GPS-X Technical Reference

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Optimizer 374 OBJECTIVE FUNCTION OPTIONS The list of objective functions available in GPS-X is given below: Absolute Difference Equation 14.1 Relative Difference Equation 14.2 Sum of Squares Equation 14.3 Relative Sum of Squares Equation 14.4 Maximum Likelihood Equation 14.5 GPS-X Technical Reference
375 Optimizer In the objective function expressions given above the following nomenclature is used: zi,j = the measured value of response j in experiment i. fi,j j m nj = the value of response variable j predicted by the process model in experiment i = the heteroscedasticity parameter for response j = the number of measured response variables = the number of experiments (i.e. observations) for response j These objective function types are accessed by clicking on the inverted triangle beside the Optimize icon and then selecting Type from the drop-down menu. Using the Optimizer for Parameter Estimation In general, the maximum likelihood objective function should be used when doing parameter estimation. This objective function calculates statistically optimal parameter estimates based on assumptions on the nature of the measurement errors. The sum of squares objective function is a special case of the maximum likelihood objective function derived using further simplifying assumptions and can also be used. It is equivalent to the maximum likelihood objective function when there is only one response or target variable. Further details on parameter estimation and the maximum likelihood and sum of squares objective functions can be found in Appendix A of this chapter. The other objective functions can be used for curve fitting when calculating statistically optimal parameter estimates is not a concern. Using the Optimizer for Process Optimization In addition to data fitting applications, GPS-X can be used for process optimization. For example, GPS-X can calculate the operating conditions for your process model that optimize some measure of process performance, such as operating cost or effluent quality. To solve this type of problem in GPS-X you need to treat the problem as a data fitting exercise. For example, if you want to minimize the value of a certain model variable you need to specify an arbitrarily small target value for the model variable in a .dat file and have the optimizer minimize the difference between the calculated variable value and the target value using the absolute difference objective function. This is equivalent to minimizing the model variable directly. The target value should be made small enough so that the optimizer cannot reach it. You are not limited to using one performance measure. You can select a number of different performance measures and have GPS-X optimize these variables simultaneously by fitting them to user-supplied targets. 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
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xi Table of Contents Figure 10-8 -
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xiii Table of Contents Figure 15-6
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xv Table of Contents Table 6-2 - Mo
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17 Modelling Fundamentals Experimen
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19 Modelling Fundamentals The proce
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21 Modelling Fundamentals Overview
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23 Modelling Fundamentals Primary a
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25 Modelling Fundamentals Vesilin
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27 GPS-X Objects CHAPTER 2 GPS-X Ob
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29 GPS- X Objects The mass balance
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31 GPS- X Objects Combining Equatio
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33 GPS-X State Variable Libraries S
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35 GPS-X State Variable Libraries C
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37 GPS-X State Variable Libraries C
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39 GPS-X Composite Variable Librari
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41 GPS-X Composite Variable Calcula
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55 Composite Variable Calculations
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61 Influent Models CHAPTER 5 Influe
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63 Influent Models Figure 5-3 - Inf
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65 Influent Models The three influe
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67 Influent Models Total runoff (Q
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69 Influent Models These inputs are
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71 Influent Models CODFractions COD
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73 Influent Models TSSCOD Figure 5-
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75 Influent Models Figure 5-13 - CN
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77 Influent Models CN and CNIP Libr
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83 Influent Models INFLUENT OBJECTS
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85 Influent Models Figure 5-22 - In
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87 Influent Models Chemical Dosage
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89 Influent Models Figure 5-29 - Ac
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91 Influent Models Figure 5-33 - Se
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93 Influent Models Figure 5-36 - Se
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95 Suspended Growth Models where: V
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97 Suspended Growth Models The baro
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99 Suspended Growth Models Oxygen M
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101 Suspended Growth Models In the
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103 Suspended Growth Models The SOT
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105 Suspended Growth Models Standar
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107 Suspended Growth Models The reg
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109 Suspended Growth Models SUMMARY
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111 Suspended Growth Models Figure
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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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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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Page 361 and 362: 361 Operating Cost Models CHAPTER 1
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Page 409 and 410: 409 Optimizer APPENDIX C: NOMENCLAT
Page 411 and 412: 411 Optimizer APPENDIX D: REFERENCE
Page 413 and 414: 413 Miscellaneous CHAPTER 15 Miscel
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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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