Plant Optimization in the Process Industries

Marty Moran is a chemical engineer who has concentrated his career on using advanced computer technology in the areas of Advanced Process Control, Asset Management/Reliability, and Plant Optimization to improve the financial and technical operation of process plants Mr. Moran holds a US patent for multivariable control. He has more than 35 years of experience and has worked for companies such as Setpoint, Continental Controls, AspenTech, Meridium, and Sadara, as well as his own personal consulting business.

Foreword by Ron Lambert xxi

About the Author xxiii

Acknowledgments xxviii

Disclaimer xxx

1 Optimizing a Process Plant 1

1.1 High-Level Business Goals 1

1.2 Profit 1

1.3 Each Plant Is Unique 3

1.4 Plant Optimization Nirvana 3

1.5 Process/Asset Views of the Business Need Alignment 5

1.6 Optimization Technologies on the Process Side of the Business 6

1.7 Optimization Technologies on the Asset Side of the Business 7

1.8 Conclusion 10

1.9 Future Chapters 11

2 Gen 1 – Transitioning from Reliability to Asset Management 14

2.1 Reliability’s Early Days 14

2.2 Rebranding Reliability to be Asset Management 15

2.3 Changing the Reliability Management Structure 16

2.4 Where Did Gen 1 Fall Short? 16

2.5 Adoption of Monte Carlo Simulation Technology Has Struggled 16

2.6 Asset Optimization Nirvana – The Future 17

2.7 Conclusion 19

3 Gen 2 – Plant Optimization Using Asset Modeling Methodologies 20

3.1 Gen 2 Philosophy 20

3.2 Gen 2 Asset Optimization Applications 23

3.3 Conclusion 31

4 Selecting the Best Improvement Projects – Optimal Process Unit Availability 32

4.1 Industry Challenge 34

4.2 Improvement Projects 35

4.3 Asset Optimization Technologies 38

4.4 Optimizer Definition 42

4.5 Optimization Example 45

4.6 More General Optimization 58

4.7 Does Reducing Availability Make Sense for Any of Our Process Units? 58

4.8 Conclusion 59

5 Monte Carlo Simulation Overview 61

5.1 Reliability Block Diagram (RBD) 62

5.2 Rolling the Dice 63

5.3 Histories within a Model Run 64

5.4 Results 65

5.4.1 Probability Distributions 65

5.5 Submodel – Detailed Process Unit Model 67

5.6 What Level of Detail Is Required? 68

5.7 Definitions 68

5.8 RAM Software Tools 69

5.9 Challenge to Monte Carlo Simulation Vendors 69

5.10 Conclusions 70

6 Optimizer Overview 71

6.1 Independent Variables 71

6.2 Dependent Variables 72

6.3 Constraints 72

6.4 Objective Function 72

6.5 Optimizer Problem Definitions 73

6.6 Conclusions 82

7 The Consultation Process – The Main Work Process 83

7.1 Nobody Has Excellent Data in the Process Industries 83

7.2 Why Operating Conditions Are so Important in the Process Industries 84

7.3 Tapping into Your Company’s Innate Knowledge 84

7.4 Golden Opportunity To Test the Approach 85

7.5 Consulting Meeting Details 87

7.6 Monte Carlo Modeler Software Inputs 91

7.7 Data from Asset Management Systems 92

7.8 Data Storage/Structure 93

7.9 Conclusion 94

8 Turnaround Considerations 95

8.1 Example Problem Overview 97

8.2 Results Expectations 102

8.3 Solution Approach 106

8.4 First Problem – Fixed Start Date and Duration 108

8.5 Second Problem – Fixed Start Date, but Flexible Duration 113

8.6 Last Problem – Flexible Start Date 116

8.7 Conclusion 118

9 What About Process Conditions? 119

9.1 Examples Where Feed Quality and Process Conditions Play a Major Role 119

9.2 Operating Condition Effect on Failure Data 120

9.3 Example Incorporating Process Conditions into Our Problem Definition 121

9.4 Conclusion 125

10 Opportunistic Maintenance Optimization 126

10.1 Modeling Maintenance Plan Options 127

10.2 Example Problem Data 128

10.3 Single Equipment Opportunistic Maintenance Optimization 130

10.4 Intra Unit Opportunistic Maintenance Optimization 132

10.5 Inter Unit Opportunistic Maintenance Optimization 135

10.6 Conclusion 138

11 Spare Parts Optimization 139

11.1 Spares Parts Dependence Often Masks Other Equipment Issues 139

11.2 Typical Methods for Estimating Spare Parts 140

11.3 Logistical Challenges 140

11.4 Lead Times/Price/Vendor Issues 141

11.5 Prioritization 142

11.6 Example Problem Data 144

11.7 Effect of Failure Standard Deviation 148

11.8 Optimization Problems Overview 149

11.9 Single Equipment Spares Optimization 151

11.10 Intra-Unit Spares Optimization 152

11.11 Inter-Unit Spares Optimization 154

11.12 Common Spare Across Multiple Units 157

11.13 Full-time Spare Parts Engineer Position 161

11.14 Conclusion 161

12 Task/Resource Optimization 162

12.1 Example Problem Data 163

12.2 General Approach 164

12.3 Single Equipment Task Optimization 166

12.4 Intra-Unit Equipment Task Optimization 169

12.5 Inter-Unit Equipment Task Optimization 172

12.6 Conclusion 178

13 Tankage Determination/Optimization 179

13.1 Why Tankage Size Matters 179

13.2 Example Problem Overview 180

13.3 Same Availability for both Upstream and Downstream Process Units 181

13.4 Downstream Availability Variable with Constant Upstream Availability 182

13.5 Conclusion 184

14 Improving Availability 185

14.1 Options to Improve Availability 186

14.2 How Reliability and Process Configuration Effects Availability Results 189

14.3 Which Option Is the Best? 190

14.4 Conclusion 191

15 Equipment Reliability Optimization 192

15.1 General Approach 193

15.2 Example Problem Data 195

15.3 First Impressions of Example Data – Impact on Problem Solution 196

15.4 Effect of Failure Standard Deviation 197

15.5 Single Equipment Design Optimization 198

15.6 Intra-Unit Design Optimization 201

15.7 Inter-Unit Design Optimization 210

15.8 Scenario Final Thoughts 212

15.9 Conclusion 212

16 Plant Optimization Within the Design Process 214

16.1 Combining Process Simulation with Monte Carlo Simulation 214

16.2 Balancing the Short/Long Term within the Design Process 215

16.3 Improvement Project 215

16.4 Debottlenecking Project 218

16.5 Changes to Plant-Level Model for Grassroots Process Design 221

16.6 Grassroots Process Unit Design 221

16.7 Design Considerations 223

16.8 Conclusion 225

17 Combined Optimization 228

17.1 Combination of Improvement Projects and Crude Feed Mix Optimization 229

17.2 Combining Turnaround and Future Feed Composition 237

17.3 Conclusion 250

18 Mapping Models to Optimization Problems 251

18.1 Mapping Between Optimization Problem and Model(s) Required 251

18.2 Selection of Optimal Improvement Projects 252

18.3 Storage Optimization 253

18.4 Turnaround Timing/Duration and Equipment Restoration Selection 253

18.5 Maintenance Plan Options Optimization 253

18.6 Spares Optimization 253

18.7 Task Optimization 254

18.8 Asset Design Optimization 254

18.9 How to Kickstart Your Program 254

18.10 Standard Models or Not? 255

18.11 Process Unit Models 255

18.12 Site or Plant Models 257

18.13 Equipment Models 257

18.14 Responsibility for Equipment Models 258

18.15 Conclusion 258

19 Creating a Program Master Plan 259

19.1 Opportunity Assessment 260

19.2 Project Selection 262

19.3 Project Phases 264

19.4 Resources 266

19.5 Consultation Process 269

19.6 Data – and Its Implications 269

19.7 Technologies 270

19.8 Work Processes 272

19.9 Training 273

19.10 Conclusion 273

20 Conclusion 274

20.1 The Need for a Complex Asset Base 274

20.2 High-Level Business Goals 275

20.3 Asset Decisions that Can Drive Optimal Profit 275

20.4 A Side Benefit → Combining the Process and Equipment Views of the Business 278

20.5 How to Move Forward with Your Program 280

20.6 Limitations of Asset Modeling 281

20.7 Comparing Process and Asset Optimization 281

20.8 The Future of Optimization 282

Appendix A Nuts and Bolts of Monte Carlo Simulation 283

Appendix B Refinery Example Process Description 294

Notes 308

Index 311