A software sensor for in-situ monitoring of the 5-day biochemical oxygen demand

Authors

  • Rana Kasem
  • Dimah ALabdeh
  • Roohollah Noori University of Tehran
  • Abdulreza Karbassi

DOI:

https://doi.org/10.17794/rgn.2018.1.3

Abstract

Due to the time-consuming procedure for determining the 5-day biochemical oxygen demand (BOD5), the present study developed two software sensors based on artificial intelligence techniques to estimate this indicator instantaneously. For this purpose, feed-forward and radial basis function neural networks (FFANN and RBFANN, respectively) were tuned to estimate the maximum values of BOD5 (BOD5(max)) as a function of average, maximum and minimum dissolved oxygen in the Sefidrood River. Also, Levenberg-Marquardt (LM), resilient back propagation (RP), and scaled conjugate gradient (SCG) algorithms were used to optimize the FFANN parameters. The results demonstrated that the performance of LM algorithm in tuning the FFANN was better than others, in verification step. Besides, the performance of both FFANN and RBFANN models for prediction of the BOD5(max) was approximately the same.

Author Biography

Roohollah Noori, University of Tehran

Department of Environmental Engineeirng, Graduate Faculty of Environment

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Published

2017-12-16

How to Cite

Kasem, R., ALabdeh, D., Noori, R., & Karbassi, A. (2017). A software sensor for in-situ monitoring of the 5-day biochemical oxygen demand. Rudarsko-geološko-Naftni Zbornik, 33(1), 15–23. https://doi.org/10.17794/rgn.2018.1.3

Issue

Vol. 33 No. 1 (2018): No. 39

Section

Geology

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