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

Dhan Lord B. Fortela (), Armani Travis, Ashley P. Mikolajczyk, Wayne Sharp, Emmanuel Revellame, William Holmes, Rafael Hernandez and Mark E. Zappi
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Dhan Lord B. Fortela: Energy Institute of Louisiana, 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
Wayne Sharp: Energy Institute of Louisiana, University of Louisiana, Lafayette, LA 70504, USA
Emmanuel Revellame: Energy Institute of Louisiana, University of Louisiana, Lafayette, LA 70504, USA
William Holmes: Energy Institute of Louisiana, University of Louisiana, Lafayette, LA 70504, USA
Rafael Hernandez: Energy Institute of Louisiana, University of Louisiana, Lafayette, LA 70504, USA
Mark E. Zappi: Energy Institute of Louisiana, University of Louisiana, Lafayette, LA 70504, USA

Clean Technol., 2023, vol. 5, issue 4, 1-17

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.

Keywords: neural network regression; wastewater quality; spectral reflectance (search for similar items in EconPapers)
JEL-codes: Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2023
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