Evaluation of genetic models for COD and TSS estimation in wastewater through its spectrophotometric response

Towards highly economical and accurate wastewater sensors by reduced parts of the LED-visible spectrum

Interest is growing in simple, fast and inexpensive systems to analyze urban wastewater quality in real time. In this research project, a methodology is presented for the characterization of COD, BOD₅, TSS, TN, and TP of wastewater samples, without the need to alter the samples or use chemical reagents, from a few wavelengths, belonging to the different color groups that compose the visible spectrum in isolation: (380-700 nm): violet (380-427 nm), blue (427-476 nm), cyan (476-497 nm), green (497-570 nm), yellow (570-581 nm), orange (581-618 nm), and red (618-700 nm). In this study, about 650 raw and treated urban wastewater samples from over 43 WWTPs and a total of 36 estimation models based on genetic algorithms have been calculated. Seven models were calculated for each pollutant parameter; one model for each color group of the visible spectrum, except for TN, which includes an additional model combining the wavelengths of the violet and red region of the spectrum. All the calculated models showed high accuracy, with an R² between 80 and 85 % for COD, BOD₅ and TSS, and 66-74 % for TN and TP. The tests carried out have shown the accuracy of the models of the different color groups to be very close to each other. However, it is noted that the models making use of the wavelengths between 497 and 570 nm (green) were the ones that showed the best performance in all the parameters under study. This research work lays the foundations for the development of cheaper, faster, and simpler wastewater monitoring and characterization equipment.

A method based on a one-dimensional convolutional neural network for UV-vis spectrometric quantification of nitrate and COD in water under random turbidity disturbance scenario

This paper proposed a novel spectrometric quantification method for nitrate and COD concentration in water using a double-channel 1-D convolution neural network for relatively long UV-vis absorption spectra data (2600 points). To improve the model's ability to resist turbidity disturbance, a new dataset augmentation method was applied and the absorption spectra of nitrate and COD under different turbidity disturbances were successfully simulated. Compared to the PLSR model, the value of RRMSEP for the CNN model was reduced from 6.1% to 1.4% in nitrate solution and 4.5% to 1.3% in COD solution. Compared to the PLSR model, the regression accuracy of the CNN model was increased from 56% to 93% in nitrate solution and 68% to 91% in COD solution. The test on the actual solution under different turbidity disturbances shows that the 1D-CNN model had a bias rate of less than 2% in both nitrate and COD solutions, while the worst bias rate in the PLSR method was 15%.

Predictive modeling of the primary settling tanks based on artificial neural networks for estimating TSS and COD as typical effluent parameters

A predictive model based on artificial neural networks (ANNs) for modeling primary settling tanks' (PSTs) behavior in wastewater treatment plants was developed in this study. Two separate ANNs were built using input data, raw wastewater characteristics, and operating conditions. The output data from the ANNs consisted of the total suspended solids (TSS) concentration and chemical oxygen demand (COD) as predictions of PSTs' typical effluent parameters. Data from a large-scale wastewater treatment plant was used to illustrate the applicability of the predictive model proposal. The ANNs model showed a high prediction accuracy during the training phase. Comparisons with available empirical and statistical models suggested that the ANNs model provides accurate estimations. Also, the ANNs were tested using new experimental data to verify their reproducibility under actual operating conditions. The predicted values were calculated with satisfactory results, having an average absolute deviation of <20%. The model could be adapted to any large-scale wastewater plant to monitor and control the operation of primary settling tanks, taking advantage of the ANNs' learning capacity.