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K L, Priya [Author]; Nakhate, Dr. Vidya [Author]; Joshi, Anupama [Author]; T.S, Athira [Author]; Krishnan, Gopika [Author]; Bhatt, Aparna [Author]; Gokul, T. G. [Author]; Indu, M. S. [Author]; Haddout, S [Author]; Azhikodan, Gubash [Author]; Baiju, V [Author]; aaa, aa [Author]

Predicting Turbidity and Evaluation of Clariflocculation Process Through a Coupled Approach of Micro-Scale Investigations and Ann Modelling

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  • Media type: E-Book
  • Title: Predicting Turbidity and Evaluation of Clariflocculation Process Through a Coupled Approach of Micro-Scale Investigations and Ann Modelling
  • Contributor: K L, Priya [Author]; Nakhate, Dr. Vidya [Author]; Joshi, Anupama [Author]; T.S, Athira [Author]; Krishnan, Gopika [Author]; Bhatt, Aparna [Author]; Gokul, T. G. [Author]; Indu, M. S. [Author]; Haddout, S [Author]; Azhikodan, Gubash [Author]; Baiju, V [Author]; aaa, aa [Author]
  • Published: [S.l.]: SSRN, 2023
  • Extent: 1 Online-Ressource (33 p)
  • Language: English
  • DOI: 10.2139/ssrn.4388259
  • Identifier:
  • Keywords: Turbidity ; coagulation ; floc characteristics ; settling time ; ANN model
  • Origination:
  • Footnote:
  • Description: The present study investigated the role of settling time on the final turbidity of water though a systematic way by coupling jar test experiments, micro-scale investigations and modelling for Polyalumunium Chloride and Moringa Oleifera coagulants. The floc characteristics and their rate of change during settling was studied from which the mechanism of coagulation was delineated along with the floc dynamics. The study enlightened that the rate of settling of flocs is not uniform over the settling period and has to be considered while predicting final turbidity. To validate the hypothesis, linear regression model, non-linear regression model and ANN model were developed for two scenarios: Scenario I considering initial turbidity, pH and coagulant dose for the prediction of final turbidity; Scenario II considered initial turbidity, pH, coagulant dose and settling time for predicting final turbidity. The results suggest that the turbidity removal and hence the final water turbidity has a non-linear behaviour with settling time and the incorporation of settling time in the model enhanced the predictability of the model. ANN model showed the best performance over the regression models in predicting final turbidity with an R2 value of 0.99 against 0.89 and 0.82 for linear and non-linear regression models respectively for polyaluminium chloride while these were 0.87 and 0.77 respectively for Moringa Oleifera. The ANN model for scenario II was less sensitive to fluctuations in pH, initial turbidity and dosage compared to scenario I for both the coagulants
  • Access State: Open Access