Application of two approaches to model chlorine residuals in Severn Trent Water Ltd (STW) distribution systems

1997 ◽  
Vol 36 (5) ◽  
pp. 317-324 ◽  
Author(s):  
M.J. Rodriguez ◽  
J.R. West ◽  
J. Powell ◽  
J.B. Sérodes
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Treatment Plant ◽  
Residual Chlorine ◽  
Ann Model ◽  
Order Model ◽  
First Order ◽  
Chlorine Decay ◽  
Artificial Neural Network Ann ◽  
Key Parameter

Increasingly, those who work in the field of drinking water have demonstrated an interest in developing models for evolution of water quality from the treatment plant to the consumer's tap. To date, most of the modelling efforts have been focused on residual chlorine as a key parameter of quality within distribution systems. This paper presents the application of a conventional approach, the first order model, and the application of an emergent modelling approach, an artificial neural network (ANN) model, to simulate residual chlorine in a Severn Trent Water Ltd (U.K.) distribution system. The application of the first order model depends on the adequate estimation of the chlorine decay coefficient and the travel time within the system. The success of an ANN model depends on the use of representative data about factors which affect chlorine evolution in the system. Results demonstrate that ANN has a promising capacity for learning the dynamics of chlorine decay. The development of an ANN appears to be justifiable for disinfection control purposes, in cases when parameter estimation within the first order model is imprecise or difficult to obtain.

scholarly journals Residual Chlorine Decay in Juja Water Distribution Network using EPANET Model

2019 ◽  
Vol 9 (1) ◽  
pp. 332-334 ◽  
Keyword(s):  
Water Distribution ◽  
Distribution Network ◽  
Treatment Plant ◽  
Water Distribution Network ◽  
Residual Chlorine ◽  
Simulation Tool ◽  
First Order ◽  
Water Company ◽  
Field Samples ◽  
Chlorine Decay

The current study was carried out to analyze the residual chlorine decay analysis within the existing Juja water distribution network. The study used EPANET as a simulation tool. From the field samples, the first-order bulk decay coefficient Kb was found equal to - 0.04 . The wall coefficient Kw was assumed to - 4.0 mg/ /day as guided by literature. The analysis shows that the entire supply area of the existing distribution network faces higher residual chlorine concentration (0.70 to 0.8 mg/l) from 9 am. The study recommended the reducing of the initial chlorine added at the treatment plant and the optimization of the network, which will provide a proper residual chlorine dosage to reduce Juja consumers exposure to health risk and also to be economically reasonable for the water company in charge

scholarly journals Optimization and Assessment of Residual Chlorine using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) Modeling

2019 ◽  
Vol 8 (3) ◽  
pp. 258-263
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Distribution System ◽  
Water Distribution ◽  
Residual Chlorine ◽  
Urban Water ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Rsm Model

Water polluted with microorganisms and pathogens is one of the most significant hazards to public health. Potential microorganisms unsafe to human health can be destroyed through effective disinfection. To stop the re-growth of microorganisms, it is also advisable to take care of the residual disinfectant in the water distribution networks. The most frequently used cleanser material is chlorine. When the chlorine dosage is too low, there will be a deficiency of enough residues at the end of the water network system, leading to re-growth of microorganisms. Addition of an excessive amount of chlorine will lead to corrosion of the pipeline network and also the development of disinfection by-products (DBPs) including carcinogens. Thus, to determine the best rate of chlorine dosage, it is essential to model the system to forecast chlorine decay within the network. In this research study, two major modeling and optimization strategies were employed to assess the optimum dosage of chlorine for municipal water disinfection and also to predict residual chlorine at any predetermined node within the water distribution network. Artificial neural network (ANN) modeling techniques were used to forecast chlorine concentrations in different nodes in the urban water distribution system in Muscat, the capital of the Sultanate of Oman. One-year dataset from one of the distribution system was used for conducting network modeling in this study. The input factors to RSM model considered were pH, chlorine dosage and time. Response variables for RSM model were fixed as total organic carbon (TOC), Biological oxygen demand (BOD) and residual chlorine An Artificial neural network (ANN) model for residual chlorine was created with pH, inlet-concentration of chlorine and initial temperature as input parameters and residual chlorine in the piping network as an output parameter. The ANN model created using these data can be employed to forecast the residual chlorine value in the urban water network at any given specific location. The results from this study utilizing the uniqueness of an ANN model to predict residual chlorine and water quality parameters have the potential to detect complex, higher-order behavior between input and output parameters exist in urban water distribution system.

Poor efficacy of residual chlorine disinfectant in drinking water to inactivate waterborne pathogens in distribution systems

1999 ◽  
Vol 45 (8) ◽  
pp. 709-715 ◽  
Author(s):  
Pierre Payment
Keyword(s):  
Escherichia Coli ◽  
Drinking Water ◽  
Clostridium Perfringens ◽  
Water Samples ◽  
Distribution System ◽  
Distribution Systems ◽  
Treatment Plant ◽  
Epidemiological Studies ◽  
Residual Chlorine ◽  
Residual Concentration

To evaluate the inactivating power of residual chlorine in a distribution system, test microorganisms (Escherichia coli, Clostridium perfringens, bacteriophage phi-X 170, and poliovirus type 1) were added to drinking water samples obtained from two water treatment plants and their distribution system. Except for Escherichia coli, microorganisms remained relatively unaffected in water from the distribution systems tested. When sewage was added to the water samples, indigenous thermotolerant coliforms were inactivated only when water was obtained from sites very close to the treatment plant and containing a high residual chlorine concentration. Clostridium perfringens was barely inactivated, suggesting that the most resistant pathogens such as Giardia lamblia, Cryptosporidium parvum, and human enteric viruses would not be inactivated. Our results suggest that the maintenance of a free residual concentration in a distribution system does not provide a significant inactivation of pathogens, could even mask events of contamination of the distribution, and thus would provide only a false sense of safety with little active protection of public health. Recent epidemiological studies that have suggested a significant waterborne level of endemic gastrointestinal illness could then be explained by undetected intrusions in the distribution system, intrusions resulting in the infection of a small number of individuals without eliciting an outbreak situation.Key words: drinking water, chlorine, disinfection, pathogens, distribution system.

scholarly journals Effect of Dividing the Water Transmission Pipe Line in Modeling Residual Chlorine, Case Study: Isfahan Water Transmission Line

2021 ◽  
Author(s):  
Yasaman Frozandeh ◽  
Ali Dehnavi ◽  
Ahmad Shanehsazzadeh
Keyword(s):  
Transmission Line ◽  
Line Length ◽  
Residual Chlorine ◽  
Water Age ◽  
Order Model ◽  
First Order ◽  
Pipe Line ◽  
Complex Models ◽  
Chlorine Decay ◽  
Water Transmission

Abstract In assessing the quality of drinking water in transmission and distribution lines, the study on chlorine reactions is of particular importance. Chlorine decay happens in bulk and wall and it is mainly affected by the water age which depends on the transmission line length. Residual chlorine concentration in Isfahan water transmission line (IWTL) is simulated through three decay models, namely the first order, parallel first order and second order single reactant (SR model) which incorporated in EPANET and EPANET MSX, respectively. The results of the models are compared through two approaches, one is the one-part approach (OPA) whereby chlorine decay simulation is performed taking into account the whole line as one section and the second is multi-part approach (MPA) whereby the line is divided into two sections and decay coefficients of chlorine for each section are separately determined. Results show that in the OPA, the SR model in summer and the parallel model in winter are the best kinetic models. While in the MPA, the results of first order model has the same order of accuracy as the more complex models of parallel and SR models. In general, the simple first order model in the MPA applied by EPANET2.0 s/w provides acceptable level of accuracy in compare to the complex models applied in EPANET MSX s/w. The average RMSE volumes are reduced from 0.078 in OPA to 0.029 in MPA in summer and from 0.059 to 0.015 in winter, indicating that the dividing the line in simulation procedure and considering the individual decay coefficient for each part, considerably improves the results, more effectively than the application of advanced decay models.

Prediction of multi-components (chlorine, biomass and substrate concentrations) in water distribution systems using artificial neural network (ANN) models

2009 ◽  
Vol 9 (3) ◽  
pp. 289-297
Author(s):  
Celia D. D'Souza ◽  
M. S. Mohan Kumar
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Transport Model ◽  
Absolute Error ◽  
Temporal Variations ◽  
Residual Chlorine ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural

Artificial Neural Networks (ANN) models are used to predict residual chlorine, substrate and biomass concentrations in a Water Distribution System (WDS). ANN models with different architectures are developed: a one output ANN model (predicting chlorine, substrate and biomass individually), a two output ANN model (predicting chlorine + substrate, chlorine + biomass or substrate + biomass) and a three output ANN model (chlorine + substrate + biomass). This study is carried out for the Bangalore City and North Marin WDSs. Data for these WDSs is obtained from the multi-component reaction transport model. The models are compared using the correlation coefficient (R) and the Mean Absolute Error (MAE). The models developed are able to predict, reasonably well, the temporal variations in the chlorine, substrate and biomass concentrations. Error analysis is carried out to determine the robustness of the models.

Residual chlorine management in water distribution systems using network modelling techniques: case study in Seoul City

2004 ◽  
Vol 4 (5-6) ◽  
pp. 421-429
Author(s):  
J.C. Ahn ◽  
Y.W. Kim ◽  
K.S. Lee ◽  
J.Y. Koo
Keyword(s):  
Water Treatment ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Residual Chlorine ◽  
Network Modelling ◽  
Using Data ◽  
One Year

Twelve sampling locations in a network from a water treatment plant to consumers' taps were selected for measuring residual chlorine loss, THMs, TOC, etc. and 24 hour sampling in the locations was conducted on a bimonthly basis for one year. Chlorine bulk decay and THM formation tests were carried out by bottle tests under controlled temperatures for three locations: a water treatment plant, a large service reservoir, and a pumping station. Water quality modelling of chlorine loss in the distribution system was performed using data collected in the field study. This study contributed to the improvement of chlorine management in the distribution system by providing information for operators to maintain a minimum level of chlorine residual in a service reservoir.

Optimal Capacitors Placement of Distribution Systems Using 2nd Order Power Loss Sensitivity Analysis and Hierarchical Clustering

2014 ◽  
Vol 986-987 ◽  
pp. 377-382 ◽  
Author(s):  
Hui Min Gao ◽  
Jian Min Zhang ◽  
Chen Xi Wu
Keyword(s):  
Sensitivity Analysis ◽  
Hierarchical Clustering ◽  
Distribution System ◽  
Power Flow ◽  
Distribution Systems ◽  
Reactive Power ◽  
Digital Simulation ◽  
Second Order ◽  
First Order ◽  
The Impact

Heuristic methods by first order sensitivity analysis are often used to determine location of capacitors of distribution power system. The selected nodes by first order sensitivity analysis often have virtual high by first order sensitivities, which could not obtain the optimal results. This paper presents an effective method to optimally determine the location and capacities of capacitors of distribution systems, based on an innovative approach by the second order sensitivity analysis and hierarchical clustering. The approach determines the location by the second order sensitivity analysis. Comparing with the traditional method, the new method considers the nonlinear factor of power flow equation and the impact of the latter selected compensation nodes on the previously selected compensation location. This method is tested on a 28-bus distribution system. Digital simulation results show that the reactive power optimization plan with the proposed method is more economic while maintaining the same level of effectiveness.

scholarly journals Application of model fitting technique to enhance bacterial regrowth potential (BRP) measurement for drinking water supply monitoring

2021 ◽  
Author(s):  
Wenjin Xue ◽  
Christopher W. K. Chow ◽  
John van Leeuwen
Keyword(s):  
Drinking Water ◽  
Water Supply ◽  
Water Samples ◽  
Distribution System ◽  
Distribution Systems ◽  
Tap Water ◽  
Treatment Plant ◽  
Drinking Water Supply ◽  
Water Supply Systems ◽  
Bacterial Regrowth

Abstract The bacterial regrowth potential (BRP) method was utilised to indirectly measure the assimilable organic carbon (AOC) as an indicator for the assessment of the microbial regrowth potential in drinking water distribution systems. A model using various microbial growth parameters was developed in order to standardise the experimental interpretation for BRP measurement. This study used 82 experimental BRP data sets of water samples collected from the water treatment plant to locations (customer taps) in the distribution system. The data were used to model the BRP process (growth curve) by a data fitting procedure and to obtain a best-fitted equation. Statistical assessments and model validation for evaluating the equation obtained by fitting these 82 sets of data were conducted, and the results show average R2 values were 0.987 for treated water samples (collected at the plant prior to chlorination) and 0.983 for tap water (collected at the customer taps). The F values obtained from the F-test are all exceeded their corresponding F critical values, and the results from the t-test also showed a good outcome. These results indicate this model would be successfully applied in modelling BRP in drinking water supply systems.

An experimental study for chlorine residual and trihalomethane formation with rechlorination

2007 ◽  
Vol 55 (1-2) ◽  
pp. 307-313 ◽  
Author(s):  
J. Lee ◽  
D. Lee ◽  
J. Sohn
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Disinfection Byproducts ◽  
Correction Coefficient ◽  
Empirical Power ◽  
Chlorine Residual ◽  
Chlorine Decay ◽  
And Control ◽  
Large Distribution

Maintenance of adequate chlorine residuals and control of disinfection byproducts (DBPs) throughout water distribution systems is currently an important issue. In particular, rechlorination can be a powerful tool in controlling adequate chlorine residual in a large distribution system. The patterns of chlorine decay and formation of DBPs due to rechlorination are different from those of chlorination; chlorine decay is slower and trihalomethane (THM) formation is lower with rechlorination. The present study evaluates whether existing predictive models for chlorine residual and THM formation are applicable in the case of rechlorination. A parallel first-order decay model represents the best simulation results for chlorine decay, and an empirical power function model (modified Amy model) with an introduced correction coefficient (ϕ1, ϕ2) is more suitable to THM formation.

scholarly journals Rainfall Reliability Evaluation for Stability of Municipal Solid Waste Landfills on Slope

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Fu-Kuo Huang ◽  
Grace S. Wang ◽  
Yueh-Lin Tsai
Keyword(s):  
Municipal Solid Waste ◽  
Solid Waste ◽  
Engineering Properties ◽  
Ann Model ◽  
First Order ◽  
Reliability Method ◽  
Municipal Solid Waste Landfills ◽  
Artificial Neural Network Ann ◽  
The Stability ◽  
Solid Waste Landfills

A method to assess the reliability for the stability of municipal solid waste (MSW) landfills on slope due to rainfall infiltration is proposed. Parameter studies are first done to explore the influence of factors on the stability of MSW. These factors include rainfall intensity, duration, pattern, and the engineering properties of MSW. Then 100 different combinations of parameters are generated and associated stability analyses of MSW on slope are performed assuming that each parameter is uniform distributed around its reason ranges. In the following, the performance of the stability of MSW is interpreted by the artificial neural network (ANN) trained and verified based on the aforementioned 100 analysis results. The reliability for the stability of MSW landfills on slope is then evaluated and explored for different rainfall parameters by the ANN model with first-order reliability method (FORM) and Monte Carlo simulation (MCS).

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