Generally, the amount of wastewater in sewerage pipes is measured using sensor-based devices such as submerged area velocity flow meters or non-contact flow meters. However, these flow meters do not provide accurate measurements because of impurities, corrosion, and measurement instability due to high turbidity. However, cameras have advantages such as their low cost, easy service, and convenient operation compared to the sensors. Therefore, in this study, we examined the following three methods for measuring the flow rate by capturing images inside of a sewer pipe using a camera and analyzing the images to calculate the water level: direct visual inspection and recording, image processing, and deep learning. The MATLAB image processing toolbox was used for analysis. The image processing found the boundary line by adjusting the contrast of the image or removing noise; a network to find the boundary line between wastewater and sewer pipe was created after training the image segmentation results and placing them into three categories using deep learning. From the recognized water levels, geometrical features were used to identify the boundary lines, and flow velocities and flow rates were calculated from Manning’s equation. Using direct inspection and image-processing techniques, boundary lines in images were detected at rates of 12% and 53%, respectively. Although the deep-learning model required training, it demonstrated 100% water-level detection, thereby proving to be the most advantageous method. Moreover, there is enough potential to increase the accuracy of deep learning, and it can be a possible replacement for existing flow measurement sensors.
The odor emitted from a wastewater treatment plant (WWTP) is an important environmental problem. An estimation of odor emission rate is difficult to detect and quantify. To address this, various approaches including the development of emission factors and measurement using a closed chamber have been employed. However, the evaluation of odor emission involves huge manpower, time, and cost. An artificial neural network (ANN) is recognized as an efficient method to find correlations between nonlinear data and prediction of future data based on these correlations. Due to its usefulness, ANN is used to solve complicated problems in various disciplines of sciences and engineering. In this study, a method to predict the odor concentration in a WWTP using ANN was developed. The odor concentration emitted from a WWTP was predicted by the ANN based on water quality data such as biological oxygen demand, dissolved oxygen, and pH. The water quality and odor concentration data from the WWTP were measured seasonally in spring, summer, and autumn and these were used as input variations to the ANN model. The odor predicted by the ANN model was compared with the measured data and the prediction accuracy was estimated. Suggestions for improving prediction accuracy are presented.
Drinking water production facilities are designed to filter contaminants that are ever-present in raw water. These facilities, however, pose risks of tap water contamination or water supply discontinuation in the event of a massive chemical spill. A managed aquifer recharge (MAR) offers the advantage of purifying surface water as well as maintaining water underground for extended periods of time, thus securing sufficient time for a response to contaminant infiltration and dramatically increasing consumer safety. However, contaminated aquifers are difficult to recover; accordingly, it is important that MAR sites engage in preemptive responses to chemical spills in order to protect their aquifers. This study assesses potential risks in order to quantify the detrimental impacts of chemical spills in cities located in river basins on drinking water supply facilities. The targets of analysis are two MAR sites in South Korea. The potential risk analysis offers grounds upon which aggressive basin management can be implemented to ensure water supply facility operation safety. The lack of data for available for analysis is addressed using a stochastic methodology that ranks cities in which MAR sites are endangered based on the cities' potential risk probability distributions. The results of the analysis show that water supply facilities surrounded by larger cities have relatively higher potential risks, and would, therefore, need to handle more management targets to prevent chemical spills. Furthermore, the proposed methodology contributes not only to potential risk management of existing water supply facilities, but also to MAR site selection.
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