JOURNAL OF CHILEAN CHEMICAL SOCIETY

Vol 61 No 3 (2016): Journal of the Chilean Chemical Society
Original Research Papers

INTELLIGENT MULTIVARIATE MODEL FOR THE OPTICAL DETECTION OF TOTAL ORGANIC CARBON

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  • Taher Ahmadzadeh Kokya,
  • Naser Mehrdadi,
  • Mojtaba Ardestani,
  • Akbar Baghvand,
  • Arash Kazemi,
  • Aram A. M. Kalhori
Taher Ahmadzadeh Kokya
Department of Environmental Engineering, Faculty of Environment, University of Tehran
Naser Mehrdadi
Department of Environmental Engineering, Faculty of Environment, University of Tehran
Mojtaba Ardestani
Department of Environmental Engineering, Faculty of Environment, University of Tehran
Akbar Baghvand
Department of Environmental Engineering, Faculty of Environment, University of Tehran
Arash Kazemi
Electronics Laboratory, Array Computers Co.
Aram A. M. Kalhori
Global Change Research Group, San Diego State University
Published May 23, 2017
Keywords
  • Total Organic Carbon,
  • Modeling,
  • Artificial Neural Network,
  • UV254,
  • Color,
  • Turbidity
    • ...More
    Less
How to Cite
Kokya, T. A., Mehrdadi, N., Ardestani, M., Baghvand, A., Kazemi, A., & Kalhori, A. A. M. (2017). INTELLIGENT MULTIVARIATE MODEL FOR THE OPTICAL DETECTION OF TOTAL ORGANIC CARBON. Journal of the Chilean Chemical Society, 61(3). Retrieved from https://jcchems.com/index.php/JCCHEMS/article/view/73

Copyright (c) 2017 Taher Ahmadzadeh Kokya, Naser Mehrdadi, Mojtaba Ardestani, Akbar Baghvand, Arash Kazemi, Aram A. M. Kalhori

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Abstract

UV inactivity and fluorescence irradiance of various organic substances are the major drawbacks for a wide applicability of UV based TOC assessment models, especially in drinking water utilities and environmental fields. The adoption of an intelligent model is the key factor to access a reliable and effective detection. The accurate training of the artificial neural network model and backward elimination of less significant parameters, conferred more predictive properties to TOC detection. This led to an efficient optimal TOC detection model based on turbidity, UV254 absorbance and true color. The validation of model performance was investigated through application of untrained scenarios. The outcome of the validation analysis showed a correlation coefficient of 0.87 and root mean square error of 0.48 while the training performance of the model showed 0.95 and 0.33 respectively. The results indicated that the trained ANN model was efficiently capable for TOC detection in water resources based on the main drivers. 

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