GPS-X Technical Reference

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Influent Models 62 The Influent Advisor screen shows three columns of data: User Inputs, State Variables, and Composite Variables, as shown in Figure 5-2. Figure 5-2 - Influent Advisor Menu The fields in the left-hand column show the inputs available to the user, such as influent concentrations and stoichiometric ratios. The centre and right hand columns show the state variable and composite variable concentrations calculated from the user inputs. As the user changes values in the left-hand column, the variables in the centre and right-hand columns are automatically updated. This allows for easy debugging of confusing or conflicting influent characterization data. Clicking on any variable in the centre and right-hand column illustrates how that value is calculated. The formula will be displayed in the formula box (located in the lower middle of the screen – you may need to scroll down to see it). The values used in the formula will be highlighted in the tables so that the applicable cells can be identified. If a negative value is calculated in either table, the cell will turn red. Correcting problematic data is only a matter of adjusting the input cells to achieve non-negative values. GPS-X Technical Reference
63 Influent Models Figure 5-3 - Influent Advisor Screen Showing Highlighted Cells and Negative Values (Highlighted in Red) The influent models used in GPS-X make certain assumptions about what data may or may not be available. For instance, no influent model uses both COD and BOD data, even though this data may be available and may provide important information about how that organic material is partitioned into the available state variables. Influent Advisor helps overcome this shortcoming. Take an example in which BOD and COD data is available and the BODbased model is chosen for the influent object. The applicable data can be entered into Influent Advisor, including the available BOD data. The user can then scroll to the right table and check the COD value calculated based on the input data. If the COD data is in agreement with the measured COD data (assuming no negative values in any cells), then the user can be assured that the input data is consistent with the available data. If the COD data is different from the measured COD, then the unknown (or estimated) input data should be adjusted until acceptable agreement is achieved. GPS-X Technical Reference
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GPS-X Technical Reference GPS-X Ver
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Table of Contents iii Table of Cont
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v Table of Contents Sedimentation M
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vii Table of Contents Real Time Clo
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ix Table of Contents Figure 6-6 - P
Page 11 and 12: xi Table of Contents Figure 10-8 -
Page 13 and 14: xiii Table of Contents Figure 15-6
Page 15 and 16: xv Table of Contents Table 6-2 - Mo
Page 17 and 18: 17 Modelling Fundamentals Experimen
Page 19 and 20: 19 Modelling Fundamentals The proce
Page 21 and 22: 21 Modelling Fundamentals Overview
Page 23 and 24: 23 Modelling Fundamentals Primary a
Page 25 and 26: 25 Modelling Fundamentals Vesilin
Page 27 and 28: 27 GPS-X Objects CHAPTER 2 GPS-X Ob
Page 29 and 30: 29 GPS- X Objects The mass balance
Page 31 and 32: 31 GPS- X Objects Combining Equatio
Page 33 and 34: 33 GPS-X State Variable Libraries S
Page 35 and 36: 35 GPS-X State Variable Libraries C
Page 37 and 38: 37 GPS-X State Variable Libraries C
Page 39 and 40: 39 GPS-X Composite Variable Librari
Page 41 and 42: 41 GPS-X Composite Variable Calcula
Page 55 and 56: 55 Composite Variable Calculations
Page 61: 61 Influent Models CHAPTER 5 Influe
Page 65 and 66: 65 Influent Models The three influe
Page 67 and 68: 67 Influent Models Total runoff (Q
Page 69 and 70: 69 Influent Models These inputs are
Page 71 and 72: 71 Influent Models CODFractions COD
Page 73 and 74: 73 Influent Models TSSCOD Figure 5-
Page 75 and 76: 75 Influent Models Figure 5-13 - CN
Page 77 and 78: 77 Influent Models CN and CNIP Libr
Page 83 and 84: 83 Influent Models INFLUENT OBJECTS
Page 85 and 86: 85 Influent Models Figure 5-22 - In
Page 87 and 88: 87 Influent Models Chemical Dosage
Page 89 and 90: 89 Influent Models Figure 5-29 - Ac
Page 91 and 92: 91 Influent Models Figure 5-33 - Se
Page 93 and 94: 93 Influent Models Figure 5-36 - Se
Page 95 and 96: 95 Suspended Growth Models where: V
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Page 109 and 110: 109 Suspended Growth Models SUMMARY
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113 Suspended Growth Models General
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115 Suspended Growth Models Number
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117 Suspended Growth Models Elevati
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119 Suspended Growth Models Figure
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121 Suspended Growth Models AERATIO
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123 Suspended Growth Models The ent
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125 Suspended Growth Models The org
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127 Suspended Growth Models Process
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129 Suspended Growth Models It i
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131 Suspended Growth Models Nitroge
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133 Suspended Growth Models Include
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135 Suspended Growth Models
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137 Suspended Growth Models Althoug
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139 Suspended Growth Models Particu
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141 Suspended Growth Models 9. Grow
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143 Suspended Growth Models 21. Sto
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145 Suspended Growth Models 33. Gro
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147 Suspended Growth Models To mode
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149 Suspended Growth Models Algebra
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151 Suspended Growth Models The sto
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153 Suspended Growth Models Decay o
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155 Suspended Growth Models SUGGEST
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157 Suspended Growth Models Special
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159 Suspended Growth Models Table 6
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161 Suspended Growth Models The GPS
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163 Suspended Growth Models where:
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165 Suspended Growth Models The max
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167 Suspended Growth Models If yo
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169 Suspended Growth Models ANAEROB
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171 Suspended Growth Models Simple
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173 Suspended Growth Models With th
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175 Suspended Growth Models Manual
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177 Suspended Growth Models Equatio
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179 Suspended Growth Models The met
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181 Suspended Growth Models Pond Mo
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183 Suspended Growth Models Special
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185 Suspended Growth Models Se
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187 Suspended Growth Models aerated
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193 Suspended Growth Models Calcula
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195 Suspended Growth Models The vap
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197 Suspended Growth Models Oxygen
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199 Suspended Growth Models Estimat
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203 Suspended Growth Models Enterin
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209 Suspended Growth Models MODELLI
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211 Suspended Growth Models TEMPERA
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213 Suspended Growth Models Table 6
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215 Suspended Growth Models The PAC
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217 Suspended Growth Models CHAPTER
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219 Attached-Growth Models Each lay
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221 Attached-Growth Models Equation
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223 Attached-Growth Models The mode
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225 Attached-Growth Models The phys
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227 Attached-Growth Models Liquid F
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229 Attached-Growth Models This con
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231 Attached-Growth Models The next
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233 Attached-Growth Models The SBC
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235 Attached-Growth Models Based on
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237 Attached-Growth Models Operatio
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239 Attached-Growth Models Hydrauli
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241 Attached-Growth Models ADVANCED
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243 Attached-Growth Models Filtrati
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245 Attached-Growth Models Operatio
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247 Attached-Growth Models This for
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249 Attached-Growth Models HYBRID S
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251 Attached-Growth Models Figure 7
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253 Sedimentation and Flotation Mod
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267 Sand Filtration Models Floatati
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269 Sand Filtration Models The back
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271 Sand Filtration Models The mass
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273 Sand Filtration Models Figure 9
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275 Digestion Models Figure 10-1 -
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277 Digestion Models Seven yield co
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279 Digestion Models Based on the a
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281 Digestion Models Combining the
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283 Digestion Models The Operationa
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285 Digestion Models Kinetic and St
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287 Digestion Models The rest of th
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291 Digestion Models ADM1 State Var
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293 Digestion Models AEROBIC DIGEST
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297 Other Models CHAPTER 11 Other M
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299 Others Models High volume for p
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301 Others Models The sludge pretre
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303 Others Models With a large over
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305 Others Models Dosage Controller
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307 Others Models The set of above
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309 Others Models There are three p
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311 Others Models DISINFECTION UNIT
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313 Others Models DEWATERING Four m
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315 Others Models The optimal polym
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317 Others Models DISC AND BELT MIC
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319 Others Models Figure 11-12 - Pl
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321 Others Models Physical Setup Fo
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323 Others Models Exponential const
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325 Others Models BLACK BOX The Bla
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327 Tools Object Figure 11-19 - Spe
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329 Tools Object Figure 11-21 - Inp
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331 Tools Object Typical Model Outp
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333 Tools Object The variable speed
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335 Tools Object SLUDGE EFFLUENT BU
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337 Tools Object PH TOOL Take a sam
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339 Tools Object These concentratio
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341 Tools Object LOW-PASS FILTERING
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343 Tools Object Control Variable P
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345 Tools Object The individual mod
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347 Tools Object PID in Position Fo
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349 Tools Object The following para
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351 Tools Object The controller tun
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353 Tools Object When both the proc
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355 Tools Object TIMER CONTROL The
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357 Tools Object Controller - The c
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359 Tools Object (qcon is the crypt
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361 Operating Cost Models CHAPTER 1
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363 Operating Cost Models The energ
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365 Operating Cost Models OPERATING
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367 Operating Cost Models OPERATING
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369 Operating Cost Models CALIBRATI
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371 Operating Cost Models Category
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373 Optimizer If after the reflecti
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375 Optimizer In the objective func
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377 Optimizer Figure 14-2 - Optimiz
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379 Optimizer Figure 14-3 - Example
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381 Optimizer SUMMARY OF THE OPTIMI
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383 Optimizer Use specified standar
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385 Optimizer Level of significance
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387 Optimizer APPENDIX A: MAXIMUM L
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389 Optimizer In GPS-X the log-like
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391 Optimizer Note that the user ca
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393 Optimizer Gradient of the Objec
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395 Optimizer Hessian of the Object
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397 Optimizer For the maximum likel
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399 Optimizer Percentage Variation
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401 Optimizer GPS-X reports the F-v
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403 Optimizer The sum of squares ra
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405 Optimizer To check for violatio
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407 Optimizer If the sample autocor
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409 Optimizer APPENDIX C: NOMENCLAT
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411 Optimizer APPENDIX D: REFERENCE
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413 Miscellaneous CHAPTER 15 Miscel
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415 Miscellaneous As compared to th
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417 Miscellaneous A typical GPS-X o
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419 Miscellaneous RESIDUAL PLOTS Fo
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421 Miscellaneous Figure 15-6 - Res
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423 Miscellaneous REAL TIME CLOCK T
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425 Miscellaneous Figure 15-9 - Ste
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427 Miscellaneous Figure 15-11 - In
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429 Miscellaneous The smoothing fun
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431 Miscellaneous The Gear's Stiff
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Appendix A - Petersen Matrices A-1
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ASM2d in CNPLIB Appendix A - Peters
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15 Lysis of XBP 16 Lysis of XPP 17
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NewGeneral in CNPLIB Appendix A - P
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NewGeneral in CNPLIB Appendix A - P
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Component i Process Rates Appendix
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2 Appendix A - Prefermenter Peterse
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D-2 Appendix D - References Brockwe
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D-4 Appendix D - References Hur, D.
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D-6 Appendix D - References Reilly,
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D-8 Appendix D - References Wuhrman
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