Study On Decision Support System Of Optimal Control Of Breakdown State Of City Water Supply Network

The water supply system, one of the most important fundamental facilities of city, has the attribute of big investment, high operation expense, and difficult maintenance, and it is a large, complex and multiple-unit engineering system, which supplies water to modern cities and companies.Since the reform and opening, many domestic specialists and scholars have explored and attempted on optimal control and management of water supply system, and some big cities have established water supply information management and control system step by step. Our country, comparing with developed countries, has some disparities among study on water supply management and control, but distinct progress has been made. However, the study on management and control decision of breakdown state of city water supply network falls behind.The paper aims at the status in operation of city water supply system and the imminence of solving this question. Using the theory of system optimization and management and based on summarizing and analyzing domestic and abroad research findings, relevant problems confronted in the course of control policy decision-making for water supply were studied deep. In order to reduce the affect range and degree of breakdown state furthest and contribute to study on management and decision of city water supply information, author strives for correctly decision and optimal operation of breakdown state.The daily and hourly prediction of water consumption is an important foundation in water supply system. Therefore the methods and results of the prediction have direct influence on optimal operation and benefit of water supply system. Before thorough research on the short-term prediction of water consumption was made, all kinds of popular methods for short-term prediction to water consumption of cities were studied and compared. Nowadays, the methods and models used domestically have the problems of time-consuming, poor prediction precision and bad stability. In this paper BP ANN (Artificial Neural Network) was used to establish prediction model of water consumption, which has good precision and reliability. Commonality and aging characteristic are only up to extensive data and its accuracy. The results of verifying to an example showed correct prediction could be made not only to normal water consumption but also to breakdown water consumption, which can supply reliable operation parameter to operator. The prediction using ANN gave a better result.The domestic and abroad methods simulating the operation state of water supply network were summarized in this paper. Macro-models of network performance were presented by using the method of network structure and the method of ANN respectively. Based on the theory of macro-model building, considering the characteristic of domestic water supply network and with bulk pilot calculation and analysis, breakdown state model of water network was put forward. At this time, it can also be modeled using the same form as the macro-model for normal supply of network performance, i.e. macro variables were fixed and assigned values of breakdown state and a group of altered model coefficients was used to describe the alternation of network structure characteristics and state parameters because of breakdown of network. According to this, macro-model of breakdown state was built and correspondingly calculation program was made. The results showed that this kind of macro-model could simulate the main hydraulic parameter of breakdown state of network well and the precision and run-time of calculation were satisfied.Optimal control of water supply network of breakdown state is same with that of normal state. According to real-time variation of state variables in all monitor points and on the premise of meeting the demand to pressure, optimal operation state was determined, which made energy consumption minimize and pressure distribute evenly and attained best economic benefit.Breakdown state of water supply network in this paper means that serious accident occurs in the network...