Predictive control in process engineering: from the basics to the applications
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Weinheim
Wiley-VCH
2011
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| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Item Description: | Literaturangaben |
| Physical Description: | XXIX, 600 S. Ill., graph. Darst. |
| ISBN: | 352731492X 9783527314928 9783527636242 |
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MARC
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| 245 | 1 | 0 | |a Predictive control in process engineering |b from the basics to the applications |c Robert Haber, Ruth Bars, and Ulrich Schmitz |
| 264 | 1 | |a Weinheim |b Wiley-VCH |c 2011 | |
| 300 | |a XXIX, 600 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
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IMAGE 1
CONTENTS
PREFACE XIII REFERENCES XXI
NOTATION AND ABBREVIATIONS XXIII
1 INTRODUCTION TO PREDICTIVE CONTROL 1 1.1 PREVIEW OF PREDICTIVE CONTROL
2 1.1.1 PREDICTION OF THE REFERENCE VALUE 3 1.1.2 PREDICTION OF THE
DISTURBANCE 3 1.2 MANIPULATED, REFERENCE, AND CONTROLLED SIGNALS 4 1.3
COST FUNCTION OF PREDICTIVE CONTROL 5 1.4 REFERENCE SIGNAL AND
DISTURBANCE PREVIEW, RECEDING HORIZON,
ONE-STEP-AHEAD, AND LONG-RANGE OPTIMAL CONTROL 8 1.5 FREE AND FORCED
RESPONSES OF THE PREDICTED CONTROLLED VARIABLE 12 1.6 MINIMIZATION OF
THE COST FUNCTION 13 1.6.1 MINIMIZATION ALGORITHMS FOR NONLINEAR
PROCESSES
WITH OR WITHOUT CONSTRAINTS 14 1.6.2 MINIMIZATION OF THE QUADRATIC COST
FUNCTION FOR LINEAR PROCESSES WITHOUT CONSTRAINTS 14 1.7 SIMPLE TUNING
RULES OF PREDICTIVE CONTROL 15 1.8 CONTROL OF DIFFERENT LINEAR SISO
PROCESSES 18 1.9 CONTROL OF DIFFERENT LINEAR MIMO PROCESSES 21 1.10
CONTROL OF NONLINEAR PROCESSES 23 1.11 CONTROL UNDER CONSTRAINTS 24 1.12
ROBUSTNESS 25 1.13 SUMMARY 27
REFERENCES 28
2 LINEAR SISO MODEL DESCRIPTIONS 29 2.1 NONPARAMETRIC SYSTEM DESCRIPTION
31 2.1.1 FIR MODEL 31 2.1.2 FSR MODEL 33
2.1.3 RELATIONSHIP BETWEEN THE FIRS AND THE FSRS 35 2.1.4 DISTURBANCE
MODEL 36
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/987213903
DIGITALISIERT DURCH
IMAGE 2
VI I CONTENTS
2.2 PULSE-TRANSFER FUNCTION MODEL 36
2.2.1 PULSE-TRANSFER FUNCTION AND DIFFERENCE EQUATION 37 2.2.2
RELATIONSHIP BETWEEN THE PULSE-TRANSFER FUNCTION, THE WEIGHTING
FUNCTION, AND THE STEP RESPONSE MODELS 38 2.2.3 DISTURBANCE MODEL 41 2.3
DISCRETE-TIME STATE SPACE MODEL 43
2.3.1 MINIMAL-ORDER STATE SPACE REPRESENTATION 44 2.3.2
NON-MINIMAL-ORDER STATE SPACE REPRESENTATIONS 48 2.4 SUMMARY 54
REFERENCES 54
PREDICTIVE EQUATIONS OF LINEAR SISO MODELS 55 3.1 PREDICTIVE EQUATIONS
BASED ON NONPARAMETRIC MODELS 56 3.1.1 PREDICTIVE EQUATIONS OF THE
IMPULSE RESPONSE MODEL 56 3.1.2 PREDICTIVE EQUATIONS OF THE STEP
RESPONSE MODEL 61
3.2 PREDICTIVE EQUATIONS BASED ON THE PULSE-TRANSFER FUNCTION 64 3.2.1
REPEATED SUBSTITUTION OF THE PROCESS MODEL EQUATION 65 3.2.2 PREDICTION
BY SOLVING THE DIOPHANTINE EQUATION 70 3.2.3 PREDICTION IF THE ADDITIVE
NOISE IS AUTOREGRESSIVE 74
3.2.4 PREDICTION IN THE PRESENCE OF A MEASURABLE DISTURBANCE 78 3.2.5
PREDICTION IF THE ADDITIVE NOISE IS NONAUTOREGRESSIVE 81 3.2.6 MATRIX
CALCULATION METHOD 88 3.3 PREDICTIVE EQUATIONS OF THE STATE SPACE MODEL
93 3.4 SUMMARY 99
REFERENCES 101
PREDICTIVE ON-OFF CONTROL 103 4.1 CLASSICAL ON-OFF CONTROL BY MEANS OF
RELAY CHARACTERISTICS 104 4.2 PREDICTIVE SET POINT CONTROL 107 4.2.1
COST FUNCTION MINIMIZATION BY A SELECTION STRATEGY 108
4.2.2 COST FUNCTION MINIMIZATION BY A GENETIC ALGORITHM 110 4.2.3
SIMULATION AND COMPARISON OF THE PREDICTIVE SET POINT CONTROL ALGORITHMS
110 4.3 PREDICTIVE START-UP CONTROL AT A REFERENCE SIGNAL CHANGE 112 4.4
PREDICTIVE GAP CONTROL 118
4.4.1 QUADRATIC COST FUNCTION MINIMIZATION BY THE SELECTION STRATEGY OR
THE GENETIC ALGORITHM 118 4.4.2 QUASI CONTINUOUS-TIME OPTIMIZATION 119
4.4.3 MINIMIZING A LIMIT-VIOLATION-TIME-POINT-DEPENDENT
COST FUNCTION 121 4.4.4 ONLINE START-UP STRATEGY 124 4.4.5 COMPARISON OF
THE PREDICTIVE ON-OFF GAP CONTROL
ALGORITHMS 126
4.5 CASE STUDY: TEMPERATURE CONTROL OF AN ELECTRICAL HEAT EXCHANGER 131
IMAGE 3
CONTENTS | VII
4.6 SUMMARY 133
REFERENCES 134
GENERALIZED PREDICTIVE CONTROL OF LINEAR SISO PROCESSES 135 5.1 CONTROL
ALGORITHM WITHOUT CONSTRAINTS 135 5.2 LINEAR POLYNOMIAL FORM OF
UNCONSTRAINED GPC 142 5.3 TUNING THE CONTROLLER PARAMETERS 151
5.3.1 EFFECT OF THE CONTROLLER PARAMETERS ON THE CONTROL BEHAVIOR 151
5.3.2 RECOMMENDED INITIAL PARAMETERS 161 5.3.3 SYSTEMATIC TUNING OF THE
CONTROLLER PARAMETERS 163 5.3.4 OPTIMIZATION OF THE CONTROLLER
PARAMETERS BY A GENETIC
ALGORITHM 165 5.3.5 SIMPLE RULE OF THUMB FOR TUNING SECOND-ORDER
APERIODIC PROCESSES 172 5.4 BLOCKING AND COINCIDENCE POINTS TECHNIQUES
173
5.4.1 COINCIDENCE POINTS TECHNIQUE 175 5.4.2 BLOCKING TECHNIQUE 376
5.4.3 SIMULTANEOUS USE OF COINCIDENCE POINTS AND BLOCKING TECHNIQUES 178
5.4.4 SOME HEURISTIC RULES ON ALLOCATION OF THE COINCIDENCE POINTS 179
5.4.5 SOME BLOCKING TECHNIQUES 182 5.4.6 OPTIMAL CHOICE OF THE
COINCIDENCE POINTS
AND THE BLOCKING TECHNIQUE 283 5.5 MEASURED DISTURBANCE FEED-FORWARD
COMPENSATION 188 5.6 CONTROL ALGORITHM WITH CONSTRAINTS 193 5.6.1
PHYSICAL CONSTRAINTS 193
5.6.2 MATHEMATICAL FORMULATION OF TYPICAL CONSTRAINTS 194 5.6.3 HARD AND
SOFT CONSTRAINTS 198 5.6.4 EXPLICIT MPC WITH MULTIPARAMETRIC PROGRAMMING
WITHOUT ONLINE OPTIMIZATION 200
5.6.5 SIMULATION RESULTS 207 5.7 EXTENDED GPC WITH TERMINAL METHODS 213
5.7.1 END-POINT STATE CONSTRAINT 213 5.7.2 END-POINT STATE WEIGHTING 216
5.8 SUMMARY 218 REFERENCES 219
PREDICTIVE PID CONTROL ALGORITHMS 221 6.1 PREDICTIVE PI(D) CONTROL
STRUCTURE 222 6.2 PREDICTIVE PI CONTROL ALGORITHM 222 6.2.1
ONE-STEP-AHEAD PREDICTIVE PI CONTROL 223
6.2.2 LONG-RANGE PREDICTIVE PI CONTROL 224 6.2.3 PREDICTIVE PI CONTROL
OF A FIRST-ORDER PROCESS WITH DEAD TIME 225 6.3 PREDICTIVE PID CONTROL
ALGORITHM 228
IMAGE 4
VIII I CONTENTS
6.3.1 ONE-STEP-AHEAD PREDICTIVE PID CONTROL 229
6.3.2 LONG-RANGE PREDICTIVE PID CONTROL 230 6.3.3 PREDICTIVE PID CONTROL
OF A SECOND-ORDER PROCESS WITH DEAD TIME 231 6.4 EQUIVALENCE BETWEEN THE
PREDICTIVE PI(D) ALGORITHM AND THE GENERALIZED PREDICTIVE CONTROL
ALGORITHM 234 6.4.1 EQUIVALENCE IN THE CASE OF EXTENDED HORIZON CONTROL
234 6.4.2 EQUIVALENCE IN THE CASE OF LONG-RANGE HORIZON CONTROL 236 6.5
TUNING OF PREDICTIVE PI(D) ALGORITHMS 240 6.6 ROBUSTIFYING EFFECTS
APPLIED FOR PREDICTIVE PI(D) CONTROL ALGORITHMS 245 6.7 SUMMARY 249
REFERENCES 249
7 PREDICTIVE CONTROL OF MULTIVARIABLE PROCESSES 251 7.1 MODEL
DESCRIPTIONS 252 7.1.1 MATRIX FRACTION MODEL 252 7.1.2 STATE SPACE
REPRESENTATION 255 7.2 PREDICTIVE EQUATIONS 260
7.2.1 PREDICTION BASED ON THE MATRIX FRACTION MODEL 260 7.2.2 PREDICTION
BASED ON THE STATE SPACE REPRESENTATION 271 7.3 THE CONTROL ALGORITHM
277 7.4 POLYNOMIAL FORM OF THE CONTROLLER (WITHOUT MATRIX INVERSION) 284
7.5 PAIRING OF THE CONTROLLED AND THE MANIPULATED VARIABLES 287 7.6
SCALING OF THE CONTROLLED AND THE MANIPULATED VARIABLES 290 7.7 TUNING
294 7.8 DECOUPLING CONTROL 297
7.8.1 DECOUPLING BY USING STATIC COMPENSATORS 300 7.8.2 DECOUPLING BY
USING A DYNAMIC COMPENSATOR 308 7.8.3 DECOUPLING BY SLOWING THE
REFERENCE SIGNAL CHANGE 312 7.8.4 DECOUPLING BY CHANGING THE CONTROL
ERROR WEIGHTING FACTORS
AT ANY SET POINT CHANGE 325 7.8.5 DECOUPLING BY CONTROL-ERROR-DEPENDENT
ADAPTATION OF THE CONTROL ERROR WEIGHTING FACTORS 316 7.9 CASE STUDY:
CONTROL OF A DISTILLATION COLUMN 318
7.9.1 DISTILLATION COLUMN MODEL 328 7.9.2 PREDICTIVE TITO CONTROL 322
7.9.3 DECOUPLING BY USING STATIC COMPENSATORS 323 7.9.4 DECOUPLING BY
CONTROL-ERROR-DEPENDENT ADAPTATION
OF THE CONTROL ERROR WEIGHTING FACTORS 328 7.9.5 SUMMARY OF THE
EXPERIENCES 330 7.10 SUMMARY 332 REFERENCES 332
IMAGE 5
CONTENTS | IX
8 ESTIMATION OF THE PREDICTIVE EQUATIONS 335
8.1 LS PARAMETER ESTIMATION 336 8.2 MORE-STEPS-AHEAD PREDICTION BASED ON
THE ESTIMATED PROCESS MODEL 337 8.3 LONG-RANGE OPTIMAL SINGLE PROCESS
MODEL IDENTIFICATION 339 8.4 MULTI-STEP-AHEAD PREDICTIVE EQUATION
IDENTIFICATION 344 8.5 COMPARISON OF THE LONG-RANGE OPTIMAL
IDENTIFICATION ALGORITHMS 346 8.6 CASE STUDY: LEVEL CONTROL IN A
TWO-TANK PLANT 348 8.7 SUMMARY 350
REFERENCES 350
9 MULTIMODEL AND MULTICONTROLLER APPROACHES 353 9.1 NONLINEAR PROCESS
MODELS 354 9.1.1 MODELS WITH SIGNAL-DEPENDENT AND DIRECTION-DEPENDENT
PARAMETERS 354
9.1.2 MULTIMODELS 355 9.1.3 MULTIPARAMETER MODELS 358 9.2 PREDICTIVE
EQUATIONS 359 9.3 THE CONTROL ALGORITHM 359
9.3.1 CONTROL BASED ON THE MULTIMODEL APPROACH 359 9.3.2 CONTROL BASED
ON THE MULTIPARAMETER MODEL 360 9.3.3 MULTICONTROLLER APPROACH 360 9.4
CASE STUDY 362 9.4.1 EXPERIMENTAL MODELING OF AN ELECTRICALLY HEATED
HEAT
EXCHANGER 362 9.4.2 TEMPERATURE CONTROL OF AN ELECTRICALLY HEATED HEAT
EXCHANGER 370 9.4.3 TEMPERATURE CONTROL WITH FORCED HEATING
AND FREE CONVECTIVE COOLING 375 9.5 SUMMARY 379 REFERENCES 380
10 GPC OF NONLINEAR SISO PROCESSES 383 10.1 NONLINEAR PROCESS MODELS 383
10.1.1 NONPARAMETRIC PROCESS MODELS 384 10.1.2 CASCADE PROCESS MODELS
385
10.1.3 QUADRATIC BLOCK-ORIENTED PROCESS MODELS 386 10.1.4
LINEAR-IN-PARAMETERS MODELS INCLUDING MULTIPLICATIVE TERMS 388 10.2
PREDICTIVE EQUATIONS FOR THE NONPARAMETRIC AND PARAMETRIC HAMMERSTEIN
AND VOLTERRA MODELS 392 10.2.1 PREDICTIVE EQUATION USING THE INPUT
SIGNAL 392 10.2.2 PREDICTIVE EQUATION USING THE INPUT INCREMENTS 397
10.2.3 COMMON INCREMENTAL PREDICTIVE FORM OF THE QUADRATIC
MODELS 405
IMAGE 6
X I CONTENTS
10.3 CONTROL BASED ON NONPARAMETRIC
AND PARAMETRIC HAMMERSTEIN AND VOLTERRA MODELS 406 10.3.1
ONE-DIMENSIONAL MINIMIZATION OF THE COST FUNCTION 406 10.3.2
MULTIDIMENSIONAL MINIMIZATION OF THE COST FUNCTION 420 10.3.3 SIMULATION
RESULTS 424 10.4 CONTROL BASED ON LINEARIZED MODELS 422 10.5 CONTROL
BASED ON NONLINEAR FREE AND LINEARIZED FORCED
RESPONSES 422 10.6 CASE STUDY: LEVEL CONTROL OF A TWO-TANK PLANT 424
10.6.1 NONLINEAR PROCESS MODEL OF THE TWO-TANK PROCESS 424
10.6.2 LEVEL CONTROL OF THE TWO-TANK PROCESS 426 10.6.3 LEVEL CONTROL
BASED ON LINEARIZED MODELS 430 10.6.4 LEVEL CONTROL BASED ON NONLINEAR
FREE AND LINEARIZED FORCED RESPONSES 431 10.7 SUMMARY 433
REFERENCES 434
11 PREDICTIVE FUNCTIONAL CONTROL 437 11.1 CONTROL STRATEGY AND
CONTROLLER PARAMETERS FOR A CONSTANT SET POINT 438 11.2 PFC FOR
APERIODIC PROCESSES 443
11.2.1 PFC FOR FIRST-ORDER PROCESSES WITHOUT DEAD TIME 443 11.2.2 PFC
FOR HIGHER-ORDER APERIODIC PROCESSES WITHOUT DEAD TIME 446
11.2.3 PFC FOR A FIRST-ORDER PROCESS WITH DEAD TIME 449 11.3 PFC WITH
DISTURBANCE FEED-FORWARD 452 11.3.1 ALGORITHM FOR FIRST-ORDER PROCESS
AND DISTURBANCE MODELS WITHOUT DEAD TIME 452
11.3.2 ALGORITHM FOR FIRST-ORDER PROCESS AND DISTURBANCE MODELS WITH
DEAD TIME 452 11.4 PFC WITH CONSTRAINTS 454 11.5 NONLINEAR PFC FOR
PROCESSES WITH SIGNAL-DEPENDENT PARAMETERS 457
11.6 CASE STUDY: TEMPERATURE CONTROL OF A HOT AIR BLOWER 458 11.6.1
PHYSICAL MODEL OF THE PILOT PLANT 459 11.6.2 LINEAR PROCESS AND
DISTURBANCE MODEL IDENTIFICATION 459 11.6.3 SIMULATION OF LINEAR
TEMPERATURE CONTROL 460 11.6.4 LINEAR TEMPERATURE CONTROL WITH A REAL
PROCESS SYSTEM 462 11.6.5 NONLINEAR PROCESS MODEL IDENTIFICATION 462
11.6.6 NONLINEAR TEMPERATURE CONTROL WITH A REAL PROCESS
SYSTEM 463
11.7 SUMMARY 464 REFERENCES 465
12 CASE STUDIES 467 12.1 PREDICTIVE TEMPERATURE CONTROL OF AN INJECTION
MOLDING MACHINE 468 12.1.1 MODELING OF THE PLASTIFICATION UNIT 469
IMAGE 7
CONTENTS I XI
12.1.2 PREDICTIVE ZONE TEMPERATURE CONTROL 473
12.1.3 PREDICTIVE START-UP CONTROL WITH TEMPERATURE DIFFERENCE
LIMITATION 475 12.1.4 SUMMARY 477 12.2 WASTEWATER QUALITY CONTROL OF AN
INTERMITTENTLY OPERATED PLANT 477
12.2.1 MODELING OF THE PLANT 479 12.2.2 CONVENTIONAL ON-OFF CONTROL 480
12.2.3 PREDICTIVE ON-OFF CONTROL BASED ON THE SIMULATED PHYSICAL MODEL
481 12.2.4 PREDICTIVE ON-OFF CONTROL BASED ON A SIMPLIFIED MODEL 482
12.2.5 SUMMARY 487 12.3 WASTEWATER QUALITY CONTROL WITH
PRE-DENITRIFICATION 488 12.3.1 MODELING OF THE PLANT 489 12.3.2
COMPARISON OF THE CONVENTIONAL
AND PREDICTIVE THREE-LEVEL CONTROL 489 12.3.3 SUMMARY 492 REFERENCES 494
13 INDUSTRIAL APPLICATIONS 497 13.1 CONCENTRATION CONTROL AND PRESSURE
MINIMIZATION OF A PETROCHEMICAL DISTILLATION COLUMN 498
13.1.1 DESCRIPTION OF THE DISTILLATION COLUMN 499 13.1.2 CONTROL
TARGETS, CONTROLLED AND MANIPULATED SIGNALS, CONSTRAINTS 500 13.1.3 THE
PROCESS MODEL 504 13.1.4 CHOICE OF THE SET VALUES, CONSTRAINTS, AND
WEIGHTING
FACTORS 506
13.1.5 SIMULATION RESULTS OFTHE TUNING OF THE WEIGHTING FACTORS 508
13.1.6 IMPLEMENTATION AND RESULTS OF REAL-TIME CONTROL 511 13.1.7
ECONOMIC BENEFITS OFTHE OPTIMIZATION 513 13.1.8 SUMMARY 524 13.2
CONCENTRATION CONTROL AND REDUCING STEAM CONSUMPTION
IN A STRIPPING COLUMN 525 13.2.1 DESCRIPTION OFTHE STRIPPING COLUMN AND
THE BASIC CONTROL LOOPS 525 13.2.2 CONTROL TARGETS, CONTROLLED AND
MANIPULATED SIGNALS,
CONSTRAINTS 526 13.2.3 PROCESS MODELS FROM STEP TESTS 529 13.2.4
HIERARCHICAL MINIMIZATION OFTHE CONTROL COST FUNCTION 520 13.2.5 SET
POINT CONTROL CONSIDERING THE CONSTRAINTS 522 13.2.6 THE CONTROLLER
PARAMETERS 523
13.2.7 REAL-TIME CONTROL 523 13.2.8 ECONOMIC BENEFITS AND SUMMARY 525
IMAGE 8
XII I CONTENTS
13.3 TEMPERATURE AND COMBUSTION CONTROL
OF A GAS-HEATED FURNACE FOR CHEMICAL GASOLINE 527 13.3.1 THE FURNACE
WITH THE REACTOR AND CONTROL TARGETS 528 13.3.2 MANIPULATED, CONTROLLED,
AND DISTURBANCE SIGNALS 529 13.3.3 PROCESS MODEL 530 13.3.4 PREDICTIVE
CONTROL ALGORITHM AND THE CHOICE
OFTHE SET VALUES, CONSTRAINTS, AND WEIGHTING FACTORS 534 13.3.5
SIMULATION OF SET VALUE, DISTURBANCE CHANGES, AND CONSTRAINT VIOLATION
539 13.3.6 THE EFFECT OF THE WEIGHTING FACTORS OF THE CONTROLLED,
MANIPULATED, AND CONSTRAINED SIGNALS 544 13.3.7 REAL-TIME PREDICTIVE
CONTROL 549 13.3.8 SUMMARY 551 REFERENCES 552
14 PRACTICAL ASPECTS AND FUTURE TRENDS 553 14.1 CLASSIFICATION OF A
PREDICTIVE CONTROL PROJECT 553 14.2 PROJECT IMPLEMENTATION 559 14.2.1
APC PROJECT CHAIN 562
14.2.2 ACHIEVABLE BENEFITS, AN EXAMPLE 565 14.3 IMPLEMENTATION OF A
PREDICTIVE CONTROLLER 566 14.3.1 CONTROL AIM AND OPTIMIZATION 566 14.3.2
ADVANCED OPTIMIZATION FEATURES 569
14.3.3 SIGNAL SCALING AND WEIGHTING 572 14.3.4 HANDLING OF 111
CONDITIONS 574 14.3.5 HANDLING OF CONSTRAINTS 575 14.3.6 HANDLING OF SET
POINT OR SET RANGE CHANGES 578 14.3.7 TECHNIQUES TO REDUCE COMPUTATIONAL
EFFORT 580 14.3.8 DEALING WITH INNER LOOP PERFORMANCE 581 14.4 FUTURE
TRENDS 585
14.4.1 IDENTIFICATION AND TESTING OF THE PLANT 585 14.4.2 NONLINEAR MPC
592 14.4.3 INTEGRATION OF A PREDICTIVE CONTROLLER INTO THE BASE LAYER
592
14.4.4 PERFORMANCE MONITORING 593 14.5 SUMMARY 594 REFERENCES 595
INDEX 597 |
| any_adam_object | 1 |
| author | Haber, Robert Bars, Ruth Schmitz, Ulrich |
| author_GND | (DE-588)1015810292 |
| author_facet | Haber, Robert Bars, Ruth Schmitz, Ulrich |
| author_role | aut aut aut |
| author_sort | Haber, Robert |
| author_variant | r h rh r b rb u s us |
| building | Verbundindex |
| bvnumber | BV039669108 |
| classification_rvk | ZQ 9910 |
| ctrlnum | (OCoLC)751856676 (DE-599)DNB987213903 |
| dewey-full | 660.2815 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 660 - Chemical engineering |
| dewey-raw | 660.2815 |
| dewey-search | 660.2815 |
| dewey-sort | 3660.2815 |
| dewey-tens | 660 - Chemical engineering |
| discipline | Chemie / Pharmazie Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| format | Book |
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| id | DE-604.BV039669108 |
| illustrated | Illustrated |
| indexdate | 2024-07-21T00:14:21Z |
| institution | BVB |
| isbn | 352731492X 9783527314928 9783527636242 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-024518310 |
| oclc_num | 751856676 |
| open_access_boolean | |
| owner | DE-83 DE-703 DE-634 DE-1102 DE-92 DE-1046 DE-29T |
| owner_facet | DE-83 DE-703 DE-634 DE-1102 DE-92 DE-1046 DE-29T |
| physical | XXIX, 600 S. Ill., graph. Darst. |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | Wiley-VCH |
| record_format | marc |
| spelling | Haber, Robert Verfasser aut Predictive control in process engineering from the basics to the applications Robert Haber, Ruth Bars, and Ulrich Schmitz Weinheim Wiley-VCH 2011 XXIX, 600 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Literaturangaben Verfahrenstechnik (DE-588)4062781-0 gnd rswk-swf Prädiktive Regelung (DE-588)4271836-3 gnd rswk-swf Prädiktive Regelung (DE-588)4271836-3 s Verfahrenstechnik (DE-588)4062781-0 s DE-604 Bars, Ruth Verfasser aut Schmitz, Ulrich Verfasser (DE-588)1015810292 aut Erscheint auch als Online-Ausgabe, EPUB 978-3-527-63625-9 Erscheint auch als Online-Ausgabe, PDF 978-3-527-63626-6 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=3060778&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024518310&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Haber, Robert Bars, Ruth Schmitz, Ulrich Predictive control in process engineering from the basics to the applications Verfahrenstechnik (DE-588)4062781-0 gnd Prädiktive Regelung (DE-588)4271836-3 gnd |
| subject_GND | (DE-588)4062781-0 (DE-588)4271836-3 |
| title | Predictive control in process engineering from the basics to the applications |
| title_auth | Predictive control in process engineering from the basics to the applications |
| title_exact_search | Predictive control in process engineering from the basics to the applications |
| title_full | Predictive control in process engineering from the basics to the applications Robert Haber, Ruth Bars, and Ulrich Schmitz |
| title_fullStr | Predictive control in process engineering from the basics to the applications Robert Haber, Ruth Bars, and Ulrich Schmitz |
| title_full_unstemmed | Predictive control in process engineering from the basics to the applications Robert Haber, Ruth Bars, and Ulrich Schmitz |
| title_short | Predictive control in process engineering |
| title_sort | predictive control in process engineering from the basics to the applications |
| title_sub | from the basics to the applications |
| topic | Verfahrenstechnik (DE-588)4062781-0 gnd Prädiktive Regelung (DE-588)4271836-3 gnd |
| topic_facet | Verfahrenstechnik Prädiktive Regelung |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=3060778&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024518310&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT haberrobert predictivecontrolinprocessengineeringfromthebasicstotheapplications AT barsruth predictivecontrolinprocessengineeringfromthebasicstotheapplications AT schmitzulrich predictivecontrolinprocessengineeringfromthebasicstotheapplications |