Detailed resource on the “Why,” “What,” and “How” of integrated process modeling, advanced control and data analytics explained via hands-on examples and workshops for optimizing polyolefin manufacturing.
Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing discusses, as well as demonstrates, the optimization of polyolefin production by covering topics from polymer process modeling and advanced process control to data analytics and machine learning, and sustainable design and industrial practice. The text also covers practical problems, handling of real data streams, developing the right level of detail, and tuning models to the available data, among other topics, to allow for easy translation of concepts into practice.
Written by two highly qualified authors, Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing includes information on: - Segment-based modeling of polymer processes; selection of thermodynamic methods; estimation of physical properties for polymer process modeling - Reactor modeling, convergence tips and data-fit tool; free radical polymerization (LDPE, EVA and PS), Ziegler-Natta polymerization (HDPE, PP, LLPDE, and EPDM) and ionic polymerization (SBS rubber) - Improved polymer process operability and control through steady-state and dynamic simulation models - Model-predictive control of polyolefin processes and applications of multivariate statistics and machine learning to optimizing polyolefin manufacturing
Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing enables readers to make full use of advanced computer models and latest data analytics and machine learning tools for optimizing polyolefin manufacturing, making it an essential resource for undergraduate and graduate students, researchers, and new and experienced engineers involved in the polyolefin industry.
Table of Contents
Volume 1
Foreword xvii
Preface xxxiii
Acknowledgment xxxvii
Copyright Notice xxxix
About the Authors xli
About the Companion Website xliii
1 Introduction to Integrated Process Modeling, Advanced Control, and Data Analytics in Optimizing Polyolefin Manufacturing 1
1.1 Segment-Based Modeling of Polymerization Processes: Component Characterization and Polymer Attributes 1
1.1.1 Component Types in Polymer Process Modeling 1
1.1.2 Concept of Moments and Some Basic Polymer Attributes 3
1.1.3 Stream Initialization and Basic Polymer Attributes 5
1.2 Workshop 1.1: Finding the Resulting Stream Attributes After Mixing Two Copolymer Streams 7
1.2.1 Objective 7
1.2.2 Problem Statement 7
1.2.3 Process Flowsheet 7
1.2.4 Unit System, Components, and Characterization of Copolymers 7
1.2.5 Property Method and Property Parameters for Components 9
1.2.6 Specifications of Streams and Blocks 11
1.3 Workshop 1.2: A Simplified Simulation Model for a Slurry HDPE Process and the Workflow for Developing a Polymer Process Simulation Model 16
1.3.1 Objective 16
1.3.2 Step 1: Problem Setup 16
1.3.3 Step 2: Component Specifications 18
1.3.4 Step 3: Property Method 20
1.3.5 Step 4: Property Parameters - Obtaining Values from Databanks and Estimating Missing Parameters 20
1.3.6 Step 5: Verification of the Accuracy of the Selected Property Method by Comparing Predicted Pure-Component Property Values with Report Experimental Data 21
1.3.7 Step 6: Regress Component Liquid Density Data and Binary Vapor- Liquid Equilibrium (TPXY) Data to Estimate Missing Pure-component and Binary Interaction Parameters of Selected Property Method and Verify Predicted VLE Results with Experimental Data 22
1.3.8 Step 7: Develop Correlations for Polymer Product Quality Indices, Such as Density and Melt Index (Melt Flow Rate) Based on Plant Data 22
1.3.9 Step 8: Define the Polymerization Reactions and Enter the Initial Reaction Rate Constants 22
1.3.10 Step 9: Draw the Open-Loop Process Flowsheet and Enter the Inputs for Streams and Blocks 24
1.3.11 Step 10: Run the Initial Open-loop Process Simulation and Check if the Simulation Results Are Reasonable 25
1.3.12 Step 11: Close the Recycled Loops, Finalize a Converged Closed-loop Steady-state Simulation Model, and Investigate Applications to Improving Process Operations and Identifying Operating Conditions for New Product Design 26
1.3.13 Step 12: Convert the Steady-state Simulation Model in Aspen Plus to a Dynamic Simulation Model in Aspen Plus Dynamics; Add Appropriate Controllers; and Investigate Process Operability, Control, and Grade Changes 26
1.4 Industrial and Potential Applications of Integrated Process Modeling, Advanced Control, and Data Analytics to Optimizing Polyolefin Manufacturing 26
1.4.1 Industrial and Potential Applications of Process Modeling to Optimizing Polyolefin Manufacturing 26
1.4.2 Industrial and Potential Applications of Advanced Process Control to Optimizing Polyolefin Manufacturing 28
1.4.3 Industrial and Potential Applications of Data Analytics to Optimizing Polyolefin Manufacturing 31
1.4.4 Hybrid Modeling: Integrated Applications of Process Modeling, Advanced Control, and Data Analytics to Optimizing Polyolefin Manufacturing 34
References 36
2 Selection of Property Methods and Estimation of Physical Properties for Polymer Process Modeling 41
2.1 Property Methods and Thermophysical Parameter Requirements for Process Simulation 41
2.2 Polymer Activity Coefficient Models (ACM): Polymer Nonrandom Two-liquid (POLYNRTL) Model 42
2.2.1 Vapor-Liquid Equilibrium for an Ideal Vapor Phase and a Nonideal Liquid Phase 42
2.2.2 General Vapor-Liquid Equilibrium Relationships Based on Fugacity Coefficient and Liquid-phase Activity Coefficient 43
2.2.3 Segment-based Mole Fraction Versus Species-based Mole Fraction 43
2.2.4 POLYNRTL: Polymer Nonrandom Two-liquid Activity Coefficient Model 44
2.2.5 Concept of Henry Components for Vapor-Liquid Equilibrium for a Vapor Phase and a Nonideal Liquid Phase Involving Supercritical Components 46
2.3 Workshop 2.1. Estimating POLYNRTL Binary Parameters Using UNIFAC 50
2.3.1 Objective 50
2.3.2 Estimating POLYNRTL Binary Parameters Using UNIFAC for Polystyrene Manufacturing 51
2.4 Prediction of Polymer Physical Properties by Van Krevelen Functional Group Method 53
2.5 Workshop 2.2. Estimating the Physical Properties of a Copolymer Using the Van Krevelen Group Contribution Method 55
2.5.1 Objective 55
2.5.2 Draw the Process Flowsheet and Specify the Unit Set and Global Options 55
2.5.3 Define Components, Segments, and Polymer and Characterize Their Structures 55
2.5.4 Choosing Property Method and Entering or Estimating Property Parameters 56
2.5.5 Specifications of Feed Stream and Flash Block 57
2.5.6 Creating Property Sets 58
2.5.7 Defining Property Analysis Run to Create Property Tables 58
2.6 Polymer Sanchez-Lacombe Equation of State (POLYSL) 61
2.7 Workshop 2.3. Estimating Property Parameters Using Data
Regression Tool 64
2.7.1 Objective 64
2.7.2 Defining a DRS Run 64
2.7.3 Specifying a Unit Set and Global Options 64
2.7.4 Defining Components, Segments, Oligomers, and Polymer 65
2.7.5 Choose Property Method and Enter Known Property Parameters from Aspen Enterprise Databanks 66
2.7.6 Enter Experimental Data for Data Regression, Run the Regression, and Examine the Results 67
2.7.7 Specifying a Regression Run and the Parameters to be Regressed 69
2.7.8 Running the Regression Case and Examining the Results 69
2.8 Polymer Perturbed-chain Statistical Fluid Theory (POLYPCSF) Equation of State 72
2.9 Workshop 2.4. Regression of Property Parameters for POLYPCSF EOS 74
2.9.1 Objective and Data Sources 74
2.9.2 Regression of Pure Component Parameters for POLYPCSF EOS 75
2.10 Correlation of Polymer Product Quality Indices and Structure-Property Correlations 77
2.10.1 Polyolefin Product Quality Indices 77
2.10.2 Empirical Correlations of Polymer Product Quality Targets 80
2.10.3 Estimation of Apparent Newtonian Viscosity from MI-MWW Measurement 81
References 83
3 Reactor Modeling, Convergence Tips, and Data-Fit Tool 87
3.1 Kinetic or Rate-Based Reactors 87
3.2 Continuous Stirred-Tank Reactor Model (RCSTR) 87
3.2.1 RCSTR Configurations 87
3.2.2 RCSTR Specifications 88
3.3 Plug-Flow Reactor Model (RPLUG) 89
3.3.1 RPLUG Configurations 89
3.3.2 RPLUG Specifications 90
3.4 Batch Reactor Model (RBATCH) 91
3.4.1 RBATCH Configuration 91
3.4.2 RBATCH Specifications 92
3.5 Representation of Nonideal Reactors 93
3.6 RCSTR Convergence 93
3.6.1 Initialization 93
3.6.2 Scaling Factors 95
3.6.3 Residence Time Loop 95
3.6.4 Energy Balance Loop 96
3.6.5 Mass Balance Loop 97
3.6.6 Flash Loop 98
3.6.7 Recommendation for RCSTR Mass Balance Algorithm for Polyolefin Process Simulation 99
3.7 RPLUG/RBATCH Model Convergence 100
3.8 Data Fit (Simulation Data Regression) 101
3.9 Workshop 3.1: Data Fit of Kinetic Parameters for Styrene Polymerization Using Concentration Profile Data 103
3.9.1 Objective 103
3.9.2 A Simplified Kinetic Model for Styrene Polymerization 103
3.9.3 Datasets 106
3.9.4 Simulation Data Regression (Data Fit) 107
3.10 Workshop 3.2: Data Fit of Kinetic Parameters for Styrene Polymerization Using Point Data 111
3.10.1 Objective 111
3.10.2 Dataset 111
3.10.3 Simulation Data Regression (Data Fit) 112
References 114
4 Free Radical Polymerizations: LDPE and EVA 115
4.1 Polymers by Free Radical Polymerization 115
4.2 Kinetics of Free Radical Polymerization 115
4.2.1 Initiator and Its Decomposition-Rate Parameters 116
4.2.2 Chain Initiation Reactions 118
4.2.3 Chain Propagation Reactions 119
4.2.4 Chain Transfer Reactions 120
4.2.5 Termination Reactions 121
4.2.6 Autoacceleration, Trommsdorff Effect, or Gel Effect 122
4.2.7 Other Free Radical Polymerization Reactions 123
4.3 Thermodynamic Methods and Property Parameter Requirements 123
4.4 Workshop 4.1: Simulation of an Autoclave High-pressure LDPE Process 124
4.4.1 Objectives 124
4.4.2 Process Flowsheet and Simulation Representation 124
4.4.3 Unit System, Components, and Characterization of Polymer 126
4.4.4 Thermodynamic Methods and Property Parameters for Components, Segment, and Polymer 129
4.4.5 PCES (Physical Constant Estimation System) for Estimating Missing-Property Parameters 130
4.4.6 Defining Free Radical Polymerization Reactions for LDPE 130
4.4.7 Specifications of Inlet Process Streams and Unit Operation and Reactor Blocks 133
4.4.8 Methodology for Improving Simulation Convergence and for Kinetic Parameter Estimation 133
4.4.9 Base-Case Simulation Results 136
4.4.10 Model Applications 138
4.4.11 Separation Section 139
4.5 Workshop 4.2: Simulation of Tubular Reactors for HP LDPE Process 140
4.5.1 Objectives 140
4.5.2 Process Flowsheet and Simulation Representation 141
4.5.3 Unit System, Components, and Characterization of Polymer 141
4.5.4 Thermodynamic Method and Property Parameters for Components 142
4.5.5 PCES (Physical Constant Estimation System) for Estimating Missing-Property Parameters 144
4.5.6 Free Radical Polymerization Reactions for LDPE 144
4.5.7 Specifications of Inlet Process Streams and Unit Operation and Reactor Blocks 144
4.5.8 User FORTRAN Subroutine for Heat Transfer Calculations for the LDPE Reactor 145
4.5.9 Base-Case Simulation Targets and Kinetic Parameter Estimation 147
4.5.10 Model Applications 149
4.6 Workshop 4.3: Simulation of Tubular Reactors for Ethylene-Vinyl Acetate (EVA) Copolymerization Process 151
4.6.1 Objective 151
4.6.2 Process Background 151
4.6.3 Unit System, Components, and Characterization of Polymer 153
4.6.4 Thermodynamic Method and Property Parameters for Components and Polymer 155
4.6.5 Free Radical Polymerization Kinetics for EVA Copolymerization 156
4.6.6 Specifications of Inlet Process Streams and Unit Operation and Reactor Blocks 156
4.6.7 Base-Case Simulation Targets and Kinetic Parameter Estimation 158
References 160
5 Ziegler-Natta Polymerization: HDPE, PP, LLDPE, and EPDM 163
5.1 Ziegler-Natta (ZN) Polymerization 164
5.1.1 Introduction 164
5.1.2 Ziegler-Natta Catalysts 164
5.2 Ziegler-Natta Polymerization Kinetics 165
5.2.1 Catalyst Activation (ACT) 165
5.2.2 Chain Initiation (CHAIN-INI) 166
5.2.3 Chain Propagation (PROP) 166
5.2.4 Chain-Transfer Reaction (CHAT) 167
5.2.5 Catalyst Deactivation (DEACT) 167
5.2.6 Catalyst Inhibition (INH) 167
5.2.7 Copolymerization Kinetics 168
5.3 Modeling Considerations 170
5.3.1 Reactor Types 170
5.3.2 Process Flowsheets 171
5.3.3 Polymer Types 172
5.3.4 Molecular Weight Distribution (MWD) and Multi-Modal Distributions 173
5.3.5 Thermodynamics 174
5.3.6 Global Kinetics Versus Local Kinetics 174
5.4 Commercial Polyolefin Production Targets 175
5.4.1 General Production Targets 175
5.4.1.1 Production Rate 175
5.4.1.2 MWN 175
5.4.1.3 MI 176
5.4.1.4 Conversion 176
5.4.1.5 PDI 176
5.4.1.6 SMWN and SPFRAC 176
5.4.1.7 SFRAC and SCB 176
5.4.1.8 Rho 176
5.4.1.9 Residence Time 177
5.4.2 Polymer-Specific Targets 177
5.4.2.1 CISFRAC 177
5.4.2.2 ATFRAC 177
5.5 Methodology for Polyolefin Kinetic Estimation 178
5.5.1 Efficient Use of Software Tool: Data Fit 179
5.5.2 Flowchart of the Methodology for Kinetic Parameter Estimation 179
5.5.2.1 Multiple Product Grades and Single Active Catalyst Site 180
5.5.2.2 Multisite Model and Deconvolution Analysis 183
5.5.2.3 GPC Data and Deconvolution Analysis to Estimate the Number of Active Catalyst Sites 185
5.5.3 Efficient Use of Software Tools: Sensitivity Analysis 188
5.5.4 Efficient Use of Software Tools: Design Specification 191
5.5.5 Model Applications 194
5.6 Workshop 5.1: Simulation of a Slurry HDPE Process 195
5.6.1 Objective 195
5.6.2 Process Flowsheet 195
5.6.3 Unit System, Components, and Characterization of Oligomer, Polymer, and Site-Based Species 195
5.6.4 The Role of Solid Polymer in Phase-Equilibrium Calculations 199
5.6.5 Thermodynamic Model and Parameters 199
5.6.6 Pure-Component Parameters 200
5.6.7 Feed Streams 203
5.6.8 Ziegler-Natta Kinetics Specifications 204
5.6.9 Specifications of Unit Operations and Chemical Reactor Blocks 207
5.6.9.1 Mixers (Figure 5.38) 207
5.6.9.2 Reactors (Figures 5.39-5.42) 207
5.6.9.3 Specification of Flash Drums (Figure 5.42) 208
5.6.10 Simulation Results 209
5.6.11 Sensitivity Analysis 209
5.6.12 Closing the Recycle Loops 210
5.7 Workshop 5.2: Simulation of Stirred-Bed Gas-Phase PP Process 214
5.7.1 Objective 214
5.7.2 Process Description 214
5.7.3 Modeling the Stirred-Bed Reactor 215
5.7.4 Process Flowsheet 218
5.7.5 Unit System, Components, and Characterization of Polymer and Site-Based Species 218
5.7.6 Thermodynamic Model and Parameters 220
5.7.7 Feed Streams 222
5.7.8 Ziegler-Natta Kinetics Specifications 223
5.7.9 Specifications of Unit Operation and Chemical Reactor Blocks 225
5.7.9.1 Mixers MIX1 to MIX8 (Figure 5.62) 225
5.7.9.2 Reactors R1 to R8 (Figures 5.63 and 5.64) 225
5.7.9.3 Other Blocks 225
5.7.9.4 Convergence Blocks 226
5.7.10 Open-Loop Simulation Results and Closing the Loop 227
5.7.11 Model Applications 229
5.8 Workshop 5.3: Simulation of a Gas-Phase Fluid-Bed LLDPE Process with Condensed Mode Cooling 229
5.8.1 Objective 229
5.8.2 Condensed Mode Cooling in Ethylene Polymerization in a Fluidized-Bed Reactor 230
5.8.3 Process Flowsheet 234
5.8.4 Unit System, Components, and Characterization of Oligomer, Polymer, and Site-Based Species 234
5.8.5 Deconvolution Analysis of GPC Data to Determine the Number of Active Catalyst Sites 235
5.8.6 Thermodynamic Model and Parameters 237
5.8.7 Inlet Stream Specifications for Grades A and B 238
5.8.8 Specifications of Unit Operation and Chemical Reactor Blocks 238
5.8.9 Ziegler-Natta Kinetics Specifications 241
5.8.10 Reactor and Flowsheet Simulation to Match Plant Production Targets 242
5.8.11 Model Applications 242
5.9 Workshop 5.4: Simulation of a Solution Polymerization Process for Producing Ethylene-Propylene Copolymer (EPM) or an
Ethylene-Propylene-Diene Terpolymer (EPDM) with Metallocene Catalysts 247
5.9.1 Objective 247
5.9.2 Process Background 247
5.9.3 EPM Copolymerization Kinetics and EPDM Terpolymerization Using a Metallocene Catalyst System 249
5.9.4 Unit System, Components, and Characterization of Polymer 250
5.9.5 Thermodynamic Method and Property Parameters for Components and Polymer 254
5.9.6 Process Flowsheet and Inlet Stream and Block Specifications 255
5.9.7 Base-Case Simulation Results 256
5.9.8 Extension to EPDM (Ethylene-Propylene-Diene Terpolymer) 257
5.10 Conclusions 260
References 261
6 Free Radical and Ionic Polymerizations: PS and SBS Rubber 267
6.1 Workshop 6.1: Simulation of Polystyrene Reactors with Gel Effect and
Oligomer Formation 268
6.1.1 Objective 268
6.1.2 Process Flowsheet 269
6.1.3 Unit System, Components, and Characterization of Polymer 270
6.1.4 Characterization of Oligomers 271
6.1.5 Thermodynamic Method and Property Parameters for Components and Oligomers 276
6.1.6 PCES (Physical Constant Estimation System) for Estimating Property Parameters for Oligomers 278
6.1.7 Defining Free Radical Reactions and Oligomer Reactions 279
6.1.8 Specification of Inlet Process Streams and Unit Operation and Reactor Blocks 284
6.1.9 Kinetic Parameter Estimation and Model Validation 286
6.1.10 Model Applications 288
6.2 Workshop 6.2: Production of Poly(Styrene-Butadiene-Styrene) or SBS Rubber by Ionic Polymerization 292
6.2.1 Motivation and Objective for Modeling Ionic Polymerization Processes 292
6.2.2 Reactor Configurations and Copolymer Products 293
6.2.2.1 Tapered Block Copolymer 293
6.2.2.2 Di-/Tri-Block Copolymer and a Star-Shaped Block Copolymer 294
6.2.2.3 Random Copolymer 294
6.2.3 Components, Segments, and Polymer in Anionic Copolymerization of Styrene and Butadiene 294
6.2.4 Thermodynamic Method and Property Parameters of Components and Polymer 294
6.2.5 Kinetics of Anionic Copolymerization of Styrene and Butadiene 295
6.2.5.1 Initiator Disassociation (INIT-DISSOC) 295
6.2.5.2 Chain Initiation (CHAIN-INI) 296
6.2.5.3 Chain Propagation (PROPAGATION) 300
6.2.5.4 Association or Aggregation (ASSOCIATION) 300
6.2.5.5 Chain Transfer (CHAT) 301
6.2.5.6 Chain Termination (TERM-AGENT) 302
6.2.5.7 Equilibrium with Counter-Ion or Reversible Ionization (EQUILIB-CION) 302
6.2.5.8 Batch Reactor for Producing a Tapered Block Copolymer 302
6.2.5.9 Semi-Batch Reactor for Producing a Tri-Block SBS Copolymer by an Industrial Batch-Sequence Recipe 306
6.2.5.10 Semi-Batch Reactor for Producing a Tri-Block SBS Copolymer by a Literature Batch-Sequence Recipe 313
References 318
7 Improved Polymer Process Operability and Control Through Steady-State and Dynamic Simulation Models 321
7.1 Workshop 7.1: Workflow for Dynamic Process Modeling Using Aspen Plus and Aspen Plus Dynamics 322
7.2 Running Simulation in Aspen Plus Dynamics 325
7.2.1 Types of Dynamic Simulations: Flow-Driven and Pressure-Driven 325
7.2.2 Graphical Interface of Aspen Plus Dynamics 325
7.2.3 Simulation Run Modes and Run Control 326
7.2.4 Viewing Simulation Results Using Predefined Tables and Plots 328
7.2.5 Specification Status and Analysis 329
7.2.6 Creating New Tables and Plots 332
7.2.7 Variable Finding in Aspen Plus Dynamics (AD) 334
7.3 Process Control in Aspen Plus Dynamics 335
7.3.1 Workshop 7.2: Adding a PID Controller 335
7.3.2 Configuring a PID Controller 337
7.4 Snapshots 344
7.5 Workshop 7.3: Tasks for Implementing Discrete Events 344
7.6 Workshop 7.4: Dynamic Simulation and Grade Change of a Slurry HPDE Process 350
7.6.1 Objectives 350
7.6.2 Stepwise Procedure to Develop Aspen Plus Dynamics (AD) Simulation Model 350
7.6.3 Simulation of Grade-Change Operations 355
7.7 Workshop 7.5: Dynamic Simulation and Control of a Commercial Slurry HDPE Process 359
7.7.1 Objectives 359
7.7.2 Converting a Steady-State Simulation Model to a Dynamic Simulation Model 359
7.7.3 Initial Adjustments of the AD Model 361
7.7.3.1 Polymer Attributes for Streams and Blocks 361
7.7.3.2 Implementation of Reactor Level Control Using Mechanical Weir 361
7.7.3.3 Improvement of the Reactor Temperature Controller 362
7.7.3.4 Deletion of Pressure Controllers 363
7.7.3.5 Adding a Hydrogen-Ethylene Ratio Controller to the Recycle Gas 363
7.8 Workshop 7.6: Dynamic Simulation and Control of a Gas-Phase Fluidized-Bed Process for Producing LLDPE in Condensed Mode Operation 367
7.8.1 Objectives 367
7.8.2 Converting a Steady-State Simulation Model to a Dynamic Simulation Model 367
7.9 Workshop 7.7: Dynamic Simulation and Control of a Slurry HDPE Process Using an Inferential Controller 370
7.9.1 Objective 370
7.9.2 Inferential Control Theory and Recent Applications 370
7.9.3 HDPE Process Description and Steady-State Model Empirical Correlation 372
7.9.4 Grade-Change Transition Using Basic H2-Based Controller 373
7.9.5 Open-Loop Inferential Controller Using Dynamic Model 374
7.9.6 Closed-Loop Inferential Controller 375
References 378
Volume 2
Foreword xv
Preface xxxi
Acknowledgment xxxv
Copyright Notice xxxvii
About the Authors xxxix
About the Companion Website xli
8 Model-Predictive Control of Polyolefin Processes 381
9 Application of Multivariate Statistics to Optimizing Polyolefin Manufacturing 477
10 Applications of Machine Learning to Optimizing Polyolefin Manufacturing 533
11 A Hybrid Science-Guided Machine Learning Approach for Modeling Chemical and Polymer Processes 651
Appendix A Matrix Algebra in Multivariate Data Analysis and Model Predictive Control 699
Appendix B Introduction to Python for Chemical Engineers 737
Aman Aggarwal
Index 759
