多段進流去氮除磷系統穩動態處理及; 控制特性之研究 The characteristics of nutrient removal in multiple stages in enhanced biological nutrient removal process

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题名:	多段進流去氮除磷系統穩動態處理及;控制特性之研究 The characteristics of nutrient removal in multiple stages in enhanced biological nutrient removal process
作者:	周裕然;yu-jan chou
贡献者:	環境工程研究所
关键词:	TNCU-III 程序;動態多段進流;柔性計算原理;soft-computing technology;dynamic loading;TNCU-III process
日期:	2002-09-23
上传时间:	2009-09-21 12:13:04 (UTC+8)
出版者:	國立中央大學圖書館
摘要:	摘要本研究所探討之TNCU-III 程序由厭氧及多段好氧-缺氧槽串聯而成，在無回流好氧硝化液的情況下，藉由階梯進流結合傳統活性污泥程序的方式，可提升總氮去除效率而不造成總磷去除效果的降低。本研究以穩、動態進流的方式進行微生物馴養，探討TNCU-III 程序於不同進流狀態時之營養鹽去除特性，並輔以分子生物技術之微生物鑑定、不同外部碳源添加及柔性計算原理，結合線上水質監測技術，建立動態多段進流提昇總氮去除率之技術與操作準則。穩態實驗結果顯示TNCU-III 程序無須迴流硝化液，並可節省鹼劑使用量，因此大幅降低操作費用，三段式、兩段式及不分流操作皆可滿足出流水總氮小於 15mg/L、總磷2mg/L 之放流水標準。若欲達總氮濃度小於10mg/L、總磷濃度小於0.5mg/L 之出流水質，則所需總水力停留時間約為10 小時，其中厭氧段、好氧段與缺氧段之水力停留時間比約為1.5:5.5:3.0。TNCU-III 程序活性污泥菌相分析方面，不分流操作之活性污泥菌相包括 Proteobacteria、CFB group、Chloroflexi 及Firmicutes 屬；而三段分流操作之活性污泥菌相包含Proteobacteria、CFB group、Chloroflexi 、Nitrospirae group 及 Planctomycetales 屬。兩污泥樣本中皆存在硝化菌、脫硝菌、除磷菌、絲狀菌與化學需氧量降解菌，其中三段分流操作化學需氧量降解菌菌種多樣性高於單段操作，同時兼具除磷與脫硝能力的Acidovorax defluvii 菌種比例亦較高，約為15.2%。此外兩試程操作皆馴養出Eikelboom Type 1851 的絲狀菌，約佔不分流與三段分流操作總菌落數10%與12%。添加外部碳源提升總氮去除之批次實驗結果顯示，醋酸鈉之添加將導致磷的再釋出效應，且較大的C/N 比會造成較高的比釋磷量及PHAs 累積。添加甲醇時，微生物在前15 分鐘可進行脫硝反應，但其後反應槽混合液溶解性化學需氧量增加、變動，同時過濾液濁度增加，實驗終了時無亞硝酸氮的累積。若以醣類作為脫硝碳源，最終總硝酸氮去除率高於醋酸鈉、甲醇之添加約10~25%。因批次實驗（醋酸鈉除外）皆檢測不到反應液正磷酸鹽濃度，但活性污泥中PHAs 卻增加，故推論TNCU-III 程序中存在肝醣蓄積菌，增加脫硝所需之碳源劑量。動態實驗結果顯示因進流量變動發生污泥沖刷、營養鹽未能充分分解利用的現象以前四槽最為明顯，且十二個實驗試程厭氧段C/P 比約在20~70 之間變化，因C/P 變化過大，影響磷蓄積菌除磷之穩定性，故除磷效果較穩態為不理想。水質數據顯示以回流比=0.5、Q1:Q2:Q3=(0.7:0.2:0.1)試程之總氮、總磷去除效果最佳，出流水總氮、總磷濃度可低於7.6 與0.3mg/L。此外即時監測數據顯示厭氧段pH 值可作為基質酸化水解指標，而厭氧段ORP 及好氧第一段殘餘溶氧濃度變化可呈現污水生物處理之負荷變動趨勢，亦可作為動態控制的指標參數，此指標參數於TNCU-III 程序動態操作時發生1~2 小時的時間延遲現象，且出流水殘餘營養鹽濃度或是懸浮固體物濃度變化與進流水流量變動無明顯之一致性。此外本研究結合遺傳演算法與描述營養鹽降解之串聯式類神經網路以建立水質模擬模式，並進行動態操作之最佳化演算與模式敏感度分析，獲致TNCU-III程序動態操作不同時段之最佳分流比及污泥回流比，因水質實驗數據與模擬值間的高度相關性與低均方根誤差，且最佳試程操作之出流水水質總氮濃度較固定進流分流比、污泥回流比操作為佳，因此人工智慧技術可做為EBNR 程序水質模擬及最佳化控制之輔助工具。 ABSTRACT Improvement of nutrient removal efficiency without recycling nitrified liquor was accomplished in this research. A step feed strategy was successfully incorporated into the TNCU-III process for enhancing the nitrogen removal without phosphorus elimination decline. The thesis investigated the nutrient removal characteristics, microbial population, and denitrification behavior of microorganisms by cultivating activated sludge under stable wastewater influent condition, molecular biotechnology, and external carbon source addition, respectively. Moreover, microbial kinetic parameters obtained from continuous pilot-plant operation were used to optimize each reactive volume of TNCU-III process. Under dynamic loading, real-time effluent water quality data acquired from monitoring instruments and soft-computing technology in terms of artificial neural networks (ANNs) and genetic algorithm (GA) were combined to minimize effluent nutrients concentration. In the steady-state experiments, non-, two or three step-feeding strategy resulted in excellent treated water quality, and effluent total nitrogen (T-N) and phosphorus (T-P) concentration were less than 15 mg/L and 2 mg/L, respectively. Results also showed the potential of saving energy or alkaline consumption in TNCU-III process. When three step-feeding strategy and enough hydraulic retention time (10hrs) were implemented, the better effluent water quality was obtained (T-N<10mg/L, T-P<0.5mg/L); meanwhile the ratio of anaerobic-oxic-anoxic retention time was 1.5:5.5:3.0. In the microbial population identification, bacteria of Proteobacteria group, CFB group, Chloroflexi group, and Firmicutes group were present in non step-feeding operation. Additionally, bacteria of Proteobacteria group, CFB group, Chloroflexi group, Nitrospirae group, and Planctomycetales existed in three step-feeding sludge sample. Both two activated sludge samples involved specific species for nutrient removal, carbon utilization, and filamentous bacteria. The diversity of microbial population response for carbon uptake in three step-feeding was higher than that in non step-feeding mode. Filamentous bacteria (Eikelboom Type 1851) existed in both two sludge samples, and the proportion were about 10 and 12%. According to external carbon source addition experiments, sodium acetate addition resulted in phosphate re-release problem. If methanol was used to be the electron donor, microorganisms needed more adaptive time to utilize methanol; meanwhile resulted in carbon breakthrough. When glucose was fed, the final nitrate (NO3-N) removal efficiency was higher than former carbon sources about 10~25%. Moreover, the final PHAs contained within the biomass were more than original level and no PO4-P re-release was observed when methanol and glucose addition, glycogen accumulating organisms (GAOs) might exist in TNCU-III process and increased carbon dosage for NO3-N removal. Furthermore, wash-out effect of mixed liquor suspended solid and nutrients were occurred in the first four reactive zones under dynamic loading. Because of the obvious variation of C/P ratio (C/P=20~70) in anaerobic zone, the activity of polyphosphate accumulating organisms (PAOs) decreased. When TNCU-III process was operated at sludge recycle ratio=0.5 and Q1:Q2:Q3=(0.7:0.2:0.1), effluent T-N and T-P concentration were less than 7.6mg/L and 0.3mg/L, respectively. Additionally, real-time monitoring data demonstrated pH and reduction potential in anaerobic zones and residual dissolved oxygen (DO) concentration in the first aerobic zone well described the variation of organic loading. All the aforementioned parameters could be considered as the indicators in TNCU-III control strategy-making procedure. Moreover, artificial intelligence (AI) technique by means of serial ANNs describing metabolic behavior of microorganisms and GA were successfully integrated into the modeling system. The modeling system simulate the dynamic characteristics of nutrients removal and minimized effluent NO3-N concentration. Due to low root mean squared of error and high correlation coefficient between simulated and experimental data, AI technology offered an alternative approach to simulate and optimized nutrient removal of an EBNR system.
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