Expert System for Management of Water
Distribution Network (WDN)
SANDEEP KULSHRESTHA*
Research Scholar, IIT Delhi, India san1.djb@gmail.com
RAKESH KHOSA
Civil Engineering Department,IIT Delhi,Hauz Khaz, New rakesh.khosa@gmail.com
Abstract
This paper describes a component of an ongoing research initiative to investigate the potential application of artificial intelligence in the development of an ‘expert’ decision support system for management of a water distribution network, WAMAN (WAter MANagement). The system aims to meet several concerns of modern water utility managers as it attempts to formalize operational and management experiences, and provides a frame work for assisting water utility managers even in the absence of expert personnel. Expert System incorporates a dynamic knowledge acquisition system driven by simulated runs of a hydraulic model, suitably calibrated and validated for the given water utility. The Expert System uses CLIPS ( Artificial Intelligence (AI) tool developed by NASA's Software Technology Branch ) and integrates computational platforms such as MATLAB, open source GIS, and a Relational Database Management System (RDBMS) working under the umbrella of a common User Interface.
Key Words: CLIPS, EXPERT SYSTEM, MATLAB, GIS, RDBMS
1. Introduction
Difficulties in managing a typical water supply system arise on account of (i) absence of a well defined policy framework, (ii) difficulty in gathering information that is coherent and objective because, as is often the case, information is based on individual perception and experience, (iii) complexity of a typical water supply system on account of a variety of control mechanisms, and (iv) frequent changes in the network topology [Leon et al.(2000)]. These reasons pose great difficulties in efforts to develop mathematical models of such inherently complex systems [Walski(1993)]. Further, municipal water distribution networks (WDN) are seldom new. As a result of the natural process of ageing, and with frequent interventions necessitated by our perceived need to meet various performance goals, water supply networks undergo physical changes that significantly impinge on their hydraulic responses. Often, these impacts result in performance levels that are well below expectations. Additionally, WDN are managed by experts, who, over the years of their association and responsibility, acquire an empirical knowledge of the system and, characteristically, this knowledge remains largely confined to their respective personal domains. In the event of any new information and/or emergence of a new problem, these experts apply simple heuristics to design corrective measures and cognitively seek to predict network performance. Understandably, therefore, the assurance of a satisfactory response of the study network to suggested interventions is often based more on hope rather than on a validated belief. In the present day scenario, managers of water utilities face numerous difficulties on account various reasons such as i) Expert scientific knowledge is not readily accessible; ii) Expert scientific knowledge is not available in user friendly manner; and iii) The tacit, undocumented expertise required for operation of a water supply system may be lost when experienced personnel leave due to retirement or transfer.
defined domain during a problem-solving process. This type of program is designed to assist in solving problems that require the skill and expertise of a human, by the application of heuristic rules of thumb. It is generally referred to either as a knowledge-based system (KBS) or an Expert System [Chau 2004].
One of the major advances in computer science emerging from AI is the development of computer systems which separate the underlying knowledge from the procedural part of the system, thus allowing knowledge to be stored and edited without changing a single line of program code. These systems are called Knowledge Based Systems (KBS). Researchers, since then, have used different Artificial Intelligence (AI) tools such as LISP, PROLOG, Smalltalk and ObjTalk etc. In the field of water management some important application of Expert System may include EXPLORE [Leon et al., (2000)] ; OASIS [Goforth and Floris, (1991)]; CRITQUING Expert System [Shepherd and Ortolano, (1996)]; IITWSEXP [Khosa et al., (1995)]; Expert System treated water distribution [Bunn et al.,( 2001)];Network Management System For Water Distribution System [Raghvendran et al, (2007)]; Intelligent Control System For a Municipal Water Distribution Network [Chan et al., (1999)].Review of literature further reveals that presently available expert systems have an extremely limited scope of applications and are confined to only those specific tasks that they are designed for and, it follows, are unable to reason broadly across fields of expertise. These expert systems employ static knowledge domains without the ability to store newer facts and, therefore, unable to learn.
Present study attempts to develop an Expert System (called WAMAN) that incorporates a dynamic knowledge acquisition system driven by simulated runs of a hydraulic model, suitably calibrated and validated for the given water utility. The Expert System developed uses AI language tool CLIPS (short for C Language Integrated Production System). CLIPS is a forward-chaining rule-based language that resembles OPS5 and ART, other widely known rule-based development environments The Expert System integrates computational platforms such as MATLAB, open source GIS, CLIPS (an expert system tool developed by NASA’s Software Technology Branch), and a Relational Database Management System (RDBMS) working under the umbrella of a common User Interface. The User Interface has been designed as a PC based application using Visual Studio .Net programming language.
2. Objectives and primary waman architecture
The Expert System aims to meet several concerns of modern water utility managers as it attempts to formalize operational and management experiences, and provides a frame work for assisting water utility managers even in the absence of expert personnel. The scope of work can broadly depicted as shown in figure 1.
Ex ist in g W D S Know le d ge Ba se
H y dr a ulic M ode l
Ca libr a t ion of H y dr a ulic M ode l
Use r
I nt e r v e n t ion & Que r y Sim ula t ion Ru n
Of Ca libr a t e d H yd. M ode l
Ex pe r t Sy st e m
Em e r ging Sce na r ios
Ad vice / W a r ning / Su gge st ion s
Fig.1. Objectives of Proposed Expert System
Fig. 2. Architecture of Expert System -WAMAN
As shown in figure 2, the main components of WAMAN are:
2.1.Relational Data Base.
Before a model of WDN is attempted, data pertaining to the components of WDN must be collected from different sources. Data should be collected and stored in a format that is readily available to modellers, planners and decision makers. The main advantage of using a database is fast and efficient data retrieval. A database helps organize your data in a logical manner. Database management systems are fine-tuned to rapidly retrieve the data you want in the way you want it. Databases also enable you to break data into specific parts. Relational databases have the further advantage of allowing in specifying how different data relates to each other. In the present study attempt has been made to create a Relational Data Base Management System (RDBMS) for WDN using Structured Query Language (SQL).
2.2.Hydraulic Model and Calibration of the Existing WDN.
The WDN infrastructure consists of basic distribution elements and advanced devices. The basic elements are tanks, nodes, pumps, valves and pipes. All networks will have these basic elements. The advanced devices include sensors. A water distribution model is a mathematical description of a real world system. Reservoirs, Tanks and Junctions are modelled as nodes whereas pipes, valves and pumps are modelled as links. The mathematical representation of the physical network of Hydraulic Model is then solved using some Hydraulic Network Solver (HNS) to predict the system heads and flows under different operating conditions. The study aims to develop a hydraulic model of the existing WDN and HNS for generating status under different operating conditions. The HNS is proposed to be developed in MATLAB platform based on Todini and Pilati [Todini and Pilati 1988] improved gradient method.
The aim all previous network calibration methods [e.g., Ormsbee (1989) and Lansey and Basnet (1991)] was to obtain the best network operating parameters which minimize the difference between observed measurements and computed results. Such methods do not take into account that initial roughness values are fairly accurate estimates determined from pipe age curve or the standard charts and curves [e.g., California Section Committee 1962; Committee (1935); Lamont (1981); Williams and Hazen (1933)]. Ironically, such available information is not exploited while solving the calibration problem [Greco 1999].
2.3. Artificial Intelligence tool.
Review of literature further reveals that presently available expert systems have an extremely limited scope of applications and are confined to only those specific tasks that they are designed for and, it follows, are unable to reason broadly across fields of expertise. These expert systems employ static knowledge domains without the ability to store newer facts and, therefore, unable to learn. The present study employs a dynamic knowledge domain that is designed to be self learning as it accommodates newer knowledge for future guidance. Present work integrates a public domain Rule-based AI language, C Language Integrated Production System (CLIPS), with SQL. CLIPS is a complete environment for developing expert systems and includes features such as an integrated editor and a debugging tool. CLIPS is a public domain language (C Language Integrated Production System) developed by Software Technology Branch (STB), NASA/Lyndon B. Johnson Space Center. CLIPS is a complete environment for developing expert systems and includes features such as an integrated editor and a debugging tool. A schematic of CLIPS is presented below as (figure3) and provides the following basic elements of an expert system: i) Fact-List and Instance-List: Global memory for data; ii) Knowledge-Base: This is the rule base and contains all rules; iii) Inference Engine: Controls overall execution of rules.
Fig. 3. Basic Elements of CLIPS
The 'Working Memory' module of CLIPS consists of a Fact-List, an Inference Engine with Agenda for a particular rule execution and Knowledge Base. Together these interact with a User Interface. (figure 4) shows certain facts asserted in the working memory which sets the agenda for firing of specific rules. (figure 4) also shows a sample rule written for the simulation module of WAMAN.
Fig. 4. Working Memory with resident Facts and Rules
2.4. Mathematical capability.
Development of Expert Support System for management of Water Distribution Network stresses the need for complex computational capabilities of hydraulic network solver. The required capability has been provided with integration of MATLAB with the Expert System (ES). MATLAB has been integrated with ES with stand-alone COM components created by MATLAB compiler. Each of the COM components is designed to perform desired computations using data from SQL RDMS and to report the results to .Net environment
2.5.Spatial data manipulation task
GIS software provides general tools for performing spatial-data-manipulation tasks. These tools are becoming a significant element in the modeling of spatial processes because different applications can share the same GIS data base describing the area under study [Evans, et al, (1993)]. MapWindow is an open source geographic information system (GIS) and an application programming interface (API) built upon the Microsoft Dot Net Framework 2.0. The software, distributed under the Mozilla Public License (MPL), supports manipulation, analysis, and viewing of geospatial data in many standard GIS data formats. MapWindow functions as a mapping tool, a GIS modeling system and a GIS application programming interface (API).
2.6. Integration under common UI.
The User Interface (UI) is a place where interaction between human and machine occurs. It allows the user to manipulate a system and/or allows the system to indicate the effects of the users’ manipulation. With advancement of computer science, the use of Graphical User Interface (GUI) has increased rapidly. To develop an ES, as explained above, the capabilities of platforms such as MATLAB, CLIPS, GIS and SQL are required to be integrated under a common umbrella. The common umbrella has been chosen as Visual Studio.Net which is capable of integrating the above platforms through Component Object Model, or COM
3. Secondary process architecture of waman
The expert decision support system, WAMAN, has two parallel and independent processing streams, one for an existing network (i.e. a network already incorporated on WAMAN) and the other for a new network. The flow chart of these processing streams is presented below as (figure 5).
Fig. 5. Processing streams of ES-WAMAN
5.1 Existing network processing stream
Fig. 6. Snapshot of Existing stream of WAMAN
3.1. Calibration Module
A formulation is developed for the automatic calibration of an existing WDN by using latest heuristic algorithm -Ant Colony Optimization (ACO). The age of the WDN is used as prior information to guide optimal solution in ACO. This paper presents an enhancement in the existing ACO formulation for automatic calibration of an existing water distribution system. The calibration is limited to the task of deriving seemingly reasonable estimates of internal roughness values of pipes of an existing network. The enhancement presented uses prior information about the age of the network as a heuristic for the foraging colony of ants
The schematic of Calibration module is shown in (figure 7). Two sets of observed values of flow (Q) in pipes and head (H) at different loading conditions are taken as input parameters. WDN is calibrated using ACO with one set of observed boundary conditions and then validated with other set of boundary conditions. The calibrated network is saved in RDBMS for further use.
Fig. 7. Calibration Module of WAMAN
In water utility practices, Hazen-Williams C-factor is commonly used as a parameter to represent internal roughness of water mains and, by extension, their carrying capacity [Walski et al. (1988)]. Walski et al (1988), presented Eq 1, to estimate Hazen-William’s roughness coefficients.
CHW = 18.0 – 37.2 log (X) (1)
Where, X= (e0 + at)/D and D is the diameter of pipe, ‘a’ is the roughness growth rate and‘t’ is the age of the pipe in years.
The relationship between the annual roughness growth rate, ‘a’, and the Langelier saturation index (LI), (with the latter controlled by factors such as the pH of water, its alkalinity, and calcium content), can be represented as Eq 2 [Lamont (1981)].
(4.08 0.38 )
10
LIThe snopshot of the calibration module of WAMAN is shown in figure 8.
Fig.8. Screen shot of Calibration Module of WAMAN
As shown in figure 8, for calibration of existing WDN , user enters the observed datasets for measurement of link flows and junction heads for two different operating conditions ( Demand at nodes, reservoir heads or valve and pump settings). The data pertaing to WDN resides in SQL server and contains information about WDN components like pipes( Id, Starting node, ending node, length, diameter and age etc.), demand nodes ( Id, demand, elevation) etc. Before proceeding to calibration, user is required to select the type of attack ( i.e. rougness growth on inside of pipe which can be SLIGHT, MODERATE, APPRECIABLE or SEVERE).
System parameter estimates can be improved by increasing the number of measurements of nodal pressure heads from different loading conditions or by reducing the number of parameters. Towards this goal, parameterization involves organization of the network into non-overlapping clusters of pipe elements with each such cluster being assigned a single common value. Criteria for selecting pipe groupings include (i) pipe age and material; ii) pipe diameter; iii) relative locations; and iv) critical pipes that affect nodal heads [Mallick et al (2002)].
Based on user’s choice of type of attack, the roughness growth rate ‘a’ is computed using equation 2 and then depending upon the age of the pipe the value of Hazen-William’s coefficient are computed for each pipe. The system then groups the pipes depending upon the diameter, age and material. The WDN is then calibrated using Ant Colony Optimization algorithm and calibrated C-value is stored in the database. As a case study, the Anytown network was used and calibration results of the network are presented in table 1.
Table1. Prior estimates of Hazen-William’s C-values and calibration results for Anytown network
Group no.
Diameter
(mm) Age
(yrs.)
Number of pipes
Prior estimate for C- factor
Calibrated C-value using WAMAN
1 406 30 4 81.7 82
2 305 20 3 83.5 83
3 305 30 5 77.2 78
4 254 10 5 91.1 91
5 254 20 9 80.7 81
6 203 20 4 77.2 78
7 203 0 5 130 130
3.2. Simulation Module
organizational constraints. Often, the management of water utilities in mega cities is also facing problems of gradual influx of people. These constraints force the management to make designed or un-designed interventions like: i) Bring new areas under command of the existing network; ii) Add or delete some demand nodes; iii) Change demands at nodes; iv) Adding/ deleting/ upgrading existing pipes;v) Change status of Valves (Open/ Close); vi) Add or remove PRV; vii) Adding/ removing/ upgrading existing Pumps.
Managers of water utilities design most of their interventions and other decisions based on intuition, rule of thumb heuristics and trial and error. These operative decisions are often vague and lack objectivity and consistency. The expert system, therefore, has been designed to Warn/Advice/Suggest network managers about the possible impact of their possible interventions on the performance of the network. The working of the simulation module of WAMAN can be schematically shown as per (figure 9).
Fig. 9. Schematic of Simulation Module of WAMAN
The Water Distribution Networks (WDN) are managed by experts, who, over the years of their association and responsibility, acquire an empirical knowledge of the system and, characteristically, this knowledge remains largely confined to their respective personal domains. In the event of any new information and/or emergence of a new problem, these experts apply simple heuristics to design corrective measures and cognitively seek to predict network performance. The Simulation is designed to this intervention of the Expert and save the results as possible scenario. As shown in figure 9, the possible intervention of the user like changes in pipes (addition/deletion/update) , demand node, source node, valves etc is one complete scenario. If the proposed changes, match the earlier tried similar scenario then the inference engine of CLIPS issues massage that similar scenario exists and prompts the user to view the scenario or proceed further. The details of the scenario are shown to the user as shown in figure 10.
Fig. 10. Details of earlier existing scenario
3.3. Daily Diagnostics Module (DDM)
The module has been developed purely from the point of management of WDN by unskilled/novice operators.The screen shot of this module has been shown in Figure 11 The Module has three sub-module
Fig. 11. Daily Diagnostic Module of WAMAN
3.3.1. Sub-module-I
For a given status of source nodes, nodal demands, pump status, electrical scenario , the Daily Run Module solves the given pressurized WDN and stores the corresponding link flows and nodal heads in the data base with date and time slot identifier. Thus,through this sub-module, the details of link flows and nodal heads can be retrieved for any past date and time. the status of WDN on some previous date and time.
3.3.2. Sub-module-II
This sub-module assists a network manager for ascertaining the status of the WDN for given set of input parameters. The input parameters are used by network solver to compute flow in each pipe and pressure at each head of the WDN. Rules have been written in the knowledge base of CLIPS which compare nodal heads at each node with the ideal working condition of the WDN; compare the heads at start node and end node of each link and compare with ideal conditions in the memory of the CLIPS. The CLIPS Inference Engine takes action and issue warning/ advice like i) Possible leakage in some pipe; ii) Critical flow in pipe/ head at node in some segment etc. The schematic of this sub-module is shown in figure 12.
I
II
Fig. 12. Schematic of Sub-module-II of DDM
3.3.3. Sub-module-III
A WDN provides water services to customers under normal and abnormal conditions. It is important for water utilities to be informed to what degree or level that a system is able to supply its customers for given set of input system parameters. This module of DDM is designed to manage consumer complaints. This module uses backward chaining process in the Inference Engine of CLIPS. Whenever a complaint is received, it identifies to which component of WDN the consumer is attached. Then using backward chaining mechanism it identifies possible cause of the complaint and suggests suitable measures to the network manager to overcome the complaint. . The schematic of working of this sub-module is shown in (figure 13).
Fig. 13. Schematic of Sub-module-III of DDM
4. New network processing stream
and Demands etc. is mapped on to the RDBMS, (SQL), in the form of tables while maintaining its compatibility with CLIPS, MATLAB, and MapWindow that constitute the other component platforms of WAMAN. Following prescribed pre-processing, the data is imported through Existing Network stream for further processing. The screen shot of new network processing stream is shown in (figure14). This module can load the base map in JPEG format over which shape files corresponding to different elements of WDN ( Pipes, nodes, valves etc) can be created. With creation of shape files, this module, creates all the database tables required for computation and storage of results.
Fig. 14. Screen shot of new network processing stream of WAMAN
5. Conclusion
The architecture of WAMAN has been dictated by the perceived challenges that managers of large water utilities have to contend with. The primary objective for the development of WAMAN is to integrate spatially distributed data pertaining to a typical water distribution network with any generic geographic information system in order to facilitate network data archival and retrieval. Furthermore, the proposed structure is seen to facilitate specialized tasks such as network modeling including its calibration. WAMAN is also designed to perform other routine tasks such as directed simulation runs required to monitor a network as well as to generate knowledge regarding network performance following any planned or unplanned intervention. These results constitute new knowledge and are transferred dynamically to the knowledge domain of WAMAN for future guidance. With development of WAMAN it has been established that it is possible to integrate complex descriptive platforms such as CLIPS, MATLAB, and OPEN SOURCE GIS under a common platform to make more robust applications. WAMAN has separate user complaint management system and can guide network managers while suggesting possible corrective measures in case of specific consumer complaint. WAMAN uses a RDBMS and,therefore, can easily be developed into a SCADA based on-line network management System.
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