Application of Artificial Neural Networks to the Technical Condition Assessment of Water Supply Systems

Kamiński, K.; Kamiński, W.; Mizerski, Tomasz

doi:10.1515/eces-2017-0003

Cited by 13 publications

(10 citation statements)

References 20 publications

(19 reference statements)

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“…A neural network is composed of multiple perceptron and is called a deep neural network when the number of hidden layers is greater than or equal to 2 [19]. We use a multiple layers neural network in order to predict the WER based on nine KPIs considered as input [16].…”

Section: The Artificial Neural Network (Ann)mentioning

confidence: 99%

Cost–Benefit Prediction of Asset Management Actions on Water Distribution Networks

Nafi

Brans

2019

Water

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The potential costs and benefits of a combination of asset management actions on the water distribution network are predicted. Two types of actions are considered: maintenance actions and renewal actions. Leak detection and reparation of failures on connections and pipes define the set of potential maintenance actions to be carried out. Renewal actions concern connections, pipes, and meters. All these actions represent the model’s decision variables in order to determine a trade-off between two objectives: (i) the maximization of the water efficiency rate and (ii) the minimization of the total cost of actions to be carried out on the water system. The assessment of objective functions is ensured by an artificial neural network (ANN) trained on a French mandatory database «SISPEA». A non-dominated sorting genetic algorithm (NSGA-II) is coupled to the ANN to reach the set of compromised solutions representing potential actions to achieve. Applied to a real water distribution system in the southeast of France, the proposed decision model indicates that the improvement of water efficiency rate (WER) in the short term requires increasing operation expenditures (OPEX), which represent 99% of the total cost. Results show the existence of a threshold effect that implies to use the budget in a certain way to improve performance. A potential solution can be chosen by the decision maker among the generated Pareto front with regard to the constraint on the budget and the targeted WER.

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Section: The Artificial Neural Network (Ann)mentioning

confidence: 99%

Cost–Benefit Prediction of Asset Management Actions on Water Distribution Networks

Nafi

Brans

2019

Water

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“…The explained and explanatory variables should be normalized in order to remove bias due to scale and unit differences. It seems that learning machines models are sensitive to variables difference of magnitude, which is the reason why it is recommended to transform them before [9][10][11][12][13][14][15][16][17]. The z-score normalization is frequently used in learning models [7], and the new scale is defined as in Equation (6):…”

Section: Normalization Of Variablesmentioning

confidence: 99%

“…The neural network was trained on six years of historical data. On a similar topic, a method for discerning sections of water networks in need of renewal is presented in [13]. They aim to develop a system-expert based on the training a multilayer ANN on more than 20 years data concerning hundreds of pipe sections.…”

mentioning

confidence: 99%

Prediction of Water Utility Performance: The Case of the Water Efficiency Rate

Nafi

Brans

2018

Water

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This paper deals with the development of a decision-aiding model for predicting, in an ex-ante way, the effects of a mix of actions on an asset and on its operation. The objective is then to define a compromised policy between costs and performance improvements. We investigate the use of multiple regression analysis (MRA) and an artificial neural network (ANN) to establish causal relationships between the network efficiency rate, and a set of explanatory variables on one hand, and potential water loss management actions such as leak detection, maintenance and asset renewal, on the other hand. The originality of our approach is in developing a two-step ex-ante model for predicting the efficiency rate involving low and high level explanatory variables in a context of unavailability of data at the scale of the water utility. The first step exploits a national French database «SISPEA» (Système d'Information d'information sur les Services Publics d'Eau et d'Assainissement) to calibrate a general prediction model that establishes a correlation between efficiency (output) and other performance indicators (inputs). The second step involves the utility manager to build a causal model between endogenous and exogenous variables of a specific water network (low level) and performance indicators considered as inputs for the previous step (high level). Uncertainty is taken into account by Monte Carlo simulations. An application of our decision model on a water utility in the southeast of France is provided as a case study.Several input parameters from more than three hundred Districts Meter areas (DMA) to estimate the non-revenue water (NRW) using MRA and ANN models can be considered as shown in [6].The authors conclude that ANN seems to be more accurate than standard statistical methods, as with MRA. The accuracy of estimation depends on the number of ANN's neurons. An estimation of the leakage ratio based on 6 effective parameters by combining ANN with Principal Component Analysis (PCA) is carried out in [7]. PCA-ANN multiple hidden layers seem more accurate than standard ANN. A comparison of the performance of ANN and support vector regression (SVR) in predicting the pipe burst rate (PBR) is made in [8]. ANN was applied on multiple years of data collection from pipes in order to predict water mains failure; data availability seems a prerequisite for prediction accuracy [9]. The authors test six ANN models in order to select the best configuration for failures prediction to elaborate rehabilitation strategies of water distribution systems. For the same problem, a model is developed for more accurate prediction for pipes failures based on ANN and neuro-fuzzy systems [10]. The authors compare the ANN model with conventional multivariate regression, and conclude that the ANN is more accurate for prediction. An ANN model to forecast pipe breaks involving multiple explanatory variables including pipe age, diameter, length, and surrounding soil type can be created as shown in [11]. The authors build a decision model based on histo...

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“…The tests were performed at pH (3.0-7.0), with initial metal concentration (5.0-50 mg/dm 3 ), contact time (0-24 hours) and sorbent amount (0.1-1.0 g/dm 3 ), temperature (20-50 °C), and shaking speed (50-200 rpm). Isoterms were examined for initial concentration and the used sorbent amounts.…”

Section: Batch Adsorption Experimentsmentioning

confidence: 99%

“…Water supply systems usually comprise a water intake, purification facilities, transit pipelines, pumping stations and storage facilities [3]. Various drinking water treatment processes generate a vast amount of waste sludge around the worlds, which requires economically sustainable and environment-friendly ways of sludge removal methods [4].…”

Section: Introductionmentioning

confidence: 99%

Heavy Metal Adsorption by Dewatered Iron-Containing Waste Sludge

Yıldız

Sevinç

2018

Ecological Chemistry and Engineering S

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Drinking water treatment plants produce significant amounts of waste sludge. In this study, removal of Nickel ion by use of wastewater sludge was aimed. The adsorption capability of waste sludge was optimized with varying physical parameters such as pH, adsorbent dosage, adsorbate concentration, contact time, shaking speed and temperature. Initial concentration was set as 25 mg/dm3, absorbent dose was set as 0.3 g/cm3, and temperature was set as 25 °C. Compliance of balance data with Langmuir, Freundlich, Temkin and D-R isotherm models was investigated. The highest R2 values were obtained with Freundlich isotherm (R2 = 0.92-0.95). Adsorption kinetics was analysed using pseudo-first order, pseudo-second order, Weber and Morris intraparticle diffusion and Elovich kinetic models, and the system was found to be in a better compliance with pseudo-second order kinetic model. Iron sludge was used as sorbent, and accordingly total iron ion measurements were carried out to determine its possible effects on water. Additionally, SEM, EDX, FTIR spectroscopy, XRD spectrum and atomic force microscope (AFM) measurements were conducted to determine the interaction between the sorbent and metal ions, in addition to characterization of the sorbent. As indicated by research results, drinking water treatment sludge proved to be a potential adsorbent for removal of nickel(II) ions from the solution.

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Application of Artificial Neural Networks to the Technical Condition Assessment of Water Supply Systems

Cited by 13 publications

References 20 publications

Cost–Benefit Prediction of Asset Management Actions on Water Distribution Networks

Cost–Benefit Prediction of Asset Management Actions on Water Distribution Networks

Prediction of Water Utility Performance: The Case of the Water Efficiency Rate

Heavy Metal Adsorption by Dewatered Iron-Containing Waste Sludge

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