【摘要】：供水管網(wǎng)優(yōu)化調(diào)度的目標(biāo)是在滿足用戶對供水壓力、流量、水質(zhì)要求的前提下,盡可能降低生產(chǎn)的直接成本和間接成本,并提高生產(chǎn)和輸送過程的安全性、穩(wěn)定性。管網(wǎng)建模仿真是預(yù)測供水管網(wǎng)系統(tǒng)動(dòng)態(tài)工況的有效辦法,有助于實(shí)現(xiàn)對管網(wǎng)的科學(xué)化、現(xiàn)代化管理,有助于實(shí)現(xiàn)供水系統(tǒng)的科學(xué)、調(diào)度優(yōu)化,有助于實(shí)現(xiàn)按需供水、減少損耗和漏損。傳統(tǒng)的供水管網(wǎng)建模采用微觀模型,但在實(shí)際應(yīng)用中存在著一些缺點(diǎn),導(dǎo)致了在供水管網(wǎng)系統(tǒng)的調(diào)度優(yōu)化中無法充分有效地利用該模型。 本文首先利用BP神經(jīng)網(wǎng)絡(luò)建立了城市用水量的預(yù)計(jì)模型。將臨近的24小時(shí)歷史用水量數(shù)據(jù)作為模型輸入,計(jì)算下一小時(shí)的用水量,作為優(yōu)化模型中的用水量約束參數(shù)。城市供水管網(wǎng)模型采用宏觀模型,模型將各個(gè)水廠、泵站的流量作為輸入,測壓點(diǎn)壓力及水廠泵站的出口壓力作為輸出。通過BP神經(jīng)網(wǎng)絡(luò)的訓(xùn)練,可以得到供水管網(wǎng)的模型。利用歷史數(shù)據(jù)來驗(yàn)證上述兩個(gè)模型,結(jié)果表明模型具有較高的精度。 在得到供水量預(yù)計(jì)模型和管網(wǎng)模型后,建立了優(yōu)化方程。以供水總成本(電耗、制水成本等)最小作為優(yōu)化目標(biāo),并以供水量、測點(diǎn)水壓等作為約束。采用啟發(fā)式算法解決該優(yōu)化問題,首先將優(yōu)化問題通過罰函數(shù)法轉(zhuǎn)化為無約束問題,然后通過遺傳算法求解。 最后,通過實(shí)際數(shù)據(jù)來測試優(yōu)化模型,結(jié)果表明,在能夠滿足供水量、水壓的前提下,優(yōu)化的結(jié)果可以很好降低供水總成本。因此,本文設(shè)計(jì)的供水優(yōu)化調(diào)度模型可以有效的調(diào)整供水方案,為水廠、泵站的運(yùn)行調(diào)度提供理論指導(dǎo)。該模型在未來也可以與SCADA系統(tǒng)有機(jī)的結(jié)合,形成高效的自動(dòng)供水調(diào)度系統(tǒng)。
[Abstract]:The aim of optimal dispatching of water supply network is to reduce the direct and indirect cost of production and to improve the safety and stability of production and transportation process under the premise of satisfying the demand of users for water supply pressure, flow rate and water quality. The modeling and simulation of water supply network is an effective method to predict the dynamic working conditions of water supply network system. It is helpful to realize the scientific and modern management of the water supply network, to realize the science of the water supply system, to optimize the dispatching, and to realize the water supply on demand. Reduce loss and leakage. The traditional water supply network model adopts micro model, but there are some shortcomings in practical application, which leads to the failure to make full use of the model in the optimization of water supply network system. In this paper, the prediction model of urban water consumption is established by using BP neural network. The approaching 24-hour historical water consumption data is used as the input of the model and the next hour water consumption is calculated as the water consumption constraint parameter in the optimization model. The model of urban water supply network adopts macroscopic model, which takes the flow rate of each water plant and pump station as input, the pressure of measuring point and the outlet pressure of pump station of water plant as the output. Through the training of BP neural network, the model of water supply network can be obtained. The two models are verified by historical data, and the results show that the model has high accuracy. After obtaining the water supply prediction model and the pipe network model, the optimization equation is established. The minimum total cost of water supply (electricity consumption, water production cost, etc.) is taken as the optimization objective, and the water supply and water pressure are taken as constraints. The heuristic algorithm is used to solve the optimization problem. Firstly, the optimization problem is transformed into an unconstrained problem by penalty function method, and then solved by genetic algorithm. Finally, the optimization model is tested by the actual data. The results show that the optimization results can reduce the total cost of water supply well under the premise of satisfying the water supply quantity and water pressure. Therefore, the water supply optimal scheduling model designed in this paper can effectively adjust the water supply scheme and provide theoretical guidance for the operation and scheduling of water plants and pumping stations. The model can also be combined with SCADA system in the future to form an efficient automatic water supply dispatching system.
【學(xué)位授予單位】：華東理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TU991;TP183

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