title: Prediction of effluent concentration in a wastewater treatment plant using machine learning models.
abstract: Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen (T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models-artificial neural networks (ANNs) and support vector machines (SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination (R2), Nash-Sutcliff efficiency (NSE), relative efficiency criteria (drel). Additionally, Latin-Hypercube one-factor-at-a-time (LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage. However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.
authors.affiliation: School of Environmental Science and Engineering, Gwangju Institute of Science and Technology (GIST), 261 Cheomdan-gwagiro, Buk-gu, Gwangju 500-712, Republic of Korea.
authors.forename: Hong
authors.initials: H
authors.lastname: Guo
authors.affiliation: School of Environmental Science and Engineering, Gwangju Institute of Science and Technology (GIST), 261 Cheomdan-gwagiro, Buk-gu, Gwangju 500-712, Republic of Korea.
authors.forename: Kwanho
authors.initials: K
authors.lastname: Jeong
authors.affiliation: School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, Republic of Korea.
authors.forename: Jiyeon
authors.initials: J
authors.lastname: Lim
authors.affiliation: School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, Republic of Korea.
authors.forename: Jeongwon
authors.initials: J
authors.lastname: Jo
authors.affiliation: School of Environmental Science and Engineering, Gwangju Institute of Science and Technology (GIST), 261 Cheomdan-gwagiro, Buk-gu, Gwangju 500-712, Republic of Korea.
authors.forename: Young Mo
authors.initials: YM
authors.lastname: Kim
authors.affiliation: HECOREA. INC, 405, Woori Venture Town II, 70, Seonyu-ro, Yeongdeungpo-gu, Seoul, Republic of Korea.
authors.forename: Jong-pyo
authors.initials: JP
authors.lastname: Park
authors.affiliation: School of Environmental Science and Engineering, Gwangju Institute of Science and Technology (GIST), 261 Cheomdan-gwagiro, Buk-gu, Gwangju 500-712, Republic of Korea.
authors.forename: Joon Ha
authors.initials: JH
authors.lastname: Kim
authors.affiliation: School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, Republic of Korea. Electronic address: khcho@unist.ac.kr.
authors.forename: Kyung Hwa
authors.initials: KH
authors.lastname: Cho
mesh_terms: D016571:Neural Networks, Computer; D009584:Nitrogen / Q000032:analysis*; D011379:Prognosis; D056910:Republic of Korea; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D062065:Wastewater / Q000737:chemistry*; D018508:Water Purification / Q000379:methods*; D060753:Water Quality
chemical_list: D009584:Nitrogen; D062065:Wastewater
keywords: Artificial neural network; Effluent concentration; Prediction accuracy; Sensitivity analysis; Support vector machine