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 xxiAbout the Author xxiiiAcknowledgments xxviiiDisclaimer xxx1 Optimizing a Process Plant 11.1 High-Level Business Goals 11.2 Profit 11.3 Each Plant Is Unique 31.4 Plant Optimization Nirvana 31.5 Process/Asset Views of the Business Need Alignment 51.6 Optimization Technologies on the Process Side of the Business 61.7 Optimization Technologies on the Asset Side of the Business 71.8 Conclusion 101.9 Future Chapters 112 Gen 1 – Transitioning from Reliability to Asset Management 142.1 Reliability’s Early Days 142.2 Rebranding Reliability to be Asset Management 152.3 Changing the Reliability Management Structure 162.4 Where Did Gen 1 Fall Short? 162.5 Adoption of Monte Carlo Simulation Technology Has Struggled 162.6 Asset Optimization Nirvana – The Future 172.7 Conclusion 193 Gen 2 – Plant Optimization Using Asset Modeling Methodologies 203.1 Gen 2 Philosophy 203.2 Gen 2 Asset Optimization Applications 233.3 Conclusion 314 Selecting the Best Improvement Projects – Optimal Process Unit Availability 324.1 Industry Challenge 344.2 Improvement Projects 354.3 Asset Optimization Technologies 384.4 Optimizer Definition 424.5 Optimization Example 454.6 More General Optimization 584.7 Does Reducing Availability Make Sense for Any of Our Process Units? 584.8 Conclusion 595 Monte Carlo Simulation Overview 615.1 Reliability Block Diagram (RBD) 625.2 Rolling the Dice 635.3 Histories within a Model Run 645.4 Results 655.4.1 Probability Distributions 655.5 Submodel – Detailed Process Unit Model 675.6 What Level of Detail Is Required? 685.7 Definitions 685.8 RAM Software Tools 695.9 Challenge to Monte Carlo Simulation Vendors 695.10 Conclusions 706 Optimizer Overview 716.1 Independent Variables 716.2 Dependent Variables 726.3 Constraints 726.4 Objective Function 726.5 Optimizer Problem Definitions 736.6 Conclusions 827 The Consultation Process – The Main Work Process 837.1 Nobody Has Excellent Data in the Process Industries 837.2 Why Operating Conditions Are so Important in the Process Industries 847.3 Tapping into Your Company’s Innate Knowledge 847.4 Golden Opportunity To Test the Approach 857.5 Consulting Meeting Details 877.6 Monte Carlo Modeler Software Inputs 917.7 Data from Asset Management Systems 927.8 Data Storage/Structure 937.9 Conclusion 948 Turnaround Considerations 958.1 Example Problem Overview 978.2 Results Expectations 1028.3 Solution Approach 1068.4 First Problem – Fixed Start Date and Duration 1088.5 Second Problem – Fixed Start Date, but Flexible Duration 1138.6 Last Problem – Flexible Start Date 1168.7 Conclusion 1189 What About Process Conditions? 1199.1 Examples Where Feed Quality and Process Conditions Play a Major Role 1199.2 Operating Condition Effect on Failure Data 1209.3 Example Incorporating Process Conditions into Our Problem Definition 1219.4 Conclusion 12510 Opportunistic Maintenance Optimization 12610.1 Modeling Maintenance Plan Options 12710.2 Example Problem Data 12810.3 Single Equipment Opportunistic Maintenance Optimization 13010.4 Intra Unit Opportunistic Maintenance Optimization 13210.5 Inter Unit Opportunistic Maintenance Optimization 13510.6 Conclusion 13811 Spare Parts Optimization 13911.1 Spares Parts Dependence Often Masks Other Equipment Issues 13911.2 Typical Methods for Estimating Spare Parts 14011.3 Logistical Challenges 14011.4 Lead Times/Price/Vendor Issues 14111.5 Prioritization 14211.6 Example Problem Data 14411.7 Effect of Failure Standard Deviation 14811.8 Optimization Problems Overview 14911.9 Single Equipment Spares Optimization 15111.10 Intra-Unit Spares Optimization 15211.11 Inter-Unit Spares Optimization 15411.12 Common Spare Across Multiple Units 15711.13 Full-time Spare Parts Engineer Position 16111.14 Conclusion 16112 Task/Resource Optimization 16212.1 Example Problem Data 16312.2 General Approach 16412.3 Single Equipment Task Optimization 16612.4 Intra-Unit Equipment Task Optimization 16912.5 Inter-Unit Equipment Task Optimization 17212.6 Conclusion 17813 Tankage Determination/Optimization 17913.1 Why Tankage Size Matters 17913.2 Example Problem Overview 18013.3 Same Availability for both Upstream and Downstream Process Units 18113.4 Downstream Availability Variable with Constant Upstream Availability 18213.5 Conclusion 18414 Improving Availability 18514.1 Options to Improve Availability 18614.2 How Reliability and Process Configuration Effects Availability Results 18914.3 Which Option Is the Best? 19014.4 Conclusion 19115 Equipment Reliability Optimization 19215.1 General Approach 19315.2 Example Problem Data 19515.3 First Impressions of Example Data – Impact on Problem Solution 19615.4 Effect of Failure Standard Deviation 19715.5 Single Equipment Design Optimization 19815.6 Intra-Unit Design Optimization 20115.7 Inter-Unit Design Optimization 21015.8 Scenario Final Thoughts 21215.9 Conclusion 21216 Plant Optimization Within the Design Process 21416.1 Combining Process Simulation with Monte Carlo Simulation 21416.2 Balancing the Short/Long Term within the Design Process 21516.3 Improvement Project 21516.4 Debottlenecking Project 21816.5 Changes to Plant-Level Model for Grassroots Process Design 22116.6 Grassroots Process Unit Design 22116.7 Design Considerations 22316.8 Conclusion 22517 Combined Optimization 22817.1 Combination of Improvement Projects and Crude Feed Mix Optimization 22917.2 Combining Turnaround and Future Feed Composition 23717.3 Conclusion 25018 Mapping Models to Optimization Problems 25118.1 Mapping Between Optimization Problem and Model(s) Required 25118.2 Selection of Optimal Improvement Projects 25218.3 Storage Optimization 25318.4 Turnaround Timing/Duration and Equipment Restoration Selection 25318.5 Maintenance Plan Options Optimization 25318.6 Spares Optimization 25318.7 Task Optimization 25418.8 Asset Design Optimization 25418.9 How to Kickstart Your Program 25418.10 Standard Models or Not? 25518.11 Process Unit Models 25518.12 Site or Plant Models 25718.13 Equipment Models 25718.14 Responsibility for Equipment Models 25818.15 Conclusion 25819 Creating a Program Master Plan 25919.1 Opportunity Assessment 26019.2 Project Selection 26219.3 Project Phases 26419.4 Resources 26619.5 Consultation Process 26919.6 Data – and Its Implications 26919.7 Technologies 27019.8 Work Processes 27219.9 Training 27319.10 Conclusion 27320 Conclusion 27420.1 The Need for a Complex Asset Base 27420.2 High-Level Business Goals 27520.3 Asset Decisions that Can Drive Optimal Profit 27520.4 A Side Benefit → Combining the Process and Equipment Views of the Business 27820.5 How to Move Forward with Your Program 28020.6 Limitations of Asset Modeling 28120.7 Comparing Process and Asset Optimization 28120.8 The Future of Optimization 282Appendix A Nuts and Bolts of Monte Carlo Simulation 283Appendix B Refinery Example Process Description 294Notes 308Index 311

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 xxiAbout the Author xxiiiAcknowledgments xxviiiDisclaimer xxx1 Optimizing a Process Plant 11.1 High-Level Business Goals 11.2 Profit 11.3 Each Plant Is Unique 31.4 Plant Optimization Nirvana 31.5 Process/Asset Views of the Business Need Alignment 51.6 Optimization Technologies on the Process Side of the Business 61.7 Optimization Technologies on the Asset Side of the Business 71.8 Conclusion 101.9 Future Chapters 112 Gen 1 – Transitioning from Reliability to Asset Management 142.1 Reliability’s Early Days 142.2 Rebranding Reliability to be Asset Management 152.3 Changing the Reliability Management Structure 162.4 Where Did Gen 1 Fall Short? 162.5 Adoption of Monte Carlo Simulation Technology Has Struggled 162.6 Asset Optimization Nirvana – The Future 172.7 Conclusion 193 Gen 2 – Plant Optimization Using Asset Modeling Methodologies 203.1 Gen 2 Philosophy 203.2 Gen 2 Asset Optimization Applications 233.3 Conclusion 314 Selecting the Best Improvement Projects – Optimal Process Unit Availability 324.1 Industry Challenge 344.2 Improvement Projects 354.3 Asset Optimization Technologies 384.4 Optimizer Definition 424.5 Optimization Example 454.6 More General Optimization 584.7 Does Reducing Availability Make Sense for Any of Our Process Units? 584.8 Conclusion 595 Monte Carlo Simulation Overview 615.1 Reliability Block Diagram (RBD) 625.2 Rolling the Dice 635.3 Histories within a Model Run 645.4 Results 655.4.1 Probability Distributions 655.5 Submodel – Detailed Process Unit Model 675.6 What Level of Detail Is Required? 685.7 Definitions 685.8 RAM Software Tools 695.9 Challenge to Monte Carlo Simulation Vendors 695.10 Conclusions 706 Optimizer Overview 716.1 Independent Variables 716.2 Dependent Variables 726.3 Constraints 726.4 Objective Function 726.5 Optimizer Problem Definitions 736.6 Conclusions 827 The Consultation Process – The Main Work Process 837.1 Nobody Has Excellent Data in the Process Industries 837.2 Why Operating Conditions Are so Important in the Process Industries 847.3 Tapping into Your Company’s Innate Knowledge 847.4 Golden Opportunity To Test the Approach 857.5 Consulting Meeting Details 877.6 Monte Carlo Modeler Software Inputs 917.7 Data from Asset Management Systems 927.8 Data Storage/Structure 937.9 Conclusion 948 Turnaround Considerations 958.1 Example Problem Overview 978.2 Results Expectations 1028.3 Solution Approach 1068.4 First Problem – Fixed Start Date and Duration 1088.5 Second Problem – Fixed Start Date, but Flexible Duration 1138.6 Last Problem – Flexible Start Date 1168.7 Conclusion 1189 What About Process Conditions? 1199.1 Examples Where Feed Quality and Process Conditions Play a Major Role 1199.2 Operating Condition Effect on Failure Data 1209.3 Example Incorporating Process Conditions into Our Problem Definition 1219.4 Conclusion 12510 Opportunistic Maintenance Optimization 12610.1 Modeling Maintenance Plan Options 12710.2 Example Problem Data 12810.3 Single Equipment Opportunistic Maintenance Optimization 13010.4 Intra Unit Opportunistic Maintenance Optimization 13210.5 Inter Unit Opportunistic Maintenance Optimization 13510.6 Conclusion 13811 Spare Parts Optimization 13911.1 Spares Parts Dependence Often Masks Other Equipment Issues 13911.2 Typical Methods for Estimating Spare Parts 14011.3 Logistical Challenges 14011.4 Lead Times/Price/Vendor Issues 14111.5 Prioritization 14211.6 Example Problem Data 14411.7 Effect of Failure Standard Deviation 14811.8 Optimization Problems Overview 14911.9 Single Equipment Spares Optimization 15111.10 Intra-Unit Spares Optimization 15211.11 Inter-Unit Spares Optimization 15411.12 Common Spare Across Multiple Units 15711.13 Full-time Spare Parts Engineer Position 16111.14 Conclusion 16112 Task/Resource Optimization 16212.1 Example Problem Data 16312.2 General Approach 16412.3 Single Equipment Task Optimization 16612.4 Intra-Unit Equipment Task Optimization 16912.5 Inter-Unit Equipment Task Optimization 17212.6 Conclusion 17813 Tankage Determination/Optimization 17913.1 Why Tankage Size Matters 17913.2 Example Problem Overview 18013.3 Same Availability for both Upstream and Downstream Process Units 18113.4 Downstream Availability Variable with Constant Upstream Availability 18213.5 Conclusion 18414 Improving Availability 18514.1 Options to Improve Availability 18614.2 How Reliability and Process Configuration Effects Availability Results 18914.3 Which Option Is the Best? 19014.4 Conclusion 19115 Equipment Reliability Optimization 19215.1 General Approach 19315.2 Example Problem Data 19515.3 First Impressions of Example Data – Impact on Problem Solution 19615.4 Effect of Failure Standard Deviation 19715.5 Single Equipment Design Optimization 19815.6 Intra-Unit Design Optimization 20115.7 Inter-Unit Design Optimization 21015.8 Scenario Final Thoughts 21215.9 Conclusion 21216 Plant Optimization Within the Design Process 21416.1 Combining Process Simulation with Monte Carlo Simulation 21416.2 Balancing the Short/Long Term within the Design Process 21516.3 Improvement Project 21516.4 Debottlenecking Project 21816.5 Changes to Plant-Level Model for Grassroots Process Design 22116.6 Grassroots Process Unit Design 22116.7 Design Considerations 22316.8 Conclusion 22517 Combined Optimization 22817.1 Combination of Improvement Projects and Crude Feed Mix Optimization 22917.2 Combining Turnaround and Future Feed Composition 23717.3 Conclusion 25018 Mapping Models to Optimization Problems 25118.1 Mapping Between Optimization Problem and Model(s) Required 25118.2 Selection of Optimal Improvement Projects 25218.3 Storage Optimization 25318.4 Turnaround Timing/Duration and Equipment Restoration Selection 25318.5 Maintenance Plan Options Optimization 25318.6 Spares Optimization 25318.7 Task Optimization 25418.8 Asset Design Optimization 25418.9 How to Kickstart Your Program 25418.10 Standard Models or Not? 25518.11 Process Unit Models 25518.12 Site or Plant Models 25718.13 Equipment Models 25718.14 Responsibility for Equipment Models 25818.15 Conclusion 25819 Creating a Program Master Plan 25919.1 Opportunity Assessment 26019.2 Project Selection 26219.3 Project Phases 26419.4 Resources 26619.5 Consultation Process 26919.6 Data – and Its Implications 26919.7 Technologies 27019.8 Work Processes 27219.9 Training 27319.10 Conclusion 27320 Conclusion 27420.1 The Need for a Complex Asset Base 27420.2 High-Level Business Goals 27520.3 Asset Decisions that Can Drive Optimal Profit 27520.4 A Side Benefit → Combining the Process and Equipment Views of the Business 27820.5 How to Move Forward with Your Program 28020.6 Limitations of Asset Modeling 28120.7 Comparing Process and Asset Optimization 28120.8 The Future of Optimization 282Appendix A Nuts and Bolts of Monte Carlo Simulation 283Appendix B Refinery Example Process Description 294Notes 308Index 311