發(fā)布時(shí)間：2018-01-01 08:20

  本文關(guān)鍵詞：基于改進(jìn)神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)的智能污水處理監(jiān)控系統(tǒng)設(shè)計(jì)　出處：《青島科技大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 污水處理 監(jiān)控系統(tǒng) 神經(jīng)網(wǎng)絡(luò) 溶解氧預(yù)測(cè)

【摘要】：本設(shè)計(jì)以青島市高新區(qū)污水處理廠為現(xiàn)場(chǎng)背景,在對(duì)其工藝流程、設(shè)備設(shè)施作了詳細(xì)地介紹與分析情況下,根據(jù)信息物理融合的思想以及工業(yè)4.0的要求進(jìn)行了監(jiān)控系統(tǒng)的全面設(shè)計(jì)�？傮w設(shè)計(jì)上采用四層信息物理架構(gòu),分為感知交流、融合處理、推送、執(zhí)行四大部分。融合處理部分采用神經(jīng)網(wǎng)絡(luò)智能算法實(shí)現(xiàn)溶解氧預(yù)測(cè),根據(jù)預(yù)測(cè)值調(diào)整送氧量實(shí)現(xiàn)精確曝氣,進(jìn)而優(yōu)化出水水質(zhì);推送、執(zhí)行部分采用多參數(shù)監(jiān)測(cè)實(shí)現(xiàn)設(shè)備平穩(wěn)運(yùn)行,保證系統(tǒng)安全。污水處理過(guò)程采用的是A2/O工藝,其凈化機(jī)理主要是好氧池(即曝氣池)污泥中附著的微生物在適當(dāng)?shù)难鯕鈼l件下,通過(guò)新陳代謝分解污染物,實(shí)現(xiàn)污水的凈化,因此溶解氧的控制最為關(guān)鍵,所以本設(shè)計(jì)提出一種新的基于神經(jīng)網(wǎng)絡(luò)的溶解氧優(yōu)化控制策略。通過(guò)試驗(yàn)以及歷史數(shù)據(jù),獲取在出水較優(yōu)的情況下的曝氣池入水指標(biāo)以及此時(shí)的溶解氧值作為樣本,根據(jù)樣本訓(xùn)練采用粒子群算法優(yōu)化的BP神經(jīng)網(wǎng)絡(luò),最后實(shí)現(xiàn)在不同入水條件下的溶解氧的精準(zhǔn)預(yù)測(cè)。污水處理設(shè)備的平穩(wěn)運(yùn)行由下位機(jī)和上位機(jī)共同來(lái)完成。下位機(jī)設(shè)計(jì)中,首先對(duì)污水處理的各工藝段按照順序配置了監(jiān)測(cè)設(shè)施,全面采集各設(shè)備參數(shù),監(jiān)測(cè)關(guān)鍵設(shè)備的開(kāi)啟以及狀態(tài);然后設(shè)計(jì)了上位機(jī)與下位機(jī)和下位機(jī)與傳感器之間的通訊網(wǎng)絡(luò);下位機(jī)采用PLC作為核心,通過(guò)STEP7對(duì)PLC進(jìn)行編程,采用PID算法進(jìn)行溶解氧控制。上位機(jī)用C#語(yǔ)言開(kāi)發(fā),實(shí)現(xiàn)用戶登錄、實(shí)時(shí)數(shù)據(jù)顯示、超限以及故障報(bào)警、報(bào)表查詢、用戶管理,并且通過(guò)混合編程,將Matlab編寫(xiě)的溶解氧預(yù)測(cè)神經(jīng)網(wǎng)絡(luò)集成在上位機(jī)平臺(tái)里,由上位機(jī)把預(yù)測(cè)出的精確值傳給下位機(jī)實(shí)現(xiàn)溶解氧參數(shù)的設(shè)置。最后通過(guò)系統(tǒng)的現(xiàn)場(chǎng)實(shí)施應(yīng)用,利用檢測(cè)儀對(duì)水質(zhì)數(shù)據(jù)進(jìn)行檢測(cè),與之前出水水質(zhì)數(shù)據(jù)進(jìn)行對(duì)比,證明了本設(shè)計(jì)的優(yōu)良特性。
[Abstract]:This design takes the sewage treatment plant of Qingdao High-tech Zone as the scene background, under the detailed introduction and analysis of its technological process, equipment and facilities. According to the idea of information physical fusion and the requirements of industry 4.0, the overall design of the monitoring system is carried out. In the overall design, four layers of information physical architecture are adopted, which are divided into perceptual communication, fusion processing and push. Four parts are implemented. In the fusion part, the neural network intelligent algorithm is used to predict the dissolved oxygen, and the oxygen delivery is adjusted according to the predicted value to realize the accurate aeration, and then the effluent quality is optimized. Push, the executive part uses the multi-parameter monitor to realize the equipment to run smoothly, guarantees the system safe. The sewage treatment process uses the A2 / O process. The main purification mechanism is that the microorganisms attached to sludge in aerobic tank (aeration tank) can decompose pollutants by metabolism under appropriate oxygen conditions, so the control of dissolved oxygen is the most important. Therefore, this design proposes a new neural network-based dissolved oxygen optimal control strategy, through experiments and historical data. The parameters of aeration tank and the dissolved oxygen value of the aeration tank were obtained as samples, and the BP neural network was optimized by particle swarm optimization according to the sample training. Finally, the accurate prediction of dissolved oxygen under different water entry conditions is realized. The smooth operation of sewage treatment equipment is completed by the lower computer and the upper computer. First, the monitoring facilities are arranged for each process section of sewage treatment according to the sequence, and the parameters of each equipment are collected, and the opening and status of the key equipment are monitored. Then the communication network between the upper computer and the lower computer and between the lower computer and the sensor is designed. The lower computer uses PLC as the core, PLC is programmed by STEP7, and dissolved oxygen is controlled by PID algorithm. The upper computer is developed with C # language to realize user login and real-time data display. Beyond the limit and fault alarm, report query, user management, and through mixed programming, the dissolved oxygen prediction neural network written by Matlab is integrated into the upper computer platform. The precise value of the prediction is transmitted to the lower computer by the upper computer to realize the setting of the dissolved oxygen parameter. Finally, through the field application of the system, the water quality data are detected by using the detector. Compared with the previous effluent quality data, the excellent characteristics of the design are proved.
【學(xué)位授予單位】：青島科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：X703;TP277

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