Reliable and effective continuous water quality monitoring has always been challenging. To detect water quality, deployment of multiple sensor nodes in a water distribution network generates complex and convoluted data sets. This paper demonstrates the implementation of a cyber-physical system along with soft-computing approaches (Python and fuzzy). The designed system monitors water quality in real time, simplifies the complexity of sensor data and assists water engineers in decision making.
This paper presents a simple methodology for assessing the supplying capacity of demand nodes under pressure-deficient conditions by assigning the respective emitter coefficient only to those nodes facing the pressure-deficit condition. Though the proposed approach follows an iterative methodology using EPANET software, the computational burden of adding artificial elements to the other methods is avoided and is hence useful for analyzing large networks.
Public drinking water distribution systems can be contaminated. Sensors designed to detect contaminants can provide warning through the use of a contamination warning system (CWS). A properly designed CWS may help reduce the consequences associated with contamination events. Various factors can affect the performance of a CWS design, our paper focuses on the accuracy with which the network model of a distribution system represents the actual structural details of the water distribution network.
Many waterworks utilize sand filters that rely on microorganisms for water treatment. When new, these biofilters require several months for the growth of microorganisms before they become functional. This study is the most thorough documentation of this start-up process to date. Results show that the onset of removal of individual substances proceeds in a well-defined order and that the different filter depths have different functions, suggesting which activities may shorten the start-up period.
This work is an outgrowth of engineering service learning at Villanova University in Pennsylvania, USA. Teams of students assess and collect data on site, and design and communicate information for clean water networks that benefit developing areas around the world. The design of a water network requires the selection of pipe diameters that satisfy pressure and flow requirements while minimizing cost. This work contrasts and compares results of several models and makes key recommendations.
The present work focuses on the photocatalytic degradation of methyl orange (MO) on erbium trioxide nanoparticles (Er2O3 NPs). The results revealed that the photocatalytic activity of the prepared Er2O3 NPs towards methyl orange photodegradation was manifested.
This paper concerns the extension and tuning of a genetic algorithm used for the automated design of optimal drinking water distribution networks. Different settings and extensions are tested for their effect on the speed and reproducibility with which the algorithm can produce good results. The fastest combinations are reported. Speed and reproducibility are key conditions for drinking water utilities to include the use of optimization algorithms in the regular design process of mains.
Modeling can be used to overcome water jar test limitations. In this research study, MLP-type ANNs with one hidden layer have been used for modeling jar tests to determine the dosage level of coagulant used in water treatment processes. The data contained in this research have been obtained from the drinking water treatment plant located in the Ardabil province in Iran. To evaluate the performance of the model, the mean square error and the correlation coefficient parameters have been used.
Significant drinking water contamination events pose a serious threat to public and environmental health. This paper examines the development of Water Expert, a software tool for assisting decision makers in response and recovery following a contamination event. The decision making process undertaken during contamination events in evaluated. Results indicate that the decision making process is limited without technological intervention.
Drinking-water quality monitoring is essential before consumption, as the available water is contaminated and can cause illness in an individual. The traditional methods for water quality monitoring require sample collection at different sites and a subsequent laboratory test which is labor- and cost-intensive. To, overcome this problem, a real-time drinking water quality measurement platform is designed which can provide on-site efficient water quality monitoring.
Household Water Treatment and safe Storage (HWTS) systems aim to provide safe drinking
water in an affordable manner. The effectiveness of these systems to remove pathogens is crucial for the health of its users. Different researches report wide ranges of effectiveness for each of the three selected systems: SODIS, ceramic and biosand filters. The resources available for the prices of HWTS also report wide ranges. In the data available no relation was observed between price and effectiveness.
EPANET does not always conserve constituent mass during water-quality simulations. The failure to conserve mass can result in significant errors in constituent concentrations. We document the occurrence of mass imbalances, explain why they occur, provide recommendations for minimizing mass imbalances, and present a preliminary water-quality algorithm for use in EPANET that always conserves mass. Our paper should be of interest to anyone who performs water-quality simulations using EPANET.