English  |  正體中文  |  简体中文  |  Items with full text/Total items : 66984/66984 (100%)
Visitors : 22974970      Online Users : 416
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version


    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/78042


    Title: 改良式A2O系統厭氧槽與缺氧槽系統動力模式之發展與建立;Development and establishment of system dynamics model of anaerobic tank and anoxic tank in modified A2O system
    Authors: 汪虹妙;Wang, Hong-Miao
    Contributors: 環境工程研究所
    Keywords: 實廠改良式A2O系統;系統動力模式;污水處理系統自動控制;類神經網路;Modified A2O systems in real plant;System dynamics model;Sewage treatment system of automatic control;Artificial neural network
    Date: 2018-08-24
    Issue Date: 2018-08-31 15:09:51 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 實廠改良式A2O系統係由厭氧槽、缺氧槽、好氧槽以及薄膜生物反應槽(Membrane bioreactor, MBR) 所串聯而成之污水處理程序處理都市生活污水,本研究對象為厭氧槽與缺氧槽兩個反應槽,由於污水處理系統設計多使用穩態數學模式,污水廠操作方式皆是採用固定式操作與控制方法,,加上人工水質檢測與量測方法需要耗費大量的人力物力及財力,無法應付進流水質水量隨著時間變動的動態污水處理系統。又無法對微生物本身菌群組成與微生物對各項物質能量轉化過程進行量化,就更難以掌握微生物實際的反應途徑與反應狀態,面對整個處理系統呈現高度複雜的關係,人工智慧控制系統應用在非穩定的動態系統比起傳統控制系統更兼具全面性與人性化。因此,本研究主要為將可量化非結構性問題的系統動力模式,結合具有自適應性學習能力、非線性映射能力和容錯能力的導傳遞類神經網路,整合為改良式A2O系統厭氧槽與缺氧槽系統動力模式,以即時找出改良式A2O系統厭氧槽與缺氧槽問題的原因,期望能提升系統出流水質的穩定度,以做為後續系統操作控制與維護管理改良式A2O系統正常運作之決策工具。整體而言,本研究利用導傳遞類神經網路建立系統物質反應速率推估模式發現6月份的3組循環水樣代入改良式A2O系統厭氧槽與缺氧槽系統動力模式,其模擬出流水質的驗證結果得到相對誤差百分比0.27%~39.55%為最大誤差範圍,而8月份的5組驗證組得到出流水質驗證結果平均相對誤差百分比高達50%,由此可知,本研究所建立之改良式A2O系統厭氧槽與缺氧槽系統動力模式於連續性水質檢測與監測條件下較具有可行性。並透過系統動力模式找出問題原因,並進行不同條件下的模式模擬,以擬定系統問題的操作與控制策略。;The modified A2O system is connected by anaerobic tank, anoxic tank, aerobic tank and membrane bioreactor(MBR) to treat urban domestic sewage, and this research object is anaerobic tank and anoxic tank. Since the operators of sewage treatment plant are used to take fixed operation and control method, and the sewage treatment system design is used to the steady state mathematical model, it cannot cope with the dynamic sewage treatment system with the influent water quality and quantity changing with time. This study will integrate the system dynamics model of non-structural problems, combined with the adaptive learning ability, nonlinear mapping ability and fault tolerance of the artificial neural network, integrated into the system dynamic model of the modified A2O system can find out the cause of the anaerobic tank and the anoxic tank problem. It is expected not only to improve the stability of the outflow water quality, but also to improve the normal operation of the modified A2O system for subsequent system operation, control, maintenance and management. The anaerobic tank and anoxic tank system dynamic model in modified A2O system established in this study is more feasible under continuous water quality testing and monitoring conditions. And we through the system dynamic model find the cause of the system problem and simulate the model under different conditions, to develop the operation and control strategy of problem in modified A2O system.
    Appears in Collections:[環境工程研究所 ] 博碩士論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML107View/Open


    All items in NCUIR are protected by copyright, with all rights reserved.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback  - 隱私權政策聲明