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.

To mitigate the various problems caused by using conventional plastics, bioplastic (BP) has emerged as a substitute for plastics. BP wastes after use are commonly treated using composting, causing many environmental problems. Anaerobic digestion (AD) has become prominent as an alternative method of producing renewable energy. The aim of this study was to estimate the methane production yield (MPY) of BPs (polylactic acid (PLA) and polyhydroxyalkanoate (PHA)) with mechanical pretreatment (particle size < 0.5 cm) and investigate the effect of co-digestion of BPs and food waste (FW). Batch experiments were conducted under mesophilic and thermophilic conditions at various mixing ratios (FW/PLA or PHA = 95:5 and 90:10 on a weight basis). During 20 d of digestion at temperatures of 37 and 55 °C, MPYs of PHA were 153.8–172.0 mL CH₄/g chemical oxygen demand (COD), but that of PLA was significantly low (<25.6 mL CH₄/g COD). Higher MPYs were attained at 55 °C than at 37 °C. The synergistic effects of FW addition on BP AD were observed at both temperatures, especially at 55 °C. By comparing theoretical (based on mono-digestion results) and actual (based on co-digestion results) MPYs, the synergistic effect of FW addition on MPY of co-digestion reached 8.5–26.6% and 12.7–25.5% for PLA- and PHA-fed tests, respectively. The biodegradation rates (on a volatile solids (VS) basis) of PLA and PHA were 6.0–13.7% and 49.1–52.3% and increased by 1.8–4.3 and 1.2–1.5 times in the PLA- and PHA-fed co-digestion tests, respectively. Co-digestion of FW might be a feasible treatment option for BPs combined with simple mechanical pretreatment.

Cured-in-place pipe (CIPP), as a kind of trenchless sewer rehabilitation technology, is a method to repair sewer pipe using unsaturated polyester resin. This study develops a CIPP liner using hot water or steam curing as well as glass fiber, in contrast to traditional methods, which use nonwoven fabric. Composite material samples were fabricated by combining liner materials using various methods, and the structural characteristics of the liners were compared and analyzed through short- and long-term flexural strength tests. A long-term test was conducted for 10,000 h, and the results revealed 13.3 times higher flexural strength and 8 times higher flexural modulus than the American Society for Testing Materials minimum criteria for CIPP short-term properties. The maximum creep retention factor was 0.64, thereby reducing the design thickness of the CIPP by up to 54%. The structural characteristics also improved when glass fibers were mixed with traditional CIPP liner, making it possible to reduce the thickness by 30%. Glass fibers result in high structural strength when combined with unsaturated polyester resin. Structural strength increased, even when glass fibers were mixed with traditional CIPP liner. The main contribution of this research is the development of a high strength CIPP liner and improvement of the structural properties of CIPP lining without using the specially formulated resin or lining materials.

Hydrogen sulfide (H₂S) and ammonia (NH₃), common impurities in biogas, need to be removed before utilizing it. In this study, a combined system, which consisted of an absorption column and an electrochemical oxidation reactor, was tested to simultaneously remove these impurities. The effects of the current density and the chemical loading rate on the system performance were investigated. Firstly, the mass transfer coefficients for the absorption column were determined at various gas flow rates. More mass of NH₃ was transferred, compared with that of H₂S, because of its higher solubility. In the electro-oxidation reactor, reactive chlorine species (RCSs) were generated and oxidized both H₂S and NH₃; however, NH₃ started to degrade only after H₂S was completely eliminated. At a current density of 400 A/m², the current efficiencies of H₂S and NH₃ were 23.1% and 5.9%, respectively. In the combined system, the removal efficiency of H₂S was closely related to the mass ratio of the H₂S transferred and the RCSs generated. The removal efficiency of H₂S was greater than 99% when the ratio was less than 1. The mass transfer potential and the oxidation kinetics should be balanced to improve the system performance for the simultaneous removal of H₂S and NH₃.