Stochastic Global Optimization Techniques and Applications in Chemical Engineering: Techniques and Applications in Chemical Engineering, With CD-ROM (Advances in Process Systems Engineering)

CONTENTS......Page 10
Preface......Page 6
1. Optimization in Chemical Engineering......Page 14
2.1. Modified Himmelblau function......Page 16
2.2. Ellipsoid and hyperboloid intersection......Page 17
2.3. Reactor design example......Page 18
2.4. Stepped paraboloid function......Page 20
3. Global Optimization Techniques......Page 22
4. Scope and Organization of the Book......Page 24
References......Page 27
Exercises......Page 28
1. Introduction......Page 30
2. LJ Optimization Procedure......Page 31
2.1. Example of an optimization problem—diet problem with 7 foods......Page 32
2.2. Example 2—Alkylation process optimization......Page 38
2.3. Example 3 —Gibbs free energy minimization......Page 41
3. Handling Equality Constraints......Page 45
3.1. Example 4 —Geometric problem......Page 47
3.2. Example 5 —Design of columns......Page 51
4. Effect of Parameters......Page 54
4.1. Example 7 —Minimization of Rosenbrock function......Page 59
4.2. Example 8 —Maximization of the Shubert function......Page 63
5. Conclusions......Page 65
References......Page 66
Exercises......Page 68
1. Introduction and Motivation......Page 70
2.1. Introduction......Page 71
3.1. Introduction......Page 78
3.2. SA–S/1 algorithm......Page 81
3.3.1. Initial simplex generation......Page 82
3.3.2. Determination of the initial temperature......Page 83
3.3.4. Cooling scheme—Temperature decrease......Page 84
4.1. Tests—Test problems and results......Page 85
4.2.2. Influence of parameter INV......Page 90
4.2.3. Influence of parameter K in the equilibrium criterion......Page 92
4.2.5. Influence of parameter T min......Page 94
4.3. Results and analysis of tests for LJ–MM algorithm......Page 95
4.4. Selected application issues......Page 97
4.4.1. Dealing with inequality constraints......Page 99
4.4.2. Dealing with equality constraints......Page 100
4.5. Problem size effect......Page 101
Symbols......Page 105
References......Page 106
Exercises......Page 113
Appendix A......Page 115
1. Introduction......Page 124
2. Review of Chemical Engineering Applications......Page 126
3. The Basic Genetic Algorithm......Page 127
3.1. Encoding......Page 131
3.2. Fitness evaluation......Page 132
3.3. Initial population......Page 133
3.4.1. Fitness proportionate selection......Page 134
3.4.2. Other selection schemes......Page 136
3.5. Crossover......Page 137
3.6. Mutation......Page 139
3.7. Theoretical aspects......Page 140
3.8.1. Advantages......Page 142
3.9. When should we use GAs?......Page 143
4.1.1. Encoding......Page 145
4.2.1. Reducing the total number of evaluations......Page 146
4.2.2. Reducing the cost of individual evaluation......Page 147
4.3. Constraint handling......Page 148
4.4.1. Genetic operators......Page 149
5. Advanced Topics......Page 150
5.1. Maintaining population diversity......Page 151
5.1.1. Adaptive parameter setting......Page 152
5.1.2. Island-based genetic algorithms......Page 153
5.2. Hybridization......Page 154
References......Page 155
1. Introduction......Page 160
2. Tabu Search with Quasi-Newton (TS–QN) Method......Page 161
3. Application of TS–QN to the Modified Himmelblau Function......Page 168
4. TS Methods for Global Optimization of Continuous Problems......Page 170
4.1. Multi-objective TS (MOTS)......Page 176
4.3. Other issues/developments......Page 177
6. Chemical Engineering Applications of TS......Page 178
7. Features of Tabu Search......Page 180
8. Short Term Memory (STM)......Page 182
9. Aspiration Criterion......Page 183
10. Candidate List......Page 184
11. Long Term Memory (LTM)......Page 185
12. Intensification......Page 186
12.2. Path relinking......Page 187
References......Page 188
1. Introduction......Page 216
2. Description of DE......Page 217
3.1. Initialization......Page 223
3.2. Mutation operation......Page 225
3.3. Crossover......Page 229
3.4. Selection......Page 231
3.5. Hybrid methods and new generation......Page 232
5. Conclusions......Page 235
References......Page 243
6. Exercises......Page 248
1. Introduction......Page 250
2. ACO for Continuous Function Optimization......Page 252
3. Continuous Ant Colony Optimization Algorithm (CACO)......Page 253
3.1. Local search......Page 255
3.2. Global search......Page 256
3.3. Pheromone evaporation......Page 259
4. ACO Algorithm for Combinatorial Optimization......Page 260
5.1. UIS policy......Page 261
5.2. ZW policy......Page 262
6. ACO Algorithm for Scheduling of SerialMultiproduct Batch Plant......Page 263
7. ACO for Multiobjective Scheduling of SerialMultiproduct Batch Plant......Page 265
7.1. Multiobjective optimization (MOO) problem definition......Page 266
8. ACO for Knowledge Discovery in Process Data......Page 267
9. ACO for Data Clustering......Page 268
11.1. CACO for unconstrained continuous functions......Page 270
12. CACO for Constrained Continuous Functions......Page 272
12.1. Scheduling of batch processes......Page 274
12.2. ACO for data clustering......Page 275
13. Conclusions......Page 277
References......Page 278
Acknowledgement......Page 282
1. Introduction......Page 284
2. The Initial Version of the PSO Algorithm......Page 286
3.2. Inertia weight......Page 288
3.3. Constriction factor......Page 289
3.4. Stopping criteria......Page 290
4.1. Global best / Local best PSO......Page 291
5. Extensions of Basic PSO......Page 293
6. Multi-Start PSO Algorithms......Page 294
8.1. Applications to constrained problems......Page 295
9.1. Initialization......Page 297
9.3. Handling constraints......Page 298
10. Numerical Results......Page 299
References......Page 310
1. Introduction......Page 314
2. Applications of the Harmony Search......Page 316
2.1. Selected applications broadly related to Chemical Engineering......Page 318
2.2. Chemical Engineering applications......Page 319
3.1. Basic harmony search for continuous decision variables......Page 321
Initializing harmony memory......Page 322
Improvising a new harmony......Page 323
Updating harmony memory......Page 324
3.2. The harmony search approach compared with other global optimization methods......Page 325
3.3. Basic harmony search for discrete and mixed variable problems......Page 326
3.5. Selection of algorithm parameter values......Page 328
3.6. Handling equality and inequality constraints......Page 330
4. Variations on the Basic Harmony Search Algorithm......Page 332
Termination criteria......Page 333
Variable PAR and bandwidth......Page 334
Biased selection......Page 335
Random playing replaced by simulated annealing......Page 336
Special training of elite harmonies......Page 337
4.4. Specialized applications and hybrid algorithms......Page 338
5. Harmony Search Software......Page 339
6. Illustrative Example......Page 340
References......Page 342
Exercises......Page 347
1. Introduction......Page 350
2.1. Performance measures......Page 351
2.1.2. Algorithm efficiency......Page 352
2.1.3. Quality-efficiency trade-off......Page 353
2.3. Effects of stochastic sampling......Page 354
2.4. Parameter tuning......Page 355
3. Test Problems......Page 356
3.1. Classification of test problems......Page 357
3.2. Selecting the test suite......Page 358
4.1. Preliminary experimentation phase......Page 359
5. Reporting......Page 360
5.5. Performance measures......Page 361
5.7. Discussion......Page 362
6. Conclusion......Page 363
References......Page 364
1. Introduction......Page 366
2. ProposedMethodology......Page 371
3. Sequential Niche Technique for Multimodal Function Optimization......Page 373
4. Results and Discussion......Page 379
5. Conclusions and Future Work......Page 383
References......Page 385
1. Introduction......Page 388
2. Model Reduction of s-transfer Functions......Page 391
Model reduction in frequency domain......Page 394
Model reduction in time domain......Page 395
3. Model Reduction of z-transfer Functions......Page 399
4. Parameter Estimation......Page 401
5. Optimal Control......Page 407
References......Page 420
Exercises......Page 424
1. Introduction......Page 426
2.1. Description of phase equilibrium problems......Page 429
2.1.1. Formulation of the optimization problem......Page 430
2.2. Description of phase stability problem......Page 433
2.2.1. Formulation of the optimization problem......Page 435
3. DE and TS, and Parameter Tuning......Page 437
3.1. Tuning of TS and DE parameters using performance profiles......Page 438
4.1. Benchmark problems for parameter tuning......Page 442
4.2. Results of parameter tuning......Page 445
5. Applications......Page 449
5.1. Example 1......Page 450
5.2. Example 2......Page 459
5.3. Discussion......Page 464
References......Page 465
7. Exercises......Page 467
Appendix A......Page 468
Appendix B......Page 472
1. Introduction......Page 478
2. Differential Evolution with Tabu List (DETL)......Page 480
2.1. Description of DETL-E and DETL-G......Page 481
3. Implementation and Evaluation......Page 486
4.1. Parameter tuning......Page 487
4.2. Results and discussion......Page 490
5. Phase Stability Problems......Page 495
5.1. Problem formulation......Page 496
5.2. Results and discussion......Page 500
6. Benchmark Problems Similar to Phase Stability Problems......Page 504
7. Conclusions......Page 509
Nomenclature......Page 510
References......Page 511
1. Nonrandom Two–Liquid (NRTL) Model (Renon and Prausnitz, 1968)......Page 513
2. Universal Quasi–Chemical (UNIQUAC)Model (Anderson and Prausnitz, 1978)......Page 514
3. Soave–Redlich–Kwong (SRK) Equation of State (Soave, 1972)......Page 516
1. Introduction......Page 518
2. Design ofWater Network with Reuse and Regeneration— Problem Statement......Page 522
3.1. WNRR superstructure......Page 525
3.2. Optimization problem......Page 526
4. Solution Approach by Stochastic Optimization for Superstructure Model......Page 532
5. Application of Adaptive Random Search toWNRR Problem......Page 534
6.1. Example 1......Page 539
6.2. Example 2......Page 542
6.3. Example 3......Page 545
7. Summary......Page 548
Subscripts......Page 549
References......Page 550
Exercises......Page 554
Appendix......Page 555
1. Introduction......Page 558
2. The Foundation of HEN Retrofit Approach......Page 561
3.1. Representation and codes for superstructure and structures......Page 563
3.2. Generation of structures and genetic operators......Page 565
4.1. General concept of the parameter optimization level......Page 569
4.2. Mathematical model of the HEN with fixed structure......Page 572
4.3. Genetic Algorithms solver for parameter optimization......Page 578
5. Examples......Page 583
6. Summary......Page 593
Problems and Exercises......Page 594
Symbols......Page 598
References......Page 599
1. Introduction......Page 604
2. Ant Algorithm for Discovery of Classification Rules (AntzClass)......Page 609
3. Generation of Classification Rules......Page 610
4. Probability Calculation......Page 612
5. Heuristic Information......Page 613
7. Ant Algorithm for Feature Selection (AntzFeat)......Page 614
8.1. Performance study of AntzClass......Page 618
8.2. Tennessee eastman process (TEP)......Page 621
8.3. Performance study of wrapper approach (AntzFeat-AntzClass)......Page 622
8.5. QSAR data......Page 623
9. Conclusions......Page 624
Arabic symbols......Page 625
References......Page 626
Exercises......Page 629
Acknowledgement......Page 630
1. Introduction......Page 632
2.1. Working principle......Page 633
2.2. Example......Page 636
2.3. Features of CP......Page 637
2.4. Determination of multiple optimal solutions......Page 642
2.5. Determination of pareto fronts along with multiple realizations......Page 643
2.6. Comparison of CP and GA......Page 646
3. Case Studies......Page 649
3.1.2. CP formulation......Page 650
3.1.3. GA implementation......Page 652
3.1.5. Results......Page 654
3.2.1. Problem definition......Page 663
3.2.2. CP formulation......Page 664
3.2.3. GA implementation......Page 668
3.2.4. Data......Page 669
3.2.5. Results......Page 670
4. Conclusions......Page 678
References......Page 679
Scheduling Data for Case Study......Page 681
The Data for Case Study......Page 686
1. Introduction......Page 690
2. Parallel Schemes of Stochastic Optimization Algorithms......Page 693
3. Tabu Search Algorithm......Page 695
4.1. HEN synthesis......Page 697
4.2. CAMD......Page 699
5. OpenMP Tabu Search......Page 702
5.1. Functional decomposition......Page 703
5.2. Multi-search threads......Page 705
5.3. Results......Page 706
6. Hybrid MPI/OpenMP Tabu Search......Page 707
6.1. Results......Page 709
7. Summary......Page 711
References......Page 712
Index......Page 718