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Sequential modelling of a full-scale wastewater treatment plant using an artificial neural network

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Abstract

This work proposes a sequential modelling approach using an artificial neural network (ANN) to develop four independent multivariate models that are able to predict the dynamics of biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspended solid (SS), and total nitrogen (TN) removal in a wastewater treatment plant (WWTP). Suitable structures of ANN models were automatically and conveniently optimized by a genetic algorithm rather than the conventional trial and error method. The sequential modelling approach, which is composed of two parts, a process disturbance estimator and a process behaviour predictor, was also presented to develop multivariate dynamic models. In particular, the process disturbance estimator was first employed to estimate the influent quality. The process behaviour predictor then sequentially predicted the effluent quality based on the estimated influent quality from the process disturbance estimator with other process variables. The efficiencies of the developed ANN models with a sequential modelling approach were demonstrated with a practical application using a data set collected from a full-scale WWTP during 2 years. The results show that the ANN with the sequential modelling approach successfully developed multivariate dynamic models of BOD, COD, SS, and TN removal with satisfactory estimation and prediction capability. Thus, the proposed method could be used as a powerful tool for the prediction of complex and nonlinear WWTP performance.

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Acknowledgments

This work was supported financially through Eco-Technopia 21 project (contract number 071-071-118) of the Korea Ministry of Environment, Republic of Korea. The authors thank the reviewers whose comments greatly improved the manuscript.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon, 305-701, Republic of Korea

    Joong-Won Lee & Hang-Sik Shin

  2. Water Environment Center, KIST, 39-1 Hawolgok-dong, Wolsong-gil 5, Seongbuk-gu, Seoul, Republic of Korea

    Changwon Suh

  3. Department of Civil and Environmental Engineering, London South Bank University, 103 Borough Road, London, UK

    Yoon-Seok Timothy Hong

Authors
  1. Joong-Won Lee
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  2. Changwon Suh
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  3. Yoon-Seok Timothy Hong
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  4. Hang-Sik Shin
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Correspondence to Hang-Sik Shin.

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Lee, JW., Suh, C., Hong, YS.T. et al. Sequential modelling of a full-scale wastewater treatment plant using an artificial neural network. Bioprocess Biosyst Eng 34, 963–973 (2011). https://doi.org/10.1007/s00449-011-0547-6

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  • DOI: https://doi.org/10.1007/s00449-011-0547-6

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