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Quantitating Wastewater Characteristic Parameters Using Neural Network Regression Modeling on Spectral Reflectance

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  • Dhan Lord B. Fortela

    (Energy Institute of Louisiana, University of Louisiana, Lafayette, LA 70504, USA
    Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA)

  • Armani Travis

    (Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA)

  • Ashley P. Mikolajczyk

    (Energy Institute of Louisiana, University of Louisiana, Lafayette, LA 70504, USA
    Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA)

  • Wayne Sharp

    (Energy Institute of Louisiana, University of Louisiana, Lafayette, LA 70504, USA
    Department of Civil Engineering, University of Louisiana, Lafayette, LA 70504, USA)

  • Emmanuel Revellame

    (Energy Institute of Louisiana, University of Louisiana, Lafayette, LA 70504, USA
    Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA)

  • William Holmes

    (Energy Institute of Louisiana, University of Louisiana, Lafayette, LA 70504, USA
    Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA)

  • Rafael Hernandez

    (Energy Institute of Louisiana, University of Louisiana, Lafayette, LA 70504, USA
    Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA)

  • Mark E. Zappi

    (Energy Institute of Louisiana, University of Louisiana, Lafayette, LA 70504, USA
    Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA)

Abstract

Wastewater (WW) analysis is a critical step in various operations, such as the control of a WW treatment facility, and speeding up the analysis of WW quality can significantly improve such operations. This work demonstrates the capability of neural network (NN) regression models to estimate WW characteristic properties such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammonia (NH 3 -N), total dissolved substances (TDS), total alkalinity (TA), and total hardness (TH) by training on WW spectral reflectance in the visible to near-infrared spectrum (400–2000 nm). The dataset contains samples of spectral reflectance intensity, which were the inputs, and the WW parameter levels (BOD, COD, NH 3 -N, TDS, TA, and TH), which were the outputs. Various NN model configurations were evaluated in terms of regression model fitness. The mean-absolute-error (MAE) was used as the metric for training and testing the NN models, and the coefficient of determination (R 2 ) between the model predictions and true values was also computed to measure how well the NN models predict the true values. The highest R 2 (0.994 for training set and 0.973 for testing set) and lowest MAE (0.573 mg/L BOD, 6.258 mg/L COD, 0.369 mg/L NH 3 -N, 6.98 mg/L TDS, 2.586 m/L TA, and 0.014 mmol/L TH) were achieved when NN models were configured for single-variable output compared to multiple-variables output. Hyperparameter grid-search and k-fold cross-validation improved the NN model prediction performance. With online spectral measurements, the trained neural network model can provide non-contact and real-time estimation of WW quality at minimum estimation error.

Suggested Citation

  • Dhan Lord B. Fortela & Armani Travis & Ashley P. Mikolajczyk & Wayne Sharp & Emmanuel Revellame & William Holmes & Rafael Hernandez & Mark E. Zappi, 2023. "Quantitating Wastewater Characteristic Parameters Using Neural Network Regression Modeling on Spectral Reflectance," Clean Technol., MDPI, vol. 5(4), pages 1-17, September.
    • Handle: RePEc:gam:jcltec:v:5:y:2023:i:4:p:59-1202:d:1248588
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    References listed on IDEAS

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    1. Yoonsuh Jung & Jianhua Hu, 2015. "A K -fold averaging cross-validation procedure," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 27(2), pages 167-179, June.
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