IZABELA ROJEK
Uniwersytet Kazimierza Wielkiego w Bydgoszczy Instytut Mechaniki rodowiska i Informatyki Stosowanej
Summary
The paper describes a concept of an integrated computer aided decision support system with a knowledge acquisition module for the water supply and sewage system in a city. The system is designed in compliance with the rules of designing informa-tion systems. The paper also presents a descripinforma-tion of the branch database assuming an integrating role for four subsystems: water intake station, water treatment station, water supply and sewage duct system and a water treatment plant. The required sys-tem functions were presented with specification of problems the syssys-tem should be able to solve. Additionally, a data mining system for problem solving was briefly characterised.
Keywords: integrated computer aided decision support system, knowledge acquisition module,
water supply, sewage system, data mining system
1. Introduction
An idea of an integrated, computer aided decision support system for the city water supply and sewage system was formulated subsequent to observations that the objects managed by a typi-cal water supplying company do not function separately but belong to a one, dependent system. The central function in this very system is performed by a water supply network, the load of which changes in time, thus influencing the work of pumping systems in the water intake station, the hydraulic load of the sewage system, and finally the quality of work of the water treatment plant. Accurate load forecasting and operation of the water supply system will allow energy efficient operation of pumps at the water intake and effectively manage the technological process in the sewage treatment plant by preparing it for a certain load of sewage and pollution [3]. An integrated computer aided decision support system for the city’s water and sewage network is now being formulated with the aid of the research and finance project of Ministry of Science and University Education13.
The integrated city’s water and sewage system will comprise: water intake station, water treatment station, water supply network, sewage network, and sewage treatment plant. It is as-sumed that these four modules will be cooperating and, if need be, functioning separately. The integrating centre for these systems is a branch data base with technical, technological and opera-tion data on all objects of the water supply and sewage system. The system will use mathematical
models of the analyzed objects used for simulation calculations and estimating, as well as optimi-zation algorithms used for facilitating the functioning of objects and generating scenarios for tak-ing actions in the regular operation or in the cases of failure [3].
A knowledge acquisition system will supplement this integrated computer system, and will uncover knowledge, from the branch data base, needed for the decision making process.
2. Branch data base structure description
The structure of the branch database for the water supply company comprises the intake and water treatment station, water supply system, sewage ducts, and a sewage treatment plant. Creation of a database means defining the objects in specific systems within the water supply and sewage network and their attributes. Such a database could, in the future, become a standard for the water supplying companies which nowadays create their own databases for each system, within the wa-ter supply and sewage network.
While designing a database the following problems should be solved: • Which data interest us?
• How are they represented? • Which data will be stored?
At separate stages of database design the following should be done:
• Conceptual data modelling through terms – analysing informational needs in the us-ers, a diagram of entity relationships, or the conceptual model should be created. • Designing logical structures – having chosen the standard data on the level of logic
(relational data model) the conceptual model should be converted to logical model, and logical structures unified.
• Designing physical structures, defining the data storage system, and data mining mechanisms.
In the case of a branch data base, a distributed data base should be created, hence, having completed the draft of (relational model for the data base, relation fragmentation and allocation of fragments and replications should be performed.
For instance, the data base for the subsystem of water supply network should comprise the fol-lowing definitions [2]:
• Definitions of types of the listed objects: • water supply pipeline
• central node: source (pump station, pumping station, tank), user system (user – intake), assembly node, measuring node
• network apparatus: pump, bolt, reducer, a non-return valve • Definitions of object attributers :
• conductor: length, diameter, material, age • node: pressure and water demand, coordinates • pump: type, characteristics, producer
• tank: dimensions
• bolt, reducer, valve: on/off mode and object characteristics
Database with regard to sewage treatment subsystem should contain the following data [2]: • sewage treatment (biological, mechanical),
• raw sewage parameters: inflow intensity, sewage composition, concentration of elemental fractions, temperature, values of pH, BOD5, COD,
• parameters of sewage treatment plant operation – oxygen concentration in tanks with activated sludge, degree of activated sludge recirculation, degree of sewage cir-culation.
The branch database should be developed with regard to water supply and sewage network and the intake and treatment of drinking water: data on pumps, intake and water treatment parame-ters.
3. Creating of application of an integrated water supply and sewage system
These complex water supply and sewage system will allow solving various problems. It will enable the energy efficient operation, in the water intake station, of complex pumping systems in line with the estimated load of the water supply network. The problems of managing the water supply system are mainly related to its structure and parameters, and energy efficient usage ensur-ing proper distribution of quality water, optimization of project work for the development of the network, tracing failures, drawing of overhaul plans while acknowledging possible failures, in-vestment and usage costs. In the case of the sewage network, the basic problems refer to operating the pumping stations for sewage and drawing of revitalising plans for the network. Managing the input of air in chambers with activated sludge as well as operating the sewage and activated recir-culation is the key issue in the sewage treatment plant. These operations should acknowledge the estimated inflow of raw sewage and the pollution therein contained.
With a view to designing user applications the functions to be used there should be defined. Definition of functions will enable precise estimation of functional needs in the organisation, un-derstanding of users’ needs prior to designing the application and implementing the system, as well as obtaining clear description and effective communication mechanism.
Next to the functions, events should be defined. Definitions of events stand for the develop-ment of the functional standard of the company. Events rationalise the reasons for undertaking certain actions – performing certain functions. Moreover, relations between functions are shaped. These relations allow the presentation of cause-effect relations in the functioning of the company.
Detailed model of functions, events and relations forms the basis for designing user applica-tions contained in the integrated water supply and sewage system.
For instance for the subsystem of water supply network the following functions may be distin-guished [2]:
• visualising of water supply network in graph form • simulation of network operation
• network parameter optimization
• operating pumps and filling up the equalising tanks
Additionally, a need for creating functions for additional tasks shall appear: • incremental update of data from the urban geodetic surveying
• correction of network graph topology
• generating network hydraulic nodes (hydraulic graph)
• export and import of data from and to the Branch Data Base using the buffer files. Another function is the creation of a monitoring system for the network. Water supply net-work monitoring will contain real and current information about net-work and netnet-work state. Measures
obtained from monitoring will be used for verification and calibration of the hydraulic model of network.
Monitoring function may be divided into the following sub functions:
• choice of points for measuring (minimal number – maximum information) • choice of measuring devices (water current metre, pressure metre)
• assembly of measuring devices (measuring manholes, switchboards, power) • data transfer choice (telemetry, cellular phone, radio)
• setting of rules for data transmission (continuous, periodical) • choice of storing and visualising program
The next function is the one using hydraulic model for measuring water current in pipes and pressure in network nodes.
Next follows optimization. Two tasks of optimization have been distinguished: designing + network operation, with two designing tasks: reconstruction network development.
For the sub system of sewage treatment the following functions are distinguished [2]:
• functions as processes taking place in the treatment plant (hydrolysis, nitrification, denitrification, dephosphatation, increase and dying out heterotrophic and autotro-phic bacteria),
• functions managing the sewage treatment plant
• functions as basic processes: mixing, sedimentation of sludge in a settling tank, re-duction of organic and nitric pollution in the aeration chambers.
• Operating the treatment plant and issuing prognoses • Functions of system operation:
• process parameters measured systematically • measuring are recorded in the branch data base
• concentration of raw sewage inflow + composition and concentration of pollu-tion given by neural estimating models
• parameters operating the process are calculated according to neural operating model
• calculated operations verified by means of simulation calculations using process physical model
4. Knowledge acquisition system
Knowledge can be overt and covert. By overt knowledge we mean pictures, words, and data. This knowledge leads to expertise, is easy to articulate and copy. The covert knowledge however, is the experience, know-how, intuition. This one is difficult to convey or articulate, transfer or copy but it gives us advantage over competition [5].
That is why the covert knowledge has been an interest of scientists, who, while using different methods and computer techniques, attempt to store it in computer systems so that it can be recy-cled or modified.
An attempt to transfer the covert knowledge into the computer system shall be performed in the knowledge acquisition system, using the traditional and formalised methods: artificial neural networks and decision trees induction method [4].
Data concerning the system objects shall be obtained from norms, catalogues, sources, com-pany documentation and the existing data bases.
Traditional acquisition method consisted in talking with and observing an expert. In this very method, the knowledge engineer plays key role by observing the expert solving a particular prob-lem, analysing knowledge on the basis of the expert’s instructions, including real examples solved by the expert and by gathering knowledge on the basis of analogies. Then the knowledge given by the expert is selected and ordered in the form of expert system rules, so that it is amenable to stor-ing and effective use by a computer.
In the case of formalised methods however, depending on the type of data, neural networks or decision trees induction shall be used.
While using a neural network, the knowledge acquisition process is shortened. The knowl-edge engineer does not need to enter rules into the expert system manually. The taught neural network, having the entry parameters introduced, gives a solution which may then be processed by the expert system. The data acquired by the functioning of a neural network were transferred to the expert system with the help of which the user could take decisions in the decision making system. The paper proposes a method of supervised learning creating symbolic knowledge representa-tion (inducrepresenta-tion trees algorithm). In the case of symbolic data, decision trees are more useful and easy to use in knowledge acquisition than neural networks due to the fact of presenting data in a symbolic way to which one may then attribute interpretations. This form is clear and easy to un-derstand for people [6]. In the case of neural networks however, no overt symbolic representation of knowledge is obtained. Conversely, internal records of information are formed which are not so clear for people. Decision trees induction method allows bringing closer the qualifying functions with discrete initial values referring to certain terms, decision classes.
Figure 1 shows knowledge acquisition system with the DSS system for aiding decision mak-ing, which assists the work during decision making through the knowledge base, and then transfers the decisions to the data base with a view to constant enriching the set of teaching examples in the knowledge acquisition system.
data base knowledge acquisition D SS System decision support knowledge rules new decisions data mining
system know ledge base
user
Figure 2 data mining system. Knowledge obtained by means of neural networks and decision trees induction will allow optimization:
− in the water supply network: consumption of electricity, pressure, water quality,
− in intake station and water treatment station: consumption of electricity, pump durability, − in the sewage network: consumption of electricity,
− in sewage treatment plant: consumption of electricity, quality of purification, sediment condi-tion.
Data
Knowledge
measuring
data
mathematical
models
optimalisation
algorithms
−
minimal electricity consumption
− minimal error in water pressure
− maximum water quality
− maximum pump durability
− maximum purification quality
− maximum sediment condition
decision tree neural network
Data
mining
Fig. 2. Data mining system
5. Conclusion
National water supply companies are usually government based partnerships governing the city sewage and water supply networks consisting of a water intake station, water supply network, sewage network and a sewage treatment plant. These objects, usually separately administered, are in fact interconnected in one system; however the functioning of each object influences the work of other. Present state of computing in water supply companies leaves a lot to be desired. The system is usually modernised by installing incomplete monitoring systems in water supply net-works and water treatment plants or drawing numerical maps of water supply netnet-works. Mathe-matical models for calculating hydraulic water supply systems are rarely used. Operating the pumping stations at water intakes and water supply networks as well as coordinating the filling up of the equalizing tanks and aeration of sewage is usually performed by automatic regulation sys-tems ensuring the keeping of the regulators as set. Companies use no models of sewage networks, sewage treatment plants, estimation algorithms, optimization and operational management, which simplify both technically and financially the operation of these objects [1].
With a view to complete decision aiding, an integrated computer system should be introduced, one equipped both with overt and covert knowledge. That is why the computer aided decision making system in the city’s water supply and sewage network, shall be enriched with an automatic system of knowledge acquisition equipped with neural networks and decision trees induction.
Bibliography
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IZABELA ROJEK
e-mail: izarojek@ukw.edu.pl
Uniwersytet Kazimierza Wielkiego w Bydgoszczy Instytut Mechaniki rodowiska i Informatyki Stosowanej ul. Chodkiewicza 30 85-064 Bydgoszcz
