Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 18 July 2023 / Approved: 19 July 2023 / Online: 20 July 2023 (10:44:00 CEST)

A peer-reviewed article of this Preprint also exists.

Fortela, D.L.B.; Travis, A.; Mikolajczyk, A.P.; Sharp, W.; Revellame, E.; Holmes, W.; Hernandez, R.; Zappi, M.E. Quantitating Wastewater Characteristic Parameters Using Neural Network Regression Modeling on Spectral Reflectance. Clean Technol. 2023, 5, 1186-1202. Fortela, D.L.B.; Travis, A.; Mikolajczyk, A.P.; Sharp, W.; Revellame, E.; Holmes, W.; Hernandez, R.; Zappi, M.E. Quantitating Wastewater Characteristic Parameters Using Neural Network Regression Modeling on Spectral Reflectance. Clean Technol. 2023, 5, 1186-1202.

Abstract

Wastewater (WW) analysis is a critical step in various operations such as 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 (NH3-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 (400nm-2000nm). The dataset contains samples of spectral reflectance intensity, which were the inputs, and the WW parameter levels (BOD, COD, NH3-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 (R2) between the model predictions and true values was also computed to measure how well the NN models predict the true values. 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

Subject

Engineering, Chemical Engineering

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