21
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Artificial neural network modelling of a large-scale wastewater treatment plant operation.

      Bioprocess and Biosystems Engineering
      Algorithms, Cities, Computer Simulation, Computers, Equipment Design, Models, Theoretical, Neural Networks (Computer), Reproducibility of Results, Time Factors, Turkey, Waste Disposal, Fluid, methods, Water Pollutants, Chemical, analysis, Water Purification

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Artificial Neural Networks (ANNs), a method of artificial intelligence method, provide effective predictive models for complex processes. Three independent ANN models trained with back-propagation algorithm were developed to predict effluent chemical oxygen demand (COD), suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. The appropriate architecture of ANN models was determined through several steps of training and testing of the models. ANN models yielded satisfactory predictions. Results of the root mean square error, mean absolute error and mean absolute percentage error were 3.23, 2.41 mg/L and 5.03% for COD; 1.59, 1.21 mg/L and 17.10% for SS; 52.51, 44.91 mg/L and 3.77% for MLSS, respectively, indicating that the developed model could be efficiently used. The results overall also confirm that ANN modelling approach may have a great implementation potential for simulation, precise performance prediction and process control of wastewater treatment plants.

          Related collections

          Author and article information

          Comments

          Comment on this article