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Estimation of chemical oxygen demand by ultraviolet spectroscopic profiling and artificial neural networks

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Abstract

A simple method based on the mathematical treatment of spectral absorbance profiles in conjunction with artificial neural networks (ANNs) is demonstrated for rapidly estimating chemical oxygen demand (COD) values of wastewater samples. In order to improve spectroscopic analysis and ANN training time as well as to reduce the storage space of the trained ANN algorithm, it is necessary to decrease the ANN input vector size by extracting unique characteristics from the raw input pattern. Key features from the spectral absorbance pattern were therefore selected to obtain the spectral absorbance profile, reducing the ANN input vector from 160 to 10 selected inputs. The results indicate that the COD values obtained from the selected absorbance profiles agreed well with those obtained from the entire absorbance pattern. The spectral absorbance profile technique was also compared to COD values estimated by a multiple linear regression (MLR) model to validate whether ANNs were better and more robust models for rapid COD analysis. It was found that the ANN model predicted COD values closer to standard COD values than the MLR model.

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Abbreviations

ANN:

Artificial neural network

COD:

Chemical oxygen demand

BOD:

Biochemical oxygen demand

MLR:

Multiple linear regression

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Acknowledgements

We would like to acknowledge the Australian CRC for Water Quality and Treatment for funding.

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Authors and Affiliations

  1. Centre for Aquatic Processes and Pollution, School of Environmental and Applied Sciences, Faculty of Environmental Science, Gold Coast Campus, Griffith University, PMB 50, Gold Coast Mail Centre, 9726, Gold Coast, QLD, Australia

    Shoshana Fogelman & Huijun Zhao

  2. School of Information and Communication Technology, Faculty of Engineering and Information Technology, Gold Coast Campus, Griffith University, PMB 50, Goal Coast Mail Centre, 9726, Gold Coast, QLD, Australia

    Michael Blumenstein

Authors
  1. Shoshana Fogelman
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  2. Michael Blumenstein
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  3. Huijun Zhao
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Correspondence to Shoshana Fogelman.

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Fogelman, S., Blumenstein, M. & Zhao, H. Estimation of chemical oxygen demand by ultraviolet spectroscopic profiling and artificial neural networks. Neural Comput & Applic 15, 197–203 (2006). https://doi.org/10.1007/s00521-005-0015-9

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  • DOI: https://doi.org/10.1007/s00521-005-0015-9

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