Comparisons of grey and neural network prediction of industrial park wastewater effluent using influent quality and online monitoring parameters

T. Y. Pai, S. H. Chuang, T. J. Wan, H. M. Lo, Y. P. Tsai, H. C. Su, L. F. Yu, H. C. Hu, P. J. Sung

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

In this study, Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids (SS eff) and chemical oxygen demand (COD eff) in the effluent from a wastewater treatment plant in industrial park of Taiwan. When constructing model or predicting, the influent quality or online monitoring parameters were adopted as the input variables. ANN was also adopted for comparison. The results indicated that the minimum MAPEs of 16.13 and 9.85% for SS eff and COD eff could be achieved using GMs when online monitoring parameters were taken as the input variables. Although a good fitness could be achieved using ANN, they required a large quantity of data. Contrarily, GM only required a small amount of data (at least four data) and the prediction results were even better than those of ANN. Therefore, GM could be applied successfully in predicting effluent when the information was not sufficient. The results also indicated that these simple online monitoring parameters could be applied on prediction of effluent quality well.

Original languageEnglish
Pages (from-to)51-66
Number of pages16
JournalEnvironmental Monitoring and Assessment
Volume146
Issue number1-3
DOIs
StatePublished - 2008

Keywords

  • Artificial neural network
  • Conventional activated sludge process
  • Grey model
  • Industrial park
  • Wastewater treatment plant

Fingerprint

Dive into the research topics of 'Comparisons of grey and neural network prediction of industrial park wastewater effluent using influent quality and online monitoring parameters'. Together they form a unique fingerprint.

Cite this