Overview

Optimize asset decisions and improve the financial and technical operation of process plants The process industries, particularly the refining and petrochemical industries, are comprised of capital-intensive business whose assets are valued in the trillions. Optimizing the function of refining and petrochemical plants is therefore not simply a process decision, but a business one, with even small improvements in efficiency potentially providing enormous margins. There is an urgent need for businesses to assess how the asset side of process industry production can be optimized. Plant Optimization in the Process Industries offers a pioneering asset-focused approach to plant optimization. Optimization of operating values within a processing unit is a developed area of technology with a wide and varied literature; little attention has been paid to the asset side, making this a groundbreaking and invaluable work. Outlining a multi-tiered approach to financial optimization which adjusts the variables of a statistical asset model, this volume has the potential to revolutionize businesses and generate record profit margins. Readers will also find: Comparison and contrast of different technologies on the process and asset side of the industry Detailed discussion of constrained, non-linear optimization technology, along with basic functioning of Monte Carlo modelling A real-world case study followed through the book to facilitate understanding This book is ideal for professionals who manage, design, operate, and maintain process industry facilities, particularly those in the hydrocarbon and chemical industries, as well as any asset-intensive industry.

Full Product Details

Author:   Marty Moran
Publisher:   John Wiley & Sons Inc
Imprint:   John Wiley & Sons Inc
ISBN:  

9781119707738

ISBN 10:   1119707730
Pages:   352
Publication Date:   08 October 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Awaiting stock  
The supplier is currently out of stock of this item. It will be ordered for you and placed on backorder. Once it does come back in stock, we will ship it out for you.

Table of Contents

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

Reviews

Author Information

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.

Tab Content 6

Author Website:  

Customer Reviews

Recent Reviews

No review item found!

Add your own review!

Countries Available

All regions