Modelling and Incorporating the Variable Demand Patterns to the Calibration of Water Distribution System Hydraulic Model

Adaptive urban drinking water supply model using the effect of node elevation and head loss formula: A case study

Along with population growth and health improvement, water demand due to urbanization is increasing and creating a need to develop a strategy for handling water supply networks (WSNs). In the last decade, software modeling of WSNs has been developed to evaluate the state of networks in terms of pressure control, leakage analysis, and overall demand determination. In the case of very complex and extremely large networks, it is very difficult to manage the water supply. Metropolitan Waterworks Authority (MWA) in Thailand has to supply drinking water to the three densely populated cities; Bangkok, Nonthaburi, and Samut Prakan, that cover an area of 2944.05 km2. Hence, MWA has developed a main pipe model using EPANET software as a managing tool. This tool can offer a good solution for the water supply, but there is approximately a 14 percent error, mainly due to not having the elevation data of the pipe network. The current research is based on demand and pressure modeling analysis with utilizing two important parameters, node elevations, and head loss. The first trial model was an initial revision of the node elevation based on a road surface map. It was found that the model with elevation data could offer a better solution and was 3.95% more accurate than the existing model. The result was significantly improved, but another error, which may have been caused by using an inappropriate head loss model, was found. As the introduced model is based on the Hazen-William model, it cannot offer an accurate solution for all Reynolds number ranges. Even though Darcy-Weisbach is more complex to use, it could provide a better solution. The results indicate the Darcy-Weisbach model produces results that are 8.65% more accurate than the Hazen-William model.

Development and Comparison of Water Quality Network Model and Data Analytics Model for Monochloramine Decay Prediction

The conventional drinking water treatment process involves disinfecting water at the final stage of treatment to ensure water is microbiologically safe at customer taps. Monochloramine is a popular disinfectant used in many water distribution systems (WDSs) worldwide. Understanding the factors that impact monochloramine decay in the WDS is critical for maintaining disinfection at the customer tap. While monochloramine residue moves through a WDS, it decays via several pathways including chemical, microbiological, and wall decay processes. The decay profile in these pathways is often site-specific and depends on various factors including treated water characteristics. In a water quality network model, the decay of a chemical species is often modelled using two parameters that represent bulk and wall decay kinetics. Typical bulk decay characteristics of monochloramine for a specific WDS can be easily established in the laboratory using grab sample tests, while in a real situation, wall decay is difficult to quantify. In this study, we compared two different approaches to model monochloramine decay in a WDS. In the first approach, the wall decay parameter was quantified using a parameter optimisation technique with monochloramine concentrations at different network locations simulated using a water quality network model. In the second approach, a data analytics model was developed using a machine learning algorithm. For both approaches, the model predicted monochloramine concentrations closely matched the observed data. Our study suggests that the data analytics model has a relatively higher accuracy in predicting monochloramine residual concentrations in a WDS.

Electronic Technology for Wastewater Treatment and Clean Water Production

Water is essential for society. Due to excellent distribution systems for clean drinking water and wastewater, safe and reliable water transport is guaranteed. However, due to ageing network conditions, there is a need for extensive network monitoring and replacement strategies. There is a high demand for good insight into water mains and water distribution systems. A promising way to monitor our water transport involves various types of novel sensors, including strategies for the smart placement of these sensors, maximizing performance while minimizing costs. Furthermore, processing the increasingly large amount of sensor data can be done using Artificial Intelligence and sensor fusion techniques, yielding vastly increased information about the distribution mains.

Development of an Optical Method to Monitor Nitrification in Drinking Water

Nitrification is a common issue observed in chloraminated drinking water distribution systems, resulting in the undesirable loss of monochloramine (NH₂Cl) residual. The decay of monochloramine releases ammonia (NH₃), which is converted to nitrite (NO₂^-) and nitrate (NO₃^-) through a biological oxidation process. During the course of monochloramine decay and the production of nitrite and nitrate, the spectral fingerprint is observed to change within the wavelength region sensitive to these species. In addition, chloraminated drinking water will contain natural organic matter (NOM), which also has a spectral fingerprint. To assess the nitrification status, the combined nitrate and nitrite absorbance fingerprint was isolated from the total spectra. A novel method is proposed here to isolate their spectra and estimate their combined concentration. The spectral fingerprint of pure monochloramine solution at different concentrations indicated that the absorbance difference between two concentrations at a specific wavelength can be related to other wavelengths by a linear function. It is assumed that the absorbance reduction in drinking water spectra due to monochloramine decay will follow a similar pattern as in ultrapure water. Based on this criteria, combined nitrate and nitrite spectra were isolated from the total spectrum. A machine learning model was developed using the support vector regression (SVR) algorithm to relate the spectral features of pure nitrate and nitrite with their concentrations. The model was used to predict the combined nitrate and nitrite concentration for a number of test samples. Out of these samples, the nitrified sample showed an increasing trend of combined nitrate and nitrite productions. The predicted values were matched with the observed concentrations, and the level of precision by the method was ± 0.01 mg-N L^-1. This method can be implemented in chloraminated distribution systems to monitor and manage nitrification.