000			07751nam a2200301 i 4500
007			co uu-g--u----
008			150211s2004 gw m b a001 0 eng d
010			_a2003059068
020			_a9783642621260 _qalk. paper
035			_a(OCoLC)
040			_aDLC _cDLC _dYDX _dUKM _dOHX _dCIN _dBAUN _beng _erda
049			_aBAUN_MERKEZ
050	0	0	_aTS156.8 _b.R63 2004
082	0	0	_222
100	1		_aRoffel, Brian.
245	1	0	_aAdvanced practical process control / _cB. Roffel, B.H. Betlem.
264		1	_aBerlin ; _aNew York : _bSpringer, _cc2004.
300			_aix, 309 pages : _billustrations ; _c24 cm.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aother _bcz _2rdacarrier
490	0		_aEngineering online library.
504			_aIncludes bibliographical references and index.
505	0	0	_t1 Introduction to Advanced Process Control Concepts _t-- 1.1 Process Time Constant _t-- 1.2 Domain Transformations _t-- 1.3 Laplace Transformation _t-- 1.4 Discrete Approximations _t-- 1.5 z-Transforms _t-- 1.6 Advanced and Modified z-Transforms _t-- 1.7 Common Elements in Control _t-- 1.8 The Smith Predictor _t-- 1.9 Feed-forward Control _t-- 1.10 Feed-forward Control in a Smith Predictor _t-- 1.11 Dahlin's Control Algorithm _t-- References _t-- 2 Process Simulation _t-- 2.1 Simulation using Matlab Simulink _t-- 2.2 Simulation of Feed-forward Control _t-- 2.3 Control Simulation of a 2x2 System _t-- 2.4 Simulation of Dahlin's Control Algorithm _t-- 3 Process Modeling and Identification _t-- 3.1 Model Applications _t-- 3.2 Types of Models _t-- 3.2.1 White Box and Black Box Models _t-- 3.2.2 Linear and Non-linear Models _t-- 3.2.3 Static and Dynamic Models _t-- 3.2.4 Distributed and Lumped Parameter Models _t-- 3.2.5 Continuous and Discrete Models _t-- 3.3 Empirical (linear) Dynamic Models _t-- 3.4 Model Structure Considerations _t-- 3.4.1 Parametric Models _t-- 3.4.2 Non-parametric Models _t-- 3.5 Model Identification _t-- 3.5.1 Introduction _t-- 3.5.2 Identification of Parametric Models _t-- 3.5.3 Identification of Non-parametric Models _t-- References _t-- 4 Identification Examples _t-- 4.1 SISO Furnace Parametric Model Identification _t-- 4.2 MISO Parametric Model Identification _t-- 4.3 MISO Non-parametric Identification of a Non-integrating Process _t-- 4.4 MIMO Identification of an Integrating and Non-integrating Process _t-- 4.5 Design of Plant Experiments _t-- 4.5.1 Nature of Input Sequence _t-- 4.5.2 PRBS Type Input _t-- 4.5.3 Step Type Input _t-- 4.5.4 Type of Experiment _t-- 4.6 Data File Layout _t-- 4.7 Conversion of Model Structures _t-- 4.8 Example and Comparison of Open and Closed Loop Identification _t-- References _t-- 5 Linear Multivariable Control _t-- 5.1 Interaction in Multivariable Systems _t-- 5.1.1 The Relative Gain Array _t-- 5.1.2 Properties of the Relative Gain Array _t-- 5.1.3 Some Examples _t-- 5.1.4 The Dynamic Relative Gain Array _t-- 5.2 Dynamic Matrix Control _t-- 5.2.1 Introduction _t-- 5.2.2 Basic DMC Formulation _t-- 5.2.3 One Step DMC _t-- 5.2.4 Prediction Equation and Unmeasurable Disturbance Estimation _t-- 5.2.5 Restriction of Excessive Moves _t-- 5.2.6 Expansion of DMC to Multivariable Problems _t-- 5.2.7 Equal Concern Errors _t-- 5.2.8 Constraint Handling _t-- 5.2.9 Constraint Formulation _t-- 5.3 Properties of Commercial MPC Packages _t-- References _t-- 6 Multivariable Optimal Constraint Control Algorithm _t-- 6.1 General Overview _t-- 6.2 Model Formulation for Systems with Dead Time _t-- 6.3 Model Formulation for Multivariable Processes _t-- 6.4 Model Formulation for Multivariable Processes with Time Delays _t-- 6.5 Model Formulation in Case of a Limited Control Horizon _t-- 6.6 Mocca Control Formulation _t-- 6.7 Non-linear Transformations _t-- 6.8 Practical Implementation Guidelines _t-- 6.9 Case Study _t-- 6.10 Control of a Fluidized Catalytic Cracker _t-- 6.11 Examples of Case Studies in MATLAB _t-- 6.12 Control of Integrating Processes _t-- 6.13 Lab Exercises _t-- 6.14 Use of MCPC for Constrained Multivariable Control _t-- References _t-- 7 Internal Model Control _t-- 7.1 Introduction _t-- 7.2 Factorization of Multiple Delays _t-- 7.3 Filter Design _t-- 7.4 Feed-forward IMC _t-- 7.5 Example of Controller Design _t-- 7.6 LQ Optimal Inverse Design _t-- References _t-- 8 Nonlinear Multivariable Control _t-- 8.1 Non-linear Model Predictive Control _t-- 8.2 Non-linear Quadratic DMC _t-- 8.3 Generic Model Control _t-- 8.3.1 Basic Algorithm _t-- 8.3.2 Examples of the GMC Algorithm _t-- 8.3.3 The Differential Geometry Concept _t-- 8.4 Problem Description _t-- 8.4.1 Model Representation _t-- 8.4.2 Process Constraints _t-- 8.4.3 Control Objectives _t-- 8.5 GMC Application to the CSTR System _t-- 8.5.1 Relative Degree of the CSTR System _t-- 8.5 2 Cascade Control Algorithm _t-- 8.6 Discussion of the GMC Algorithm _t-- 8.7 Simulation of Reactor Control _t-- 8.8 One Step Reference Trajectory Control _t-- 8.9 Predictive Horizon Reference Trajectory Control _t-- References _t-- 9 Optimization of Process Operation _t-- 9.1 Introduction to Real-time Optimization _t-- 9.1.1 Optimization and its Benefits _t-- 9.1.2 Hierarchy of Optimization _t-- 9.1.3 Issues to be Addressed in Optimization _t-- 9.1.4 Degrees of Freedom Selection for Optimization _t-- 9.1.5 Procedure for Solving Optimization Problems _t-- 9.1.6 Problems in Optimization _t-- 9.2 Model Building _t-- 9.2.1 Phases in Model Development _t-- 9.2.2 Fitting Functions to Empirical Data _t-- 9.2.3 The Least Squares Method _t-- 9.3 The Objective Function _t-- 9.3.1 Function Extrema _t-- 9.3.2 Conditions for an Extremum _t-- 9.4 Unconstrained Functions: one Dimensional Problems _t-- 9.4.1 Newton's Method _t-- 9.4.2 Quasi-Newton Method _t-- 9.4.3 Polynomial Approximation _t-- 9.5 Unconstrained Multivariable Optimization _t-- 9.5.1 Introduction _t-- 9.5.2 Newton's Method _t-- 9.6 Linear Programming _t-- 9.6.1 Example _t-- 9.6.2 Degeneracies _t-- 9.6.3 The Simplex Method _t-- 9.6.4 The Revised Simplex Method _t-- 9.6.5 Sensitivity Analysis _t-- 9.7 Non-linear Programming _t-- 9.7.1 The Lagrange Multiplier Method _t-- 9.7.2 Other Techniques _t-- 9.7.3 Hints for Increasing the Effectiveness of NLP Solutions _t-- References _t-- 10 Optimization Examples _t-- 10.1 AMPL: a Multi-purpose Optimizer _t-- 10.1.1 Example of an Optimization Problem _t-- 10.1.2 AMPL Formulation of the Problem _t-- 10.1.3 General Structure of an AMPL Model _t-- 10.1.4 General AMPL Rules _t-- 10.1.5 Detailed Review of the Transportation Example _t-- 10.2 Optimization Examples _t-- 10.2.1 Optimization of a Separation Train _t-- 10.2.2 A Simple Blending Problem _t-- 10.2.3 A Simple Alkylation Reactor Optimization _t-- 10.2.4 Gasoline Blending _t-- 10.2.5 Optimization of a Thermal Cracker _t-- 10.2.6 Steam Net Optimization _t-- 10.2.7 Turbogenerator Optimization _t-- 10.2.8 Alkylation Plant Optimization _t-- References _t-- 11 Integration of Control and Optimization _t-- 11.1 Introduction _t-- 11.2 Description of the Desalination Plant _t-- 11.3 Production Maximization of Desalination Plant _t-- 11.4 Linear Model Predictive Control of Desalination Plant _t-- 11.5 Reactor problem definition _t-- 11.6 Multivariable Non-linear Control of the Reactor _t-- References _t-- Appendix I. MCPC software guide _t-- I.1 Installation _t-- I.2 Model identification _t-- I.2.1 General process information _t-- I.2.2 Identification data _t-- I.2.3 Output details _t-- I.3 Controller design _t-- I.4 Control simulation _t-- I.5 Dealing with constraints _t-- I.6 Saving a project _t-- Appendix II. Comparison of control strategies for a hollow shaft reactor _t-- II.1 Introduction _t-- II.2 Model Equations _t-- II.3 Proportional Integral Control _t-- II.4 Linear Multivariable Control _t-- II.5 Non-linear Multivariable Control _t-- References.
650		0	_aProcess control _95682
700	1		_aBetlem, B. H. _q(Ben H.)
942			_2lcc _cKT
999			_c33667 _d33667