Predicting the effluent quality of an industrial wastewater treatment plant by way of optical monitoring

Abstract

Wastewater samples taken from the aeration tank of a full-scale activated sludge plant were analyzed using an automatic optical monitoring device. Five variable selection methods were utilized to find the optimal subsets of input variables to develop predictive models for the important parameters of the wastewater treatment process efficiency and the quality of the effluent, including suspended solids, biochemical oxygen demand, chemical oxygen demand, total nitrogen and total phosphorus. The dependencies between the selected variables were also inspected. The study showed that the models based solely on the optical monitoring variables can be used to predict the level of the effluent quality parameters hours before the traditional sampling and analyses. Thus, predictive modelling based on the optical monitoring variables is a potential tool to be used assistance in a process control, keeping the process in a stable operating condition and avoiding environmental risks and economic losses.

Machine Learning Models for Predicting Water Quality of Treated Fruit and Vegetable Wastewater

“…Many core raw water quality parameters were relevant as expected, which are defined, for example, BOD Raw being the core parameter for the BOD Treated model. The correlations were weak for the TSS Treated model, as the highest correlation was observed to be 0.32 with TS Raw and the lowest was 0.08 with process type, which were similar to the relationships shown in Mjalli et al [29] and Tomperi et al [7] between water quality parameters. These correlations were used as the basis for selecting the different input parameters for the studied models.…”

“…More importantly, it shows which input parameters have a good relationship with the dependent variable. Tomperi et al [7] also used linear correlation analysis to identify variables that impact each other, similarly, BOD and COD have the highest correlation, while other parameters did not show similar results. This is due to the wide variety of wash-waters/wastewater explored in this study compared to the single source, pulp, and paper mill in studies by Tomperi et al [7].…”

“…Tomperi et al [7] also used linear correlation analysis to identify variables that impact each other, similarly, BOD and COD have the highest correlation, while other parameters did not show similar results. This is due to the wide variety of wash-waters/wastewater explored in this study compared to the single source, pulp, and paper mill in studies by Tomperi et al [7]. Treatment was relevant for most water quality parameters; however, for BOD Treated and COD Treated , the process was more relevant, suggesting the process has higher control as compared to the treatment, with respect to treated waters.…”

Improving sludge settleability by introducing an innovative, two-stage settling sequencing batch reactor

“…SVI has been use widely to test sludge settleability in both laboratory scale and pilot plant scale studies [35]. The settleability of activated sludge systems can also be monitored and controlled through microscopic observation [13,36]. Quantitative image analysis is a promising technique which has been used to study different problems in activated sludge systems [2,13,37,38].…”

Modeling, Instrumentation, Automation, and Optimization of Water Resource Recovery Facilities

“…The importance of using a plan for online measurements verification and for online sensor maintenance was discussed by Smith (2017). Tomperi et al (2017) demonstrated that the models based solely on optical monitoring variables were able to be used to predict the level of the effluent quality parameters hours before the traditional sampling and analyses. Optical monitoring variables were deemed a potential tool to be used to maintain stable process control.…”