Quantitative monitoring of an activated sludge reactor using on-line UV-visible and near-infrared spectroscopy

Abstract

The performance of an activated sludge reactor can be significantly enhanced through use of continuous and real-time process-state monitoring, which avoids the need to sample for off-line analysis and to use chemicals. Despite the complexity associated with wastewater treatment systems, spectroscopic methods coupled with chemometric tools have been shown to be powerful tools for bioprocess monitoring and control. Once implemented and optimized, these methods are fast, nondestructive, user friendly, and most importantly, they can be implemented in situ, permitting rapid inference of the process state at any moment. In this work, UV-visible and NIR spectroscopy were used to monitor an activated sludge reactor using in situ immersion probes connected to the respective analyzers by optical fibers. During the monitoring period, disturbances to the biological system were induced to test the ability of each spectroscopic method to detect the changes in the system. Calibration models based on partial least squares (PLS) regression were developed for three key process parameters, namely chemical oxygen demand (COD), nitrate concentration (N-NO(3)(-)), and total suspended solids (TSS). For NIR, the best results were achieved for TSS, with a relative error of 14.1% and a correlation coefficient of 0.91. The UV-visible technique gave similar results for the three parameters: an error of approximately 25% and correlation coefficients of approximately 0.82 for COD and TSS and 0.87 for N-NO(3)(-) . The results obtained demonstrate that both techniques are suitable for consideration as alternative methods for monitoring and controlling wastewater treatment processes, presenting clear advantages when compared with the reference methods for wastewater treatment process qualification.

Monitoring Urban Wastewaters’ Characteristics by Visible and Short Wave Near-Infrared Spectroscopy

“…The PLSR modeling approach allowed the development of a full cross-validation scheme with a moderate number of samples and large number of relative transmittance records (i.e., 600 records for the full spectrum). The number of samples in this study (60 cross-validation + 20 prediction) was in accordance to previous studies such as Thomas et al [5] that used 86 samples, Reynolds and Ahmad [6] analyzing up to 54 samples from the same wastewater treatment plant, or even higher than other studies than employed around 40 samples [8,9].…”

“…They also noted that the addition of covariates, like the turbidity information, did not significantly improve the accuracy of the artificial neural network. Sarraguça et al [9] monitored COD of an activated sludge reactor with an UV-visible spectrometer with the spectral range 230-700 nm and a NIR spectrometer with the spectral range 900-1700 nm. They also employed PLSR analysis and reported a low correlation coefficient (0.28) with the NIR system and comparable correlation coefficient results (0.82) for the UV-visible system.…”

“…Thomas et al [5] reported a very similar correlation coefficient (R 2 = 0.87) for an UV system. Sarraguça et al [9] also quantified TSS with their dual UV-visible and NIR spectrometers systems. They reported a good correlation coefficient (0.82) for the UV-visible system and even better for the NIR system (0.92).…”

“…In this sense, the application of UV spectroscopy for monitoring wastewater parameters has been greatly improved and new statistical methods, such as neural networks [8], are currently being researched. Moreover, the application of VNIR spectroscopy for wastewater quality is an area of current research with great challenges ahead but with a great potential for on-line monitoring of wastewater parameters [9,10].…”

Window Subtracted Wave Band Selection Method for the Ftir/Atr Spectrum Analysis

“…Using FTIR/ATR technology to directly measure blood components needs to overcome many difficulties. Prediction accuracy by the spectra of various components in the blood content has not reached the level of clinical application [10][11][12][13][14][15]. Spectral modeling optimization of human blood spectrum is one of the most important research fields.…”