E-Andrew Sindt Creative Component S11.pdf

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Figure AI.2 - Influent specifier fraction estimate page for regular mode (guess and check red colorednumbers until the best match is found)Figure AI.3 - Influent specifier COD fractions page for regular mode. Values were input into BioWin forregular mode.76
Table AI.2- Influent specifier input values BNR-S1 modeParameter Value Units Data SourceFlow 1253 m 3 Mechanical monthly monitoring reports (August,/d2005)Influent total COD 783 mg/L Estimated using Table 2 and 3 dataTKN 39.2 mg/L Calculated from Table 3 dataTotal P 9.5 mg/L Table 3Nitrate 0.3 mg/L Table 3pH 7.2 - Table 3Alkalinity 300 mg CaCO 3/L DefaultCalcium 80 mg/L DefaultMagnisium 15 mg/L DefaultD.O. 0 mg/L DefaultEffluent filtered COD 29.3 mg/L Table 3Influent filtered COD 608 mg/L Table 3Influent FF COD 340 mg/L Estimated using Table 3 data and default valuesAmmonia 24.1 mg/L Table 3Acetate 12 mg/L DefaultOrtho-phosphate 5 mg/L DefaultInfluent cBOD 5 392 mg/L Estimated using COD/cBOD = 2Influent filtered cBOD 5 304 mg/L Estimated using sCOD/fcBOD = 2Influent VSS 384 mg/L Estimated from sample taken 10/20/10Influent TSS 465 mg/LMechanical monthly monitoring reports (August,2005)Figure AI.4 - Influent specifier input page for BNR-S1 mode.77
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Prediction of nutrient removal with
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Table of ContentsList of Figures ..
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3.9 Effect of COD Fractions .......
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List of FiguresFigure 2.1- Biomass
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Figure AI.14- D.O. set point itiner
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Table AI.1- Influent specifier inpu
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Chapter 1: Introduction1.1 Importan
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Chapter 2: Literature Review2.1 Nut
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has a very low cBOD concentration a
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dissolved oxygen (D.O.) at a suffic
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Denitrification using biomass decay
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Figure 2.7- Phoredox process (Rittm
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increased the mean volatile fatty a
Page 27 and 28: used model is the Activated Sludge
Page 29 and 30: conditions included a discrete fill
Page 31 and 32: 3.1 Wastewater Treatment Plant Desc
Page 33 and 34: BNR-S2 operated with 1.5 hours simu
Page 35 and 36: Effluent 7.2 35 7.8 3.5 0.8 0.8 1.9
Page 37 and 38: constant width of 12.6 m over this
Page 39: Table 3.5- Sensitivity Parameters f
Page 42 and 43: 3.6.2 Calibration of BNR-S1 ModeThe
Page 44 and 45: Chapter 4 Results and Discussion4.1
Page 46 and 47: include 24-hour composite samples t
Page 48 and 49: Perecent deviation (predicted - obs
Page 50 and 51: Percent deviation (predicted-observ
Page 52 and 53: Percent deviaton in value (model-ob
Page 54 and 55: Percent deviation in value (modelob
Page 56 and 57: 4.2.2.2 Aerobic P/PHA uptake and P/
Page 58 and 59: Calibrated simulator parameters for
Page 60 and 61: Sum absolute value of percent devia
Page 62 and 63: Absolute value of percent deviation
Page 64 and 65: Table 4.10- BNR-S2 colder (15.7 o C
Page 66 and 67: Table 4.13- Width adjustment effect
Page 68 and 69: Percent removala linear trend from
Page 70 and 71: Chapter 5: Conclusion5.1 SummaryBio
Page 72 and 73: Chapter 6: ReferencesAlleman, J.E.,
Page 74 and 75: Libra, R. D., Wolter, C. F., and La
Page 76 and 77: Appendix IBioWin Simulation Setup74
Page 80 and 81: Figure AI.5 - Influent specifier fr
Page 82 and 83: Figure AI.9 - SBR operation page fo
Page 84 and 85: Figure AI.13 - SBR operation page f
Page 86 and 87: Appendix IISelected BioWin Simulati
Page 88 and 89: Table AII.1- Percent deviation from
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Page 92 and 93: Sum absolute value of percent devia
Page 94 and 95: AOB and NOB maximum specific growth
Page 96 and 97: Appendix IIIMonthly Monitoring Repo
Page 98 and 99: DAYIrrig.1000'sGPDInfluentNH3-Nmg/L
Page 100 and 101: D RAIN- SETT. RECYCLE Irrig.1000' I
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