Real-time monitoring can enhance the performance of biological wastewater treatment processes by preventing incidents that can lead to the imbalance of the system and eventually to the total loss of biological activity. For this purpose, in-situ monitoring techniques should not require sample pre-treatment and chemicals addition. Nowadays automation is still limited by poor sensor performance and high maintenance costs. Hence, further investigation is required in order to achieve new developments in monitoring techniques. Spectroscopic methods together with chemometrics are being presented as a powerful tool for process monitoring and control, since they can be fast, non-destructive and without the use of chemicals. In this work, UV-Visible and Near-Infrared (NIR) spectroscopy were used to monitor an activated sludge process using immersion probes connected to the respective spectrophotometers through optical fibbers. During two monitoring periods changes were induced to the system to test the ability of both probes in detecting them. While UV-Visible spectroscopy showed to be suitable for on-line monitoring, by detecting chemical oxygen demand (COD) variations in the effluent and identifying different nitrification status, NIR range also demonstrated potentialities, however, due to several experimental constrains, the results were not conclusive. Partial least squares (PLS) regression was performed for the prediction of COD, nitrate and total suspended solids (TSS) concentrations in the effluent using immersible UV-Visible probe and off-line spectra acquisition. The best results were obtained for the in-situ technique. The root mean squared error of cross validation (RMSECV) obtained for the estimative of each parameter was 15.4 mg O2/L for COD, 19.0 mg N-NO3 -/L for nitrate and 35.3 mg/L for TSS. In-situ UV-Visible range proved to be valuable for the monitoring and control of biological wastewater treatment processes, although some improvements identified in this work are still needed to overcome its limitations.
|Qualification||Master of Science|
|Award date||19 Dec 2018|
|Publication status||Published - 19 Dec 2008|