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Article

Prediction of Odor Concentration Emitted from Wastewater Treatment Plant Using an Artificial Neural Network (ANN)

1
Department of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology, 283, Goyangdae-Ro, Ilsanseo-Gu, Goyang, Gyeonggi-Do 10233, Korea
2
Department of Civil and Environmental Engineering, Sejong University, 209, Neungdong-Ro, Gwangjin-Gu, Seoul 05006, Korea
*
Author to whom correspondence should be addressed.
Atmosphere 2020, 11(8), 784; https://doi.org/10.3390/atmos11080784
Submission received: 9 June 2020 / Revised: 16 July 2020 / Accepted: 23 July 2020 / Published: 24 July 2020
(This article belongs to the Special Issue Application of Machine Learning in Air Pollution)

Abstract

The odor emitted from a wastewater treatment plant (WWTP) is an important environmental problem. An estimation of odor emission rate is difficult to detect and quantify. To address this, various approaches including the development of emission factors and measurement using a closed chamber have been employed. However, the evaluation of odor emission involves huge manpower, time, and cost. An artificial neural network (ANN) is recognized as an efficient method to find correlations between nonlinear data and prediction of future data based on these correlations. Due to its usefulness, ANN is used to solve complicated problems in various disciplines of sciences and engineering. In this study, a method to predict the odor concentration in a WWTP using ANN was developed. The odor concentration emitted from a WWTP was predicted by the ANN based on water quality data such as biological oxygen demand, dissolved oxygen, and pH. The water quality and odor concentration data from the WWTP were measured seasonally in spring, summer, and autumn and these were used as input variations to the ANN model. The odor predicted by the ANN model was compared with the measured data and the prediction accuracy was estimated. Suggestions for improving prediction accuracy are presented.
Keywords: Odor; Concentration prediction; Artificial intelligent; Regression Odor; Concentration prediction; Artificial intelligent; Regression

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MDPI and ACS Style

Kang, J.-H.; Song, J.; Yoo, S.S.; Lee, B.-J.; Ji, H.W. Prediction of Odor Concentration Emitted from Wastewater Treatment Plant Using an Artificial Neural Network (ANN). Atmosphere 2020, 11, 784. https://doi.org/10.3390/atmos11080784

AMA Style

Kang J-H, Song J, Yoo SS, Lee B-J, Ji HW. Prediction of Odor Concentration Emitted from Wastewater Treatment Plant Using an Artificial Neural Network (ANN). Atmosphere. 2020; 11(8):784. https://doi.org/10.3390/atmos11080784

Chicago/Turabian Style

Kang, Jeong-Hee, JiHyeon Song, Sung Soo Yoo, Bong-Jae Lee, and Hyon Wook Ji. 2020. "Prediction of Odor Concentration Emitted from Wastewater Treatment Plant Using an Artificial Neural Network (ANN)" Atmosphere 11, no. 8: 784. https://doi.org/10.3390/atmos11080784

APA Style

Kang, J.-H., Song, J., Yoo, S. S., Lee, B.-J., & Ji, H. W. (2020). Prediction of Odor Concentration Emitted from Wastewater Treatment Plant Using an Artificial Neural Network (ANN). Atmosphere, 11(8), 784. https://doi.org/10.3390/atmos11080784

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