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.

Most urban areas in the world have water infrastructure systems, including the buried sewer and water pipelines, which are assessed as in need of extensive rehabilitation. Deterioration by many other factors affects structural integrity. Trenchless technologies such as Cured-in-Place Pipe (CIPP) are now applied in numerous projects while minimizing disturbance in an urban environment. The main purpose of this study is to develop a high strength CIPP material using various composite materials (e.g., glass fiber, carbon fiber, polyester felt, unsaturated polyester resin, and others). Composite samples were made of the materials and tested using three-point bend apparatus to find mechanical properties, which include the flexural modulus, strength, and deflection. A composite combination with glass fibers with thin felt layers shows the best results in mechanical properties. Flexural modulus is a key factor for CIPP liner thickness design. Glass fiber composite yields between four and nine times higher values than the minimum value specified in the American Society for Testing and Materials (ASTM) F1216. This study provides a fundamental baseline for high strength CIPP liners that are capable of using conventional curing technologies.

This study examined the optimum coagulation conditions for reducing irreversible fouling during the in-line coagulation/ultrafiltration (UF) membrane process and assessed the decrease in operating cost. The coagulation conditions that generated charge-neutralization, sweep-flocculation, and under-dosing mechanisms were obtained by a jar-test, and a pilot-scale in-line coagulation/UF membrane process was operated under the coagulation conditions. Charge-neutralization and sweep-flocculation mechanisms reduced irreversible fouling effectively, and the under-dosing mechanism was able to reduce irreversible fouling only when flocs of a certain size or larger were formed. This revealed that floc size was a more important factor in reducing irreversible fouling than floc structure, and once initial cake layers were created by flocs of a fixed size, the structure of formed cake layers had only a minor effect on irreversible fouling. Regarding reduction in operating cost, 0.5 mg/L and 3 h, which were necessary to produce an under-dosing mechanism, were deemed the optimum coagulant dosage and coagulant injection time, respectively, to reduce irreversible fouling. In order to analyze the operating cost reduction effect, a pilot plant was operated under optimum operating conditions, and the total operating cost was approximately 11.2% lower than without in-line coagulation.

The purpose of this study was to evaluate the cost-benefit of on-site food waste recycling system using Life-Cycle Cost analysis, and to compare with large-scale treatment system. For accurate evaluation, the cost-benefit analysis was conducted with respect to local governments and residents, and qualitative environmental improvement effects were quantified. As for the local governments, analysis results showed that, when large-scale treatment system was replaced with on-site recycling system, there was significant cost reduction from the initial stage depending on reduction of investment, maintenance, and food wastewater treatment costs. As for the residents, it was found that the cost incurred from using the on-site recycling system was larger than the cost of using large-scale treatment system due to the cost of producing and installing the on-site treatment facilities at the initial stage. However, analysis showed that with continuous benefits such as greenhouse gas emission reduction, compost utilization, and food wastewater reduction, cost reduction would be obtained after 6 years of operating the on-site recycling system. Therefore, it was recommended for local governments and residents to consider introducing an on-site food waste recycling system if they are to replace an old treatment system or need to establish a new one.

The slope of sewer pipes is a major factor for transporting sewage at designed flow rates. However, the gradient inside the sewer pipe changes locally for various reasons after construction. This causes flow disturbances requiring investigation and appropriate maintenance. This study extracted the internal elevation fluctuation from closed-circuit television investigation footage, which is required for sanitary sewers. The principle that a change in water level in sewer pipes indirectly indicates a change in elevation was applied. The sewage area was detected using a convolutional neural network, a type of deep learning technique, and the water level was calculated using the geometric principles of circles and proportions. The training accuracy was 98%, and the water level accuracy compared to random sampling was 90.4%. Lateral connections, joints, and outliers were removed, and a smoothing method was applied to reduce data fluctuations. Because the target sewer pipes are 2.5 m concrete reinforced pipes, the joint elevation was determined every 2.5 m so that the internal slope of the sewer pipe would consist of 2.5 m linear slopes. The investigative method proposed in this study is effective with high economic feasibility and sufficient accuracy compared to the existing sensor-based methods of internal gradient investigation.