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.

185
參考文獻
1. Amman R. I., Ludwig W., and Schleifer K. H. (1995) Phylogenetic identification
and in situ detection of individual microbial cells without cultivation. Microbial
2. APHA, AWWA, and WPCF (1995) Standard Methods for the Examination of
Water and Wastewater, 19th Edition, American Public Health Association,
3. ASCE and WEF, (1992) Design of Municipal Wastewater Treatment plants. ASCE
Manual and Report on Engineering.
4. Baker P. S. and Dold P. L. (1996) Denitrification behaviour in biological excess
phosphorus removal activated sludge systems. Wat. Res., 30(4), 769-780.
5. Barlindhaug J. and Degaard H. (1996) Thermal hydrolysate as a carbon source for
denitrification. Wat. Sci. Technol., 33(12), 99-108.
6. Bond P.L., Hugenholtz P., Keller J., and Blackall L.L., (1995) Bacterial
community structures of phosphate-removing and non-phosphate-removing
activated sludge from sequencing batch reactors. Appl. Environ. Microbiol.,
5), 1910-1916.
7. Burrell P. C., Keller J., and Blackall L. L. (1998) Microbiology of a
nitrite-oxidizing bioreactor. Appl. Environ. Microbiol,. 64(5), 1878-1883.
8. Capodaglio A. G., Jones H. V., Novotny V., and Feng X. (1991) Sludge bulking
analysis and forecasting application of system identification and artificial
neural computing technologies. Wat. Res., 25, 1217-1224.
9. Cech J. S. and Hartman P. (1993) Competition between polyphosphate and
polysaccharide accumulating bacteria in enhanced biological phosphate
removal system. Wat. Res., 27(7), 1219-1225.
10. Charef A., Ghauch A., Baussand P., and Martin-Bouyer M. (2000) Water quality
monitoring using a smart sensing system. Measurement, 28, 219-224.
186
11. Charpentier J., Godart H., Martin G., and Mogno Y. (1989) Oxidation reduction
potential (ORP) regulation as a way to optimize aeration and C, N and P
removal: experience basis and various full-scale example. Wat. Sci. Technol.,
21, 1209-1223.
12. Choi D. J. and Park H. (2001) A hybrid artificial neural network as a software
sensor for optimal control of a wastewater treatment process. Wat. Res. 35(16),
3959-3967.
13. Chou Y. J., Kuo W. L., Huang H. L., and Ouyang C. F.(2001) The characteristic of
nutrient removal in the multiple stages enhanced biological nutrient removal
process by using step-feed approach. 1st IWA Asia-Pacific Regional
Conference, 531-536, Fukuoka, Japan.
14. Chou Y. J., Ouyang C. F., Kuo W. L., and Huang H. L. (2003) Denitrifying
characteristics of the multiple stages enhanced biological nutrient removal
process with external carbon sources. Journal of Environmental Science and
Health Part-A. (Accepted)
15. Chou Y. J., Ouyang C. F., and Kuo W. L. (2003) Feedforward artificial neural
networks for nutrients removal simulation in multiple stages enhanced
biological nutrient removal process. Journal of the Chinese Institute of
Engineers. (Accepted)
16. Chuang S. H., Ouyang C. F., Yuang H. C., and You S. J. (1998) Phosphorus and
polyhydroxyalkanoates variation in a combined process with activated sludge
and biofilm. Wat. Sci. Technol., 37(4-5), 593-597.
17. Comeau Y. Hall K. J., and Oldham W. K. (1988) Denitrification of
poly-β-hydroxybutyrate and poly-β-hydroxyvalerate in activate sludge by
gas-liquid chromatography. App. Environ. Microbiol., 54, 2325-2327.
18. Côté M., Grandjean B. P. A., Lessard P., and Thibault J. (1995) Dynamic
modeling of the activated sludge process: improving prediction using neural
networks. Wat. Res. 29(4), 995-1004.
187
19. Dabert P., Sialve B., Delgenes J.P., Moletta R., and Godon J.J., (2001)
Characterization of the microbial 16S rDNA diversity of an aerobic
phosphorus-removal ecosystem and monitoring of its transition to nitrate
respiration. Appl. Microbiol. Biotechnol., 55(4), 500-509.
20. Deinema M. H., Loosdrecht M. van, and Scholten A. (1985) Some physiological
characteristics of Acinetobacter spp. Accumulating large amount of phosphate.
Wat. Sci. Technol,. 17, 119-125.
21. Dedysh S. N., Khmelenina V. N., Suzina N. E., Trotsenko Y. A., Semrau J. D.,
Liesack W., and Tiedje J. M. (2002) Methylocapsa acidiphila gen. nov., sp. nov.,
a novel methane-oxidizing and nitrogen-fixing acidophilic bacterium from
Sphagnum bog. Int. J. Syst. Evol. Microbiol., 52(1), 251-261.
22. Du Y. G., Tyagi R. D., and Bhamidimarri R. (1999) Use of fuzzy neural-net model
for rule generation of activated sludge process. Process Biochemistry, 35,
77-83.
23. Frick B. R. and Richard Y. (1985) Experience with biological denitrification in a
full scale drinking water treatment. Vom. Wasser, 64, 145-154.
24. Fu C. and Poch M. (1995) System identification and real-time pattern recognition
by neural networks for activated sludge process. Environmental Intemational,
21(1), 57-69.
25. Fuente M. J. and Vega P. (1999) Neural networks applied to fault detection of a
biotechnological process. Engineering Application of Artificial Intelligence, 12,
569-584.
26. Fujii S. (1996) Theoretical analysis on nitrogen removal of the step-feed
anoxic-oxic activated sludge process and its application for the optimal
operation. Wat. Sci. Technol., 34(1-2), 459-466.
27. Glass C. and Silverstein J. (1998) Denitrification kinetics of high nitrate
concentration water: pH effect on inhibition and nitrite accumulation. Wat. Res.,
32(3), 831-839.
188
28. Görgün E., Artan N., Orhon D. and Sözen S. (1996) Evaluation of nitrogen
removal by step feeding in large treatment plants. Wat. Sci. Technol., 34(1-2),
253-260.
29. Gontarski C. A., Rodrigues P. R., Mori M., and Prenem L. F. (2000) Simulation of
an industrial wastewater treatment plant using artificial neural networks.
Computers and Chemical Engineering, 24, 1719-1723.
30. Gumaelius L., Magnusson G., Pettersson B. and Dalhammar G. (2001)
Comamonas denitrificans sp. nov., an efficient denitrifying bacterium isolated
from activated sludge. Int. J. Syst. Evol. Microbiol., 51(3), 999-1006
31. Häck M. and Köhne M. (1996) Estimation of wastewater parameters using neural
networks. Wat. Sci. Technol. 33(1), 101-115.
32. Hajda P., Novotny V., Feng X., and Yang R. (1998) Simple feedback logic, genetic
algorithms and artificial neural networks for real-time control of a collection
system. Wat. Sci. Technol. 38(3), 187-195.
33. Hallin S., Rothman M., and Pell M. (1996) Adaptation of denitrifying bacteria to
acetate and methanol in activated sludge. Wat. Res.,. 30(6), 1445-1450.
34. Halling-Sørensen B. and Jørgensen S. E. (1993) The Removal of Nitrogen
Compounds from wastewater. Elsevier, Denmark, 55-151.
35. Hamoda M. F. Al-Ghusain I. A. and Hassan A. H. (1999) Integrated wastewater
treatment plant performance evaluation using artificial neural networks. Wat.
Sci. Technol. 40(7), 55-65.
36. Hashsham S. A., Marsh T. L., Dollhopf S. L., Fernandez A. S., Dazzo F. B.,
Hickey R. F., Criddle C. S., and Tiedje J. M. (2001) Advances in Water and
Wastewater Treatment Technology-molecular technology, nutrient removal,
sludge reduction, and environmental health. Elsevier Science, 67-78.
37. Henze M., Harremoës P., and Roed Jensen O. (1977) Combined sludge
denitrification of sewage utilizing internal carbon source. Prog. Wat. Tech., 8,
589-599.
189
38. Henze M., Gujer W., Mino T., Matsuo T., Wentzel M.C., Marais G.v.R., and van
Loosdrecht M.C.M. (1999) Activated sludge model No.2d. Wat. Sci. Technol.,
39(1), 165-182.
39. Her J. J. and Huang J. S. (1995) Influences of carbon source and C/N ratio on
nitrate/nitrite denitrification and carbon breakthrough. Bioresource Technol., 54,
45-51.
40. Hiraishi A., Masamune K., and Kitamura H. (1989) Characterization of the
bacterial population structure in an anaerobic-aerobic activated sludge system
on the basis of respiratory quinine profiles. Appl. Environ. Microbiol,. 55(4),
897-901.
41. Holt G. J., Krieg R. N., Sneath H. P., Staley T. J., and Williams T. S. (1994)
Bergey’s manual of determinative bacteriology. 9th edition, Williams & Wilkins,
Maryland.
42. Hussain M. A. (1999) Review of the applications of neural networks in chemical
process control-simulation and online implementation. Artificial Intelligence in
Engineering, 13, 55-68.
43. Isaacs S., Henze M., Søeberg H., and Kümmel M. (1994) External carbon source
addition as a means to control an activated sludge nutrient removal process.
Wat. Res., 28(3), 511-520.
44. Jenkins D., Richard M. G., and Daigger G. T. (1993) Manual on the causes and
control of activated sludge bulking and forming. 2nd edition, Lewis Publishers.
45. Kim S., Lee H., Kim J., Kim C., Ko J., Woo H., and Kim S. (2002) Genetic
algorithms for the application of activated sludge model No.1. Wat. Sci.
Technol., 45(4-5), 405-411.
46. Kurt M., Dunn I. J., and Bourne J. R. (1987) Biological denitrification water using
autotrophic organisms with hydrogen in a fluidized-bed biofilm reactor.
Biotechnology Bioengineering, 29, 493-501.
47. Larachi F. (2001) Neural network kinetic prediction of coke burn-off on spent
190
MnO2/CeO2 wet oxidation catalysts. Applied Catalysis, 30, 141-150.
48. Lee D. S. and Park J. M. (1999) Neural network modeling for on-line estimation
of nutrient dynamics in a sequentially-operated batch reactor,” Journal of
Biotechnology, 75, 229-239.
49. Lee N. M. and Welander T. (1996) The effect of different carbon sources on
respiratory denitrification in biological wastewater treatment. Journal of
Fermentation and Bioengineering, 82(3), 227-285.
50. Li L., Kato C., and Horikoshi K. (1999) Microbial diversity in sediments collected
from the deepest cold-seep Area. Mar. Biotechnol., 1(4), 391-400.
51. Liu W. T., Mino T., Nakamura K. and Matsuo T. (1996) Glycogen accumulating
population and its anaerobic substrate uptake in anaerobic-aerobic activated
sludge without biological phosphorus removal. Wat. Res., 30(1), 75-82.
52. Liu E. T., Marsh T. L., Cheng H., and Forney L. J. (1997) Characterization of
microbial diversity by determining terminal restriction fragment length
polymorphisms of genes encoding 16S rRNA. Appl. Environ. Microbiol,.
63(11), 4516-4522.
53. Liu W. T., Wu J. H., Chan O. C., Cheng S. S., Tseng I. C., and Fang H. P. (2001)
Comparison of microbial communities in anaerobic granulated sludge reactors
treating benzoate, methyl benzoate and terephthalate. Advances in Water and
Wastewater Treatment Technology-molecular technology, nutrient removal,
sludge reduction, and environmental health. Elsevier Science, 79-88.
54. Liu W.T., Nielsen A.T., Wu J.H., Tsai C.S., Matsuo Y., and Molin S. (2001) In situ
identification of polyphosphate and polyhydroxyalkanoate-accumulating traits
for microbial populations in a biological phosphorus removal process. Environ.
Microbiol., 3(2), 110-122.
55. Manz W., Wagner M., Amann R., and Schleifer K. H. (1994) In situ
characterization of the microbial consortia active in two wastewater treatment
plants. Wat. Res., 28(8), 1715-1723.
191
56. Marsili-Libell S. and Giunti L. (2001) Fuzzy predictive control for nitrogen
removal in biological wastewater treatment. Instrumentation, Control and
Automation 2001(ICA 2001), IWA publishing, 37-44.
57. Matsuo Y. (1994) Effect of the anaerobic SRT on enhanced biological phosphorus
removal. Wat. Sci. Technol., 28, 127-136.
58. Matsuo Y. and Kurisu F. (2001) Observation and model analysis for the bacterial
community structure of activated sludge. Advances in Water and Wastewater
Treatment Technology-molecular technology, nutrient removal, sludge
reduction, and environmental health. Elsevier Science, 3-12.
59. Metcalf and Eddy (1991) Wastewater Engineering-treatment, disposal, and reuse.
McGraw-Hill, Inc.
60. McCarty P. J., Beck and Amant (1969) Biological denitrification of waste water
by addition of organic materials. 24th Ind. Waste Conf., Purdue University,
1271-1285.
61. Mino T., Satoh H. and Matsuo T. (1994) Metabolisms of different bacterial
populations in enhanced biological phosphate removal process. Wat. Sci.
Technol., 29(7), 67-70.
62. Mino T. (1998) Handout of Advanced Wastewater Biological Treatment
Engineering., National Central University, Taiwan.
63. Mino T., Satoh H., Onuki M., Akiyama T., Nomura T., and Matsuo T. (2001)
Strategic approach for characterization of bacterial community in enhanced
biological phosphate (EBNR) process. Advances in Water and Wastewater
Treatment Technology-molecular technology, nutrient removal, sludge
reduction, and environmental health. Elsevier Science, 21-30.
64. Mohseni-Bandpi A. and Elliott D. J. (1998) Groundwater denitrification with
alternative carbon sources. Wat. Sci. Technol., 38(6), 237-243.
65. Mohseni-Bandpi A., Elliott D. J., and Momeny-Mazdeh A. (1999) Denitrification
of groundwater using acetate acid as a carbon source. Wat. Res., 40(2), 53-59.
192
66. Monteith H. D. (1980) Industrial waste carbon source for biological denitrification.
Prog. Wat. Tech., 12, 127-141.
67. Murray R. G. E. (chairman) et al. (1984) Bergey’s manual of systematic
bacteriology. Williams & Wilkins.
68. Myers R. M., Fischer S. G., Lerman L. S., and Maniatis (1985) Near all single
base substitutions in DNA fragments joined to a GC clamp can be detected by
denaturing gradient gel electrophoresis. Nucleic Acid Res., 13(9), 3131-3145.
69. Nasu M., Yamaguchi N., and Tani K. (2001) Microbial community structure and
their activity in aquatic environment. Advances in Water and Wastewater
Treatment Technology-molecular technology, nutrient removal, sludge
reduction, and environmental health. Elsevier Science, 31-40.
70. Nielsen T. A., Liu W. T., Filipe C., Grady L., Molin S., and Stahl A. D. (1999)
Identification of a novel group of bacteria in sludge from a deteriorated
biological phosphorus removal reactor. Appl. Environ. Microbial., 65(3),
1251-1258
71. Olsson G. and Newell B. (1999) Wastewater Treatment Systems: Modeling,
Diagnosis and Control. IWA Publishing.
72. Oh J. and Silverstein J. (1999) Oxygen inhibition of activated sludge
denitrification. Wat. Res., 33(8), 1925-1937.
73. Ouyang C. F., Chou Y. J., Pai T. Y., Chang H. Y., and Liu W. T.(2001)
Optimization of enhanced biological wastewater treatment processes using a
step-feed approach. Advances in Water and Wastewater Treatment
Technology-molecular technology, nutrient removal, sludge reduction, and
environmental health. Elsevier Science, 295-304.
74. Pirsing A., Wiesmann U., Kelterbach G., Röck H. Eichner B. Szukal S., and
Schulze G. (1996) On-line monitoring and modeling based process control of
high rate nitrification-lab scale experimental results. Bioprocess Engineering,
15, 181-188.
193
75. Rauch W. and Harremoes P. (1999) Genetic algorithms in real time control applied
to minimize transient pollution from urban wastewater system. Wat. Res., 33(5),
1265-1277.
76. Richard T. (1980) Denitrification of water for human consumption. Prog. Wat.
Tech., 12, 173-191.
77. Rittmann B. E. and McCarty P. L. (2001) Environmental Biotechnology Principles
and Applications. McGraw-Hill, New York, United State, 470-524.
78. Reddy M. (1998) Biological and Chemical System for Nutrient Removal. Water
Environment Federation, Alexandria, United State, 101-189.
79. Schulze R., Spring S., Amann R., Huber I., Ludwig W., Schleifer K. H., and
Kampfer P. (1999) Genotypic diversity of Acidovorax strains isolated from
activated sludge and description of Acidovorax defluvii sp. Nov. Syst. Appl.
Microbiol., 22(2), 205-214.
80. Snaidr J., Amann R., Huber I., Ludwig W., Schleifer K. H. (1997) Phylogenetic
analysis and in situ identification of bacteria in activated sludge. Applied and
Environmental Microbiology. 63(7), 2884-2896.
81. Stensel, Loehr, and Lawrence (1973) Biological kinetics of suspended growth
denitrification. WPCF, 45, 399-410.
82. Surmacz-Górska J., Cichon A., and Miksch K. (1997) Nitrogen removal from
wastewater with high ammonia nitrogen concentration via shorter nitrification
and denitrification. Wat. Sci. Technol., 36(10), 73-78.
83. Syu M. J. and Chen B. C. (1998) Back-propagation neural network adaptive
control of a continuous wastewater treatment process. Ind. Eng. Chem. Res., 37,
3625-3630.
84. Tiedje J. Fernandez A., Hashsham S., Dollhopf S., Dazzo F., Hickey R., and
Criddle C. (2001) Stability, persistence and resilience in anaerobic reactor: a
community unveiled. Advances in Water and Wastewater Treatment
Technology-molecular technology, nutrient removal, sludge reduction, and
194
environmental health. Elsevier Science, 13-20.
85. Wagner M., Erhart R., Manz W., Amann R., Lemmer H., Wedi D., and Schleifer K.
H. (1994) Development of an rRNA-targeted oligonucleotide probe specific for
the genus Acinetobacter and its application for in situ monitoring in activated
sludge. Appl. Environ. Microbiol., 60(3), 792-800.
86. Wentzel M. C., Lotter L. H., Ekama G. A., Loewenthal R. E., and Marais G. R.
(1991) Evaluation of biochemical models for biological excess phosphorus
removal. Wat. Sci. Technol., 23, 567-576.
87. Yagi S. and Shiba S. (1999) Application of genetic algorithms and fuzzy control to
a combined sewer pumping station. Wat. Sci. Technol., 39(9), 217-224.
88. Yu R. F., Liaw S. L., Chang C. N., and Cheng W. Y. (1998) Appling real-time
control to enhance the performance of nitrogen removal in the continuous-flow
SBR system. Wat. Sci. Technol., 38(3), 271-280.
89. Zhao H., Isaacs S. H., Søeberg H., and Kümmel M. (1995) An analysis of nitrogen
removal and control strategies in an alternating activated sludge process. Wat.
Res., 29(2), 535-544.
90. Zhu J., Zurcher J., Rao M., and Meng M. Q-H. (1998) An on-line wastewater
quality prediction system based on time-delay neural network. Engineering
Application of Artificial Intelligence. 11, 747-758.
91. 歐陽嶠暉，下水道工程學，長松出版社，台北（2000）。
92. 蔡勇斌(1993)活性污泥系統自動化與最佳化動態操作控制之研究，博士論
文，國立中央大學環境工程研究所。
93. 張謝淵(2000)AOAO 污水處理程序去除營養鹽之特性研究，博士論文，國立
中央大學環境工程研究所。
94. 游勝傑(2001)併同生物膜與活性污泥程序之硝化及脫硝攝磷特性研究，博士
論文，國立中央大學環境工程研究所。