Simulated Water Distribution System Research Articles

Optimized Deep Learning Framework for Water Distribution Data-Driven Modeling

Deep Learning (DL), unlike conventional Artificial Neural network (ANN), is capable of self-learning data features layer by layer in unsupervised manner and creating a data-driven model with the given dataset. DL has been widely applied to big data analytics, graphics object detection, classification, voice recognition and many other problems. This paper presents an integrated data-driven modelling framework that couples DL with the well-developed evolutionary optimization tool in a scalable and heterogeneous high performance computing paradigm. The integrated framework enables modellers to effectively and efficiently construct a model with a given dataset. It is demonstrated that the framework has wide applicability including but not limited to the simulation, optimization and operation decision of water distribution systems. The paper elaborates the development of the deep learning framework with potential applications of facilitating the data fusion, system simulation and predictive analysis, anomaly detection from the time series data (pressures, flows and consumptions etc.), water usage prediction, construction of a meta-model as a surrogate to the physics-based models (hydraulic and water quality) for water distribution management.

Water Quality Based Multi-objective Optimal Design of Water Distribution Systems

Water distribution systems (WDSs) are one of the vital infrastructures in urban areas. The common objective considered in WDSs design is providing required water with the minimum construction cost. Less attention is paid to “water quality” as an objective in optimal design of WDSs. The aim of this paper is to include a water quality based objective in WDS design alongside other common objectives. For this purpose, the water quality reliability index developed based on “chlorine residual” and “water age”, is used. EPANET2.0 is applied for WDS simulation and ACO (Ant-Colony-Optimization) has been used as the optimization algorithm. Head-Driven-Simulation-Method (HDSM) is also considered. The proposed model is applied to Jahrom (a city in the south of Iran) WDS. The outputs show that the proposed model would result in less construction costs in comparison to the original design and 4–10 % of construction cost can be reduced depending on the considered objectives and selected optimal solution by managers and decision makers while the resulted solutions include high water quality reliability (ranging from 93 to 99 %). Also using HDSM method for hydraulic analysis of WDS instead of DDSM can lead to solutions with less construction cost (U$10,000 in average) and acceptable water quality reliability.

Draft Genome Sequence of Two Sphingopyxis sp. Strains, Dominant Members of the Bacterial Community Associated with a Drinking Water Distribution System Simulator.

We report the draft genomes of two Sphingopyxis sp. strains isolated from a chloraminated drinking water distribution system simulator. Both strains are ubiquitous residents and early colonizers of water distribution systems. Genomic annotation identified a class 1 integron (intI1) gene associated with sulfonamide (sul1) and puromycin (pac) antibiotic resistance genes.

Whole-Genome Sequences of Four Strains Closely Related to Members of the Mycobacterium chelonae Group, Isolated from Biofilms in a Drinking Water Distribution System Simulator.

We report here the draft genome sequences of four Mycobacterium chelonae strains from biofilms subjected to a “chlorine burn” in a chloraminated drinking water distribution system simulator. These opportunistic pathogens have been detected in hospital and municipal water distribution systems, in which biofilms have been recognized as an important factor for their persistence.

Improving the performance of water distribution systems’ simulation on multicore systems

Hydraulic solvers for the simulation of flows and pressures in water distribution systems (WDS) are used extensively, and their computational performance is key when considering optimization problems. This paper presents an approach to speedup the hydraulic solver using OpenMP with two efficient methods for WDS simulation. The paper identifies the different tasks carried out in the simulation, showing their contribution to the execution time, and selecting the target tasks for parallelization. After describing the algorithms for the selected tasks, parallel OpenMP versions are derived, with emphasis on the task of linear system update. Results are presented for four different large WDS models, showing considerable reduction in computing time.

Draft Genome Sequences of Six Mycobacterium immunogenum Strains Obtained from a Chloraminated Drinking Water Distribution System Simulator.

We report here the draft genome sequences of six Mycobacterium immunogenum strains isolated from a chloraminated drinking water distribution system simulator subjected to changes in operational parameters. M. immunogenum, a rapidly growing mycobacterium previously reported to be the cause of hypersensitivity pneumonitis from contaminated metalworking fluid aerosols, is becoming a public health concern.

Improving the Efficiency of the Loop Method for the Simulation of Water Distribution Systems

AbstractEfficiency of hydraulic solvers for the simulation of flows and pressures in water distribution systems (WDSs) is very important, especially in the context of optimization and risk analysis problems, where the hydraulic simulation has to be repeated many times. Among the methods used for hydraulic solvers, the most prominent nowadays is the global gradient algorithm (GGA), based on a hybrid node-loop formulation. Previously, another method based just on loop flow equations was proposed, which presents the advantage that it leads to a system matrix that is in most cases much smaller than in the GGA method, but has also some disadvantages, mainly a less sparse system matrix and the fact that introducing some types of valves requires the redefinition of the set of network loops initially defined. The contribution of this paper is to present solutions for overcoming the mentioned disadvantages of the method based on loop flow equations. In particular, efficient procedures are shown for selecting the n...

Modelling and Simulation of Water Distribution Systems with Quantised State System Methods

The work in this paper describes a study of quantised state systems in order to formulate a new framework within which water distribution systems can be modelled and simulated. In contrast to the classic time-slicing simulators, depending on the numerical integration algorithms, the quantisation of system states would allow accounting for the discontinuities exhibited by control elements in a more efficient manner, and thereby, offer a significant increase in speed of the simulation of water network models. The proposed approach is evaluated on a case study and compared against the results obtained from the Epanet2 simulator and OpenModelica.

Applications of Deep Learning for Smart Water Networks

Abstract Deep Learning (DL) is the state-of-art paradigm of Artificial Neural Network (ANN) computing. It is a new breakthrough in machine learning, and differentiates from the conventional or shallow learning algorithms by emulating the six-layer human neocortex, which is unique for human brain containing billions of interconnected neurons. Unlike canonical ANN, DL is capable of self-learning data features by mimicking the self-learning functions layer by layer in human cortex and creating a data-driven model with the given dataset. This paper reports the initial applications of deep learning for simulation, optimization and operation control of water distribution systems. It elaborates the development of efficient deep learning framework with potential applications of facilitating the data fusion, system simulation and predictive analysis, detection of abnormal events from the recorded time series data (pressures, flows and consumptions etc.), water usage prediction, construction of a meta-model as a surrogate to the physics-based models (hydraulic and water quality), and acceleration of the solution search for smart water distribution management, which aims at improving operation efficiency, reducing carbon footprint, and exceling customers’ expectation.

Long-time simulation of water distribution systems for the design of small hydropower systems

The implementation of small hydropower systems (SHPS) in existing Alpine Water distribution systems (WDS) is a meaningful strategy for the reduction of CO2 emissions. Typically, the water consumption patterns of one or several representative days are used for the design procedure of these systems. For the design of the SHPS in WDS, the most important issue is to preserve the water supply without constraints. However, regarding water quality issues (especially related to water age), SHPS can exert a positive influence on the WDS performance. In this study, a long-time simulation model (10 years) is established for a WDS model and is subsequently used for the design of the SHPS. This study is performed to establish a more efficient design of these systems and to evaluate the long-term effects on the pressure and water quality issues, i.e., water age. With a simulation time over one decade on an hourly basis, full load hours and water flows for different turbine sizes are determined in detail. Based on that information, the detailed design and optimisation of the SHPS were performed, and the most cost-efficient device size was determined.

Effect of Disinfectant, Water Age, and Pipe Materials on Bacterial and Eukaryotic Community Structure in Drinking Water Biofilm

Availability of safe, pathogen-free drinking water is vital to public health; however, it is impossible to deliver sterile drinking water to consumers. Recent microbiome research is bringing new understanding to the true extent and diversity of microbes that inhabit water distribution systems. The purpose of this study was to determine how water chemistry in main distribution lines shape the microbiome in drinking water biofilms and to explore potential associations between opportunistic pathogens and indigenous drinking water microbes. Effects of disinfectant (chloramines, chlorine), water age (2.3 days, 5.7 days), and pipe material (cement, iron, PVC) were compared in parallel triplicate simulated water distribution systems. Pyrosequencing was employed to characterize bacteria and terminal restriction fragment polymorphism was used to profile both bacteria and eukaryotes inhabiting pipe biofilms. Disinfectant and water age were both observed to be strong factors in shaping bacterial and eukaryotic community structures. Pipe material only influenced the bacterial community structure (ANOSIM test, P < 0.05). Interactive effects of disinfectant, pipe material, and water age on both bacteria and eukaryotes were noted. Disinfectant concentration had the strongest effect on bacteria, while dissolved oxygen appeared to be a major driver for eukaryotes (BEST test). Several correlations of similarity metrics among populations of bacteria, eukaryotes, and opportunistic pathogens, as well as one significant association between mycobacterial and proteobacterial operational taxonomic units, provides insight into means by which manipulating the microbiome may lead to new avenues for limiting the growth of opportunistic pathogens (e.g., Legionella) or other nuisance organisms (e.g., nitrifiers).

Formation and release behavior of iron corrosion products under the influence of bacterial communities in a simulated water distribution system

To understand the formation and release behavior of iron corrosion products in a drinking water distribution system, annular reactors (ARs) were used to investigate the development processes of corrosion products and biofilm community as well as the concomitant iron release behavior. Results showed that the formation and transformation of corrosion products and bacterial community are closely related to each other. The presence of sulfate-reducing bacteria (SRB, e.g. Desulfovibrio and Desulfotomaculum), sulfur-oxidizing bacteria (SOB, e.g. Sulfuricella), and iron-oxidizing bacteria (IOB, e.g. Acidovorax, Gallionella, Leptothrix, and Sphaerotilus) in biofilms could speed up iron corrosion; however, iron-reducing bacteria (IRB, e.g. Bacillus, Clostridium, and Pseudomonas) could inhibit iron corrosion and iron release. Corrosion scales on iron coupons could develop into a two-layered structure (top layer and inner layer) with time. The relatively stable constituents such as goethite (α-FeOOH) and magnetite (Fe3O4) mainly existed in the top layers, while green rust (Fe6(OH)12CO3) mainly existed in the inner layers. The IOB (especially Acidovorax) contributed to the formation of α-FeOOH, while IRB and the anaerobic conditions could facilitate the formation of Fe3O4. Compared with the AR test without biofilms, the iron corrosion rate with biofilms was relatively higher (p < 0.05) during the whole experimental period, but the iron release with biofilms was obviously lower both at the initial stage and after 3 months. Biofilm and corrosion scale samples formed under different water supply conditions in an actual drinking water distribution system verified the relationships between the bacterial community and corrosion products.

Bacillus spore uptake onto heavily corroded iron pipe in a drinking water distribution system simulator

A dechlorinated drinking water distribution system simulator containing 15-year old unlined iron pipe was contaminated with Bacillus globigii spores. Bulk phase spore density decreased by 1·5 – 2·0 log10 5 min after injection and declined by 1·8 – 3·4 log10 after 4 d. Spores were detected on the iron pipe interior at 2 and 22 CFU/cm2 after it was decontaminated and disassembled. These results support previous bench-scale studies that show that spores can persist on corroded surfaces in a drinking water environment.

Sociotechnical risk assessment for water distribution system contamination threats

Water distribution systems (WDS) are vulnerable to contaminants, and systematic risk assessment can provide valuable information for assisting threat management. Contamination events are sociotechnical systems, in which the interactions among consumers and water infrastructure may generate unpredicted public health consequences. This research develops a sociotechnical risk assessment framework that simulates the dynamics of a contamination event by coupling an agent-based modeling (ABM) framework with Monte Carlo simulation (MCS), genetic algorithm (GA) optimization, and a multi-objective GA. The ABM framework couples WDS simulation with agents to represent consumers in a virtual city. MCS is applied to estimate the uncertainty in human exposure, based on probabilistic models of event attributes. A GA approach is used to identify critical contamination events by maximizing risk, and a multi-objective approach explores the trade-off between consequence and occurrence probabilities. Results that are obtained using the sociotechnical approach are compared with results obtained using a conventional engineering model. The sociotechnical approach removes assumptions that have been used in engineering analysis about the static, homogeneous, and stationary behaviors of consumers, and results demonstrate new insight about the impacts of these actions and interactions on the public health consequences of contamination events.

Establishment and early succession of bacterial communities in monochloramine-treated drinking water biofilms

Monochloramine is an increasingly used drinking water disinfectant and has been shown to increase nitrifying bacteria and mycobacteria in drinking waters. The potential successions and development of these bacteria were examined by 16S rRNA gene clone libraries generated from various biofilms within a water distribution system simulator. Biofilms were obtained from in-line and off-line devices using borosilicate glass beads, along with polycarbonate coupons from annular reactors incubated for up to 8months in monochloramine-treated drinking water. No significant difference in community structures was observed between biofilm devices and coupon material; however, all biofilm communities that developed on different devices underwent similar successions over time. Early stages of biofilm formation were dominated by Serratia (29%), Cloacibacterium (23%), Diaphorobacter (16%), and Pseudomonas (7%), while Mycobacterium-like phylotypes were the most predominant populations (>27%) in subsequent months. The development of members of the nontuberculous mycobacteria (NTM) after 3months may impact individuals with predisposing conditions, while nitrifiers (related to Nitrospira moscoviensis and Nitrosospira multiformis) could impact water quality. Overall, 90% of the diversity in all the clone library samples was associated with the phyla Proteobacteria, Actinobacteria, and Bacteroidetes. These results provide an ecological insight into biofilm bacterial successions in monochloramine-treated drinking water.

상수도 관망 데이터의 사용목적에 관한 수집 주기 연구

Sampling rate of Hydraulic pressure data, depending on the intended use of the water distribution system is an important factor. If sampling interval of hydraulic data is short, that will be more useful but it demand a lot of expense for maintenance. In this study, based on simulation of water distribution system 2 khz data, statistical techniques of student t distribution, non-exceedance probability using the optimal sampling rate for research.

Influence of Biofilm on DBP Formation in Simulated Water Distribution System

Influence of Biofilm on DBP Formation in Simulated Water Distribution System

Experimental investigation of leak hydraulics

In recent decades the hydraulics of leaks, i.e. the definition of the relationships linking the hydraulic quantities in pipes with leaks, has received increasing attention. On the one hand, the definition of the relationship between the leak outflow and the relevant parameters – e.g. the leak area and shape, the pressure inside the pipe and outside the leak, and the pipe material – is crucial for pressure control and inverse analysis techniques. On the other hand, if the effect of the leakage on the governing equations is not taken into account, i.e. the loss of the flow axial momentum is not considered, significant errors can be introduced in the simulation of water distribution systems. In this paper, the governing equations for a pipe with a leak are derived. The basic equations, obtained within different approaches, are presented in a consistent formulation and then compared with the results of some experimental tests. The leak jet angle and other major features of the results are analysed. The estimated values of the parameters can be used in the water distribution network models when pipes with a diffuse leakage are considered.

Influences of pipe materials and hydraulic conditions on the process of trihalomethanes formation in water distribution network

abstract In this paper, the influences of different pipe materials and hydraulic conditions on the formation process of trihalomethanes (THMs) were investigated. Water used in this research was taken from a Chinese water treatment plant in the northeastern part of China. The results showed that, under the same condition, the ranking of THM concentration in different pipe materials was: PVC > stainless steel > ductile iron, while it was different for chlorine decay. The variation of THMs in distribution system simulator is consistent with that of bulk water experiment. THM amount is only slightly higher in water distribution system simulator. The trend is not as distinct as that of chlorine decay. A higher flow velocity of water is associated with higher THM concentration, but it was not obvious compared with the decay of chlorine. For the same distance, different flow velocities and diameters, THM concentration was dependent on the influential degree of retention time and flow velocity. For the same dista...

Identifying contaminant sources for water distribution systems using a hybrid method

The rapid discovery of the contaminant source in a water distribution system (WDS) is vital for generating an efficient control strategy during a contamination event. An inverse problem can be constructed, given sensor measurements in a WDS, to identify the contaminant source characteristics by integrating a WDS simulation model with an optimisation method. However, this approach requires numerous compute-intensive simulation runs to evaluate potential solutions. This paper reports the findings of an investigation by introducing a hybrid method for the real-time characterisation of a contaminant source. This new method integrates a simulation-optimisation approach with a logistic regression and a local improvement method to expedite the convergence and possibly solve the problem quickly. The results of numerical experiments on two example WDS networks demonstrate the efficiency of the proposed hybrid method for contaminant source characterisation. Effects of various hybrid strategies on the algorithm performance are discussed.