Prediction of effluent concentration in a wastewater treatment plant using machine learning models

• Published in: Journal of environmental sciences (China) Vol. 32; no. 6; pp. 90 - 101
• Main Authors: Guo, Hong, Jeong, Kwanho, Lim, Jiyeon, Jo, Jeongwon, Kim, Young Mo, Park, Jong-pyo, Kim, Joon Ha, Cho, Kyung Hwa
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.06.2015
• Subjects: Artificial neural network; Effluent concentration; Neural Networks (Computer); Nitrogen - analysis; Prediction accuracy; Prognosis; Republic of Korea; Sensitivity analysis; Sensitivity and Specificity; Support Vector Machine; Waste Water - chemistry; Water Purification - methods; Water Quality; 支持向量机; 机器学习; 模型预测; 水处理过程; 水浓度; 污水处理厂; 非线性时间序列; 食物垃圾; Republic of Korea; Korea, Rep; Prediction accuracy; Sensitivity analysis; Artificial neural network; Support vector machine; Effluent concentration
• ISSN: 1001-0742; 1878-7320
• DOI: 10.1016/j.jes.2015.01.007

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（R^2）, Nash-Sutcliff efficiency（NSE）, relative efficiency criteria（d rel）. 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.