estimate the BOD. The root mean squared error (RMSE) and the mean absolute error (MAE) values were used in evaluating performance criteria for each model. As a result of the general evaluation, the ML-ANN method provided the best estimation results both training and test series with 0.8924 and 0.8442 determination coefficient, respectively. | ||
| Key Words | ||
| artificial bee colony; artificial neural networks; biochemical oxygen demand; teaching-learning base algorithm; wastewater treatment plant | ||
| Address | ||
| Osman Tugrul BAKİ: Karadeniz Technical University, Faculty of Technology, Department of Civil Engineering, 61080 Trabzon, Turkey Egemen ARAS: Bursa Technical University Faculty of Engineering and Natural Sciences Department of Civil Engineering 16310 Bursa, Turkey | ||
