WDNetGIS-XL

on .

Advanced hydraulic modeling is an indispensable support and prerequisite for system management at various operational horizons. It overcomes the limitations of traditional models based on the modelling coding of EPANET and the last century. WDNetGIS-XL offers several innovative tools for advanced hydraulic and topological analyses in contrast with traditional approaches, as shown in the following table:

 

Hydraulic and topological functionality

WDNetXL

EPANET

3D viewer

YES

NO

Representation of hydraulic devices and valves as attributes of each link object

YES

NO

Demand-Driven Analysis

YES

YES

Pressure-Driven Analysis

YES

NO

Analysis of variable level tanks within the GGA (G-GGA)

YES

NO (1)

Analysis of variable level tanks supplied from nodes inside the network (from the bottom or from the top) or external pipelines, as well as volume-based controls for filling-emptying cycles

YES

NO

Hydraulic analysis with real topological variations

YES

NO (2)

Pressure-driven analysis to evaluate the water supply to individual users

YES

NO

Pressure-driven analysis of hydrants

YES

YES (3)

Pressure-driven analysis of volumetric losses (background leakages and unreported bursts) at individual pipe level

YES

NO (4)

Pressure-driven analysis of local private tanks

YES

NO

Pressure-driven analysis to evaluate the supply of multi-story buildings

YES

NO

Pressure-driven analysis of free orifices connections

YES

NO

Simple rules

YES

YES

Complex rules

YES

YES

Unlimited demand time pattern

YES

YES

Patterns of pressure setting for valves and variable speed drive pumps

YES (5)

YES

Pattern of speed factors for variable speed drive pumps

YES

YES

Flow setting pattern for flow control valves

YES

YES

Topological analysis of districts (DMA)

YES

NO

Topological analysis of the isolation valve system (IVS)

YES

NO

Pressure-driven analysis of failures accounting for IVS

YES

NO

Pressure-driven analysis of burst in the pipelines

YES

NO

"Non-heuristic" modelling of pressure reduction (or sustain) valves locally and remotely (electrical) controlled (control with nodes inside the network)

YES

NO (6)

"Non-heuristic" modelling of variable speed pumps with local control or with nodes inside the network

YES

NO (6)

Energy consumption assessments and carbon footprint analysis of all types of pumps

YES

YES (7)

Hydraulic analyses without aggregating demands of various users in the model nodes

YES

NO

(1) EPANET, like all commercial software packages, does not calculate the level of the tanks within the GGA solver as a separate variable. The level of the tanks is corrected between one simulation and the following with a mass balance which, due to the lack of accuracy, causes errors that turn into instability when more variable level tanks are hydraulically close each other. The Generalized-GGA (G-GGA) solves the problem and allows hydraulic simulation even in the presence of only variable level tanks and absence of reservoirs.

(2) EPANET, like all commercial software packages, does not perform topological analysis when a pipe is closed or closes during the simulation (hydraulic directional and/or pressure control devices), but assigns a minimum flow rate (10-6 L/s) to model the closure. This causes problems of accuracy and robustness of the solution, particularly for complex and/or large systems, not allowing different types of optimizations (for example the hydraulic optimization of DMA). WDNetXL, on the other hand, detects the topology before (e.g., in the case of an isolation valve system that separates a portion of the network during failure analysis) and during hydraulic simulations (e.g., for closing flow reversal devices unidirectional or controlled).

(3) EPANET, like almost all commercial software packages, applies a modelling device to calculate emitters that are born as free orifices to simulate hydrants. The modelling artifice is not suitable for the calculation of a large number of hydrants, while WDNetXL calculates the hydrants as free orifices (with exponent also different from ½ for the case of bursts) within the G-GGA pressure driven, i.e. the method Newton-Raphson uses the derivative schemes in favor of accuracy (especially with reference to the global mass balances of the system), of the robustness and speed of the analysis.

(4) EPANET, like all commercial software packages, does not calculate the volumetric losses as a function of the pressure at the level of the pipes, but uses the emitters in the nodes where what happens in the confluent pipes is concentrated. Besides the problems of accuracy and robustness of the simulation (see point 3), the method alters the realistic representation of volumetric losses and relative results. Furthermore, it does not allow the preservation of information at the pipe level, useful, for example, to plan its replacement.

(5) WDNetXL allows, in addition, the possibility of setting the control pressure setting of variable speed pumps.

(6) EPANET, like almost all commercial software packages, implements heuristic rules for calculating hydraulic resistance (valves regulated by pressure or flow). WDNetXL calculates the hydraulic resistances of controlled devices as variables within the G-GGA, thus allowing the control nodes of devices to be positioned also within the network.

(7) EPANET, like almost all commercial software packages, does not use a variable efficiency value with the operating point of the variable speed pumps. WDNetXL calculates both the energy consumption and the power of the individual pumps, for each simulation of the operating cycle, considering the variable efficiency and also returning the value of the CO2 produced.

 

WDNetXL generates the Digital Twin of network. The Digital Twin has a data format that is completely interoperable with the hydraulic platform WDNetXL running in Microsoft Excel and the GIS environment. Hence, the data tables in WDNetXL are the same georeferenced in GIS with the ESRI system, as shown below. In this way, WDNetGIS-XL is connected to Microsoft Excel and GIS.

 

 

Digital Water Services

Digital Water ideas will change the future management of water distribution networks.

WDNetGIS-XL integrates the tools of Complex Network Theory (CNT), evolutionary multi-objective optimization and machine learning to perform advanced analysis through Digital Water Services (DWSs). Digital Water Services are developed as plugins for QGIS software, and they are completely customizable depending on the needs of water utilities. The basis for DWSs is the Digital Twin of the network. The digital model of the system is the twin of the real system because the topological and hydraulic features of advanced hydraulic modelling allow reproducing the actual behavior of water supply networks and the integration with the GIS allows georeferencing any hydraulic system component.

Digital Water Services support problem solving in several technical tasks using WDNetGIS-XL features:

WDNetGIS

on .

WDNetGIS exploits GIS interface that allows great flexibility to users working on the analysis, design and management of water networks. WDNetGIS, is fully integrated with WDNetXL, can import data from standard * .inp EPANET files, generable by all commercial software. It is equipped with a 3D viewer with data query function, and integrates completely with WDNetXL by means of shapefiles.

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WDNetGIS

on .

Modern management of water distribution networks requires increasingly reliable and sustainable solutions aimed at adequately targeting investments, while providing efficient services to consumers.

WDNetGIS software integrates robust and advanced hydraulic simulations with topological analysis (WDNetXL), allowing the management, visualization, modification and analysis of data (GIS).

WDNetGIS exploits GIS interface that allows great flexibility to users working on the analysis, design and management of water networks without additional training in the use of data management environment (GIS). Therefore, it enables focusing on the technical-hydraulic aspect right away. WDNetGIS, like WDNetXL, can import data from standard * .inp EPANET files, generable by all commercial software. It is equipped with a 3D viewer with data query function, and integrates completely with WDNetXL by means of shapefiles.

 

WDNetXL

on .

The WDNetXL is an integrated system for water distribution network (WDN) analysis, planning and management distributed as MS-Excel® add-ins. It integrates advanced and robust WDN hydraulic simulation with topological analysis and optimization strategies to support technicians for complex WDN analysis, design and management problems. The WDNetXL system permits to realize just-in-time technology transfer from technical research to WDN management sector through a holistic platform, which is ready for possible extensions to the latest innovations, thus providing an upgradable support to match current and future technical needs.

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WDNetXL

on .

The WDNetXL is an integrated system for water distribution network (WDN) analysis, planning and management distributed as MS-Excel® add-ins. It integrates advanced and robust WDN hydraulic simulation with topological analysis and optimization strategies to support technicians for complex WDN analysis, design and management problems.

       

The WDNetXL system permits to realize just-in-time technology transfer from technical research to WDN management sector through a holistic platform, which is ready for possible extensions to the latest innovations, thus providing an upgradable support to match current and future technical needs. This also provides a practical tool for training of engineers ranging from university classes to continuing education at water companies. Its versatility also makes the system dynamic to implement customized solutions through a virtuous cycle between users, researchers and developers.

The WDNetXL system is open source meaning that, although the dlls are binary files, it is possible to use them beyond the templates provided in the original package and link them to GIS or other systems by means of standard programming languages connecting MS-Excel to external applications.

One key ingredient is the hydraulic simulation, which is pretty advanced, and if compared to EPANET2 in terms of robustness, hydraulic consistency and flexibility in analyzing many WDN elements like components of water demand, leakages, control devices, etc.

  

 ANALISI IDRAULICA AVANZATA IN WDNetXL (ITA)

  

 WDNetXL_vs_EPANET

 

 

 

To get installation packages contact us Idea-rt contact

EPR MOGA - XL

on .

The availability of large and detailed databases together with the increased computational capabilities has motivated researchers to propose innovative techniques and methodologies to mine information from data. The Evolutionary Polynomial Regression (EPR) [Giustolisi and Savic (2006)] has been introduced in the hydroinformatics community as a hybrid data-driven technique, which combines the effectiveness of genetic algorithms with numerical regression for developing simple and easily interpretable mathematical model expressions. The multi-objective search paradigm has been introduced [Giustolisi and Savic (2009)] for developing multiple models by simultaneously optimizing fitness to training data and parsimony of resulting mathematical expressions. Such improvement allows for a sudden understanding of existing patterns in data

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EPR MOGA - XL

on .

The availability of large and detailed databases together with the increased computational capabilities has motivated researchers to propose innovative techniques and methodologies to mine information from data. The Evolutionary Polynomial Regression (EPR) [Giustolisi and Savic (2006)] has been introduced in the hydroinformatics community as a hybrid data-driven technique, which combines the effectiveness of genetic algorithms with numerical regression for developing simple and easily interpretable mathematical model expressions. The multi-objective search paradigm has been introduced [Giustolisi and Savic (2009)] for developing multiple models by simultaneously optimizing fitness to training data and parsimony of resulting mathematical expressions. Such improvement allows for a sudden understanding of existing patterns in data

(if any) by comparing different optimal models and the selection of the model which best fit the purpose of the analysis. In recent years EPR was used to investigate and pipe failures in water distribution and wastewater networks, rainfall-groundwater dynamics, scour depths downstream grade-control structures, explicit formulations of Colebrook-White friction factor, and evapotranspiration process. It was also adopted for other applications in the areas of geotechnical and structural engineering.

 

            

      

 

      

 

 

 

 

 

 

 

EPR MOGA-XL vr.1 integrates some advancement in artificial intelligence and data-driven modelling areas like an efficient multi-objective genetic algorithm (OPTIMOGA) and the algorithm for developing nonlinear mathematical structures using an integer coding. EPR MOGA-XL is a MS-Excel add-in and the user can launch EPR runs as a function in MS-Excel. Input data can be manually selected from any spreadsheets, without the hindrances of previous versions which required a strict data preparation. A sheet containing all EPR modelling options can be easily modified and retrieved for next analyses. Moreover, the user is guided through proper option setting by some tips, which recall the meaning of each parameter. In order to facilitate multiple analyses, the expression(s) of model(s) obtained, the values model predictions and fitness indicators of each model are stored in a separate Excel file. This allows the user to perform multiple analyses while preserving complete information on both input data and modelling settings.

 

brochure EPR-MOGA-XL

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ANN MOGA - XL

on .

ANN MOGA (ANNs by Multi-Objective Genetic Algorithm) is a tool developed on the homonymous modelling methodology based on the ANNs paradigm [Giustolisi and Simeone (2006)]. The tool employs a particular structure of ANN named the Input-Output Neural Network (IONN). In particular, it is based on a MOGA approach for construction of IONN models, which prevents potential overfitting troubles caused by poor generalization capabilities of the identified ANNs [Giustolisi and Laucelli (2005)]. This can be obtained by minimizing the model’s input dimension and the number of hidden neurons (flexibility) while preserving fitness properties.

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