{"product_id":"plant-optimization-in-the-process-industries-isbn-9781119707738","title":"Plant Optimization in the Process Industries","description":"\u003cp\u003e\u003cb\u003eOptimize asset decisions and improve the financial and technical operation of process plants\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003ePlant Optimization in the Process Industries\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eReaders will also find: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eComparison and contrast of different technologies on the process and asset side of the industry\u003c\/li\u003e\n\u003cli\u003eDetailed discussion of constrained, non-linear optimization technology, along with basic functioning of Monte Carlo modelling \u003c\/li\u003e\n\u003cli\u003eA real-world case study followed through the book to facilitate understanding\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eThis 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. \u003c\/p\u003e\u003cp\u003eForeword by \u003ci\u003eRon Lambert\u003c\/i\u003e xxi\u003c\/p\u003e \u003cp\u003eAbout the Author xxiii\u003c\/p\u003e \u003cp\u003eAcknowledgments xxviii\u003c\/p\u003e \u003cp\u003eDisclaimer xxx\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Optimizing a Process Plant 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 High-Level Business Goals 1\u003c\/p\u003e \u003cp\u003e1.2 Profit 1\u003c\/p\u003e \u003cp\u003e1.3 Each Plant Is Unique 3\u003c\/p\u003e \u003cp\u003e1.4 Plant Optimization Nirvana 3\u003c\/p\u003e \u003cp\u003e1.5 Process\/Asset Views of the Business Need Alignment 5\u003c\/p\u003e \u003cp\u003e1.6 Optimization Technologies on the Process Side of the Business 6\u003c\/p\u003e \u003cp\u003e1.7 Optimization Technologies on the Asset Side of the Business 7\u003c\/p\u003e \u003cp\u003e1.8 Conclusion 10\u003c\/p\u003e \u003cp\u003e1.9 Future Chapters 11\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Gen 1 – Transitioning from Reliability to Asset Management 14\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Reliability’s Early Days 14\u003c\/p\u003e \u003cp\u003e2.2 Rebranding Reliability to be Asset Management 15\u003c\/p\u003e \u003cp\u003e2.3 Changing the Reliability Management Structure 16\u003c\/p\u003e \u003cp\u003e2.4 Where Did Gen 1 Fall Short? 16\u003c\/p\u003e \u003cp\u003e2.5 Adoption of Monte Carlo Simulation Technology Has Struggled 16\u003c\/p\u003e \u003cp\u003e2.6 Asset Optimization Nirvana – The Future 17\u003c\/p\u003e \u003cp\u003e2.7 Conclusion 19\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Gen 2 – Plant Optimization Using Asset Modeling Methodologies 20\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Gen 2 Philosophy 20\u003c\/p\u003e \u003cp\u003e3.2 Gen 2 Asset Optimization Applications 23\u003c\/p\u003e \u003cp\u003e3.3 Conclusion 31\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Selecting the Best Improvement Projects – Optimal Process Unit Availability 32\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Industry Challenge 34\u003c\/p\u003e \u003cp\u003e4.2 Improvement Projects 35\u003c\/p\u003e \u003cp\u003e4.3 Asset Optimization Technologies 38\u003c\/p\u003e \u003cp\u003e4.4 Optimizer Definition 42\u003c\/p\u003e \u003cp\u003e4.5 Optimization Example 45\u003c\/p\u003e \u003cp\u003e4.6 More General Optimization 58\u003c\/p\u003e \u003cp\u003e4.7 Does Reducing Availability Make Sense for Any of Our Process Units? 58\u003c\/p\u003e \u003cp\u003e4.8 Conclusion 59\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Monte Carlo Simulation Overview 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Reliability Block Diagram (RBD) 62\u003c\/p\u003e \u003cp\u003e5.2 Rolling the Dice 63\u003c\/p\u003e \u003cp\u003e5.3 Histories within a Model Run 64\u003c\/p\u003e \u003cp\u003e5.4 Results 65\u003c\/p\u003e \u003cp\u003e5.4.1 Probability Distributions 65\u003c\/p\u003e \u003cp\u003e5.5 Submodel – Detailed Process Unit Model 67\u003c\/p\u003e \u003cp\u003e5.6 What Level of Detail Is Required? 68\u003c\/p\u003e \u003cp\u003e5.7 Definitions 68\u003c\/p\u003e \u003cp\u003e5.8 RAM Software Tools 69\u003c\/p\u003e \u003cp\u003e5.9 Challenge to Monte Carlo Simulation Vendors 69\u003c\/p\u003e \u003cp\u003e5.10 Conclusions 70\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Optimizer Overview 71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Independent Variables 71\u003c\/p\u003e \u003cp\u003e6.2 Dependent Variables 72\u003c\/p\u003e \u003cp\u003e6.3 Constraints 72\u003c\/p\u003e \u003cp\u003e6.4 Objective Function 72\u003c\/p\u003e \u003cp\u003e6.5 Optimizer Problem Definitions 73\u003c\/p\u003e \u003cp\u003e6.6 Conclusions 82\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 The Consultation Process – The Main Work Process 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Nobody Has Excellent Data in the Process Industries 83\u003c\/p\u003e \u003cp\u003e7.2 Why Operating Conditions Are so Important in the Process Industries 84\u003c\/p\u003e \u003cp\u003e7.3 Tapping into Your Company’s Innate Knowledge 84\u003c\/p\u003e \u003cp\u003e7.4 Golden Opportunity To Test the Approach 85\u003c\/p\u003e \u003cp\u003e7.5 Consulting Meeting Details 87\u003c\/p\u003e \u003cp\u003e7.6 Monte Carlo Modeler Software Inputs 91\u003c\/p\u003e \u003cp\u003e7.7 Data from Asset Management Systems 92\u003c\/p\u003e \u003cp\u003e7.8 Data Storage\/Structure 93\u003c\/p\u003e \u003cp\u003e7.9 Conclusion 94\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Turnaround Considerations 95\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Example Problem Overview 97\u003c\/p\u003e \u003cp\u003e8.2 Results Expectations 102\u003c\/p\u003e \u003cp\u003e8.3 Solution Approach 106\u003c\/p\u003e \u003cp\u003e8.4 First Problem – Fixed Start Date and Duration 108\u003c\/p\u003e \u003cp\u003e8.5 Second Problem – Fixed Start Date, but Flexible Duration 113\u003c\/p\u003e \u003cp\u003e8.6 Last Problem – Flexible Start Date 116\u003c\/p\u003e \u003cp\u003e8.7 Conclusion 118\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 What About Process Conditions? 119\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Examples Where Feed Quality and Process Conditions Play a Major Role 119\u003c\/p\u003e \u003cp\u003e9.2 Operating Condition Effect on Failure Data 120\u003c\/p\u003e \u003cp\u003e9.3 Example Incorporating Process Conditions into Our Problem Definition 121\u003c\/p\u003e \u003cp\u003e9.4 Conclusion 125\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Opportunistic Maintenance Optimization 126\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Modeling Maintenance Plan Options 127\u003c\/p\u003e \u003cp\u003e10.2 Example Problem Data 128\u003c\/p\u003e \u003cp\u003e10.3 Single Equipment Opportunistic Maintenance Optimization 130\u003c\/p\u003e \u003cp\u003e10.4 Intra Unit Opportunistic Maintenance Optimization 132\u003c\/p\u003e \u003cp\u003e10.5 Inter Unit Opportunistic Maintenance Optimization 135\u003c\/p\u003e \u003cp\u003e10.6 Conclusion 138\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Spare Parts Optimization 139\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Spares Parts Dependence Often Masks Other Equipment Issues 139\u003c\/p\u003e \u003cp\u003e11.2 Typical Methods for Estimating Spare Parts 140\u003c\/p\u003e \u003cp\u003e11.3 Logistical Challenges 140\u003c\/p\u003e \u003cp\u003e11.4 Lead Times\/Price\/Vendor Issues 141\u003c\/p\u003e \u003cp\u003e11.5 Prioritization 142\u003c\/p\u003e \u003cp\u003e11.6 Example Problem Data 144\u003c\/p\u003e \u003cp\u003e11.7 Effect of Failure Standard Deviation 148\u003c\/p\u003e \u003cp\u003e11.8 Optimization Problems Overview 149\u003c\/p\u003e \u003cp\u003e11.9 Single Equipment Spares Optimization 151\u003c\/p\u003e \u003cp\u003e11.10 Intra-Unit Spares Optimization 152\u003c\/p\u003e \u003cp\u003e11.11 Inter-Unit Spares Optimization 154\u003c\/p\u003e \u003cp\u003e11.12 Common Spare Across Multiple Units 157\u003c\/p\u003e \u003cp\u003e11.13 Full-time Spare Parts Engineer Position 161\u003c\/p\u003e \u003cp\u003e11.14 Conclusion 161\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Task\/Resource Optimization 162\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Example Problem Data 163\u003c\/p\u003e \u003cp\u003e12.2 General Approach 164\u003c\/p\u003e \u003cp\u003e12.3 Single Equipment Task Optimization 166\u003c\/p\u003e \u003cp\u003e12.4 Intra-Unit Equipment Task Optimization 169\u003c\/p\u003e \u003cp\u003e12.5 Inter-Unit Equipment Task Optimization 172\u003c\/p\u003e \u003cp\u003e12.6 Conclusion 178\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Tankage Determination\/Optimization 179\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Why Tankage Size Matters 179\u003c\/p\u003e \u003cp\u003e13.2 Example Problem Overview 180\u003c\/p\u003e \u003cp\u003e13.3 Same Availability for both Upstream and Downstream Process Units 181\u003c\/p\u003e \u003cp\u003e13.4 Downstream Availability Variable with Constant Upstream Availability 182\u003c\/p\u003e \u003cp\u003e13.5 Conclusion 184\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Improving Availability 185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Options to Improve Availability 186\u003c\/p\u003e \u003cp\u003e14.2 How Reliability and Process Configuration Effects Availability Results 189\u003c\/p\u003e \u003cp\u003e14.3 Which Option Is the Best? 190\u003c\/p\u003e \u003cp\u003e14.4 Conclusion 191\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Equipment Reliability Optimization 192\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 General Approach 193\u003c\/p\u003e \u003cp\u003e15.2 Example Problem Data 195\u003c\/p\u003e \u003cp\u003e15.3 First Impressions of Example Data – Impact on Problem Solution 196\u003c\/p\u003e \u003cp\u003e15.4 Effect of Failure Standard Deviation 197\u003c\/p\u003e \u003cp\u003e15.5 Single Equipment Design Optimization 198\u003c\/p\u003e \u003cp\u003e15.6 Intra-Unit Design Optimization 201\u003c\/p\u003e \u003cp\u003e15.7 Inter-Unit Design Optimization 210\u003c\/p\u003e \u003cp\u003e15.8 Scenario Final Thoughts 212\u003c\/p\u003e \u003cp\u003e15.9 Conclusion 212\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Plant Optimization Within the Design Process 214\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Combining Process Simulation with Monte Carlo Simulation 214\u003c\/p\u003e \u003cp\u003e16.2 Balancing the Short\/Long Term within the Design Process 215\u003c\/p\u003e \u003cp\u003e16.3 Improvement Project 215\u003c\/p\u003e \u003cp\u003e16.4 Debottlenecking Project 218\u003c\/p\u003e \u003cp\u003e16.5 Changes to Plant-Level Model for Grassroots Process Design 221\u003c\/p\u003e \u003cp\u003e16.6 Grassroots Process Unit Design 221\u003c\/p\u003e \u003cp\u003e16.7 Design Considerations 223\u003c\/p\u003e \u003cp\u003e16.8 Conclusion 225\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Combined Optimization 228\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Combination of Improvement Projects and Crude Feed Mix Optimization 229\u003c\/p\u003e \u003cp\u003e17.2 Combining Turnaround and Future Feed Composition 237\u003c\/p\u003e \u003cp\u003e17.3 Conclusion 250\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Mapping Models to Optimization Problems 251\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Mapping Between Optimization Problem and Model(s) Required 251\u003c\/p\u003e \u003cp\u003e18.2 Selection of Optimal Improvement Projects 252\u003c\/p\u003e \u003cp\u003e18.3 Storage Optimization 253\u003c\/p\u003e \u003cp\u003e18.4 Turnaround Timing\/Duration and Equipment Restoration Selection 253\u003c\/p\u003e \u003cp\u003e18.5 Maintenance Plan Options Optimization 253\u003c\/p\u003e \u003cp\u003e18.6 Spares Optimization 253\u003c\/p\u003e \u003cp\u003e18.7 Task Optimization 254\u003c\/p\u003e \u003cp\u003e18.8 Asset Design Optimization 254\u003c\/p\u003e \u003cp\u003e18.9 How to Kickstart Your Program 254\u003c\/p\u003e \u003cp\u003e18.10 Standard Models or Not? 255\u003c\/p\u003e \u003cp\u003e18.11 Process Unit Models 255\u003c\/p\u003e \u003cp\u003e18.12 Site or Plant Models 257\u003c\/p\u003e \u003cp\u003e18.13 Equipment Models 257\u003c\/p\u003e \u003cp\u003e18.14 Responsibility for Equipment Models 258\u003c\/p\u003e \u003cp\u003e18.15 Conclusion 258\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Creating a Program Master Plan 259\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Opportunity Assessment 260\u003c\/p\u003e \u003cp\u003e19.2 Project Selection 262\u003c\/p\u003e \u003cp\u003e19.3 Project Phases 264\u003c\/p\u003e \u003cp\u003e19.4 Resources 266\u003c\/p\u003e \u003cp\u003e19.5 Consultation Process 269\u003c\/p\u003e \u003cp\u003e19.6 Data – and Its Implications 269\u003c\/p\u003e \u003cp\u003e19.7 Technologies 270\u003c\/p\u003e \u003cp\u003e19.8 Work Processes 272\u003c\/p\u003e \u003cp\u003e19.9 Training 273\u003c\/p\u003e \u003cp\u003e19.10 Conclusion 273\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Conclusion 274\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 The Need for a Complex Asset Base 274\u003c\/p\u003e \u003cp\u003e20.2 High-Level Business Goals 275\u003c\/p\u003e \u003cp\u003e20.3 Asset Decisions that Can Drive Optimal Profit 275\u003c\/p\u003e \u003cp\u003e20.4 A Side Benefit → Combining the Process and Equipment Views of the Business 278\u003c\/p\u003e \u003cp\u003e20.5 How to Move Forward with Your Program 280\u003c\/p\u003e \u003cp\u003e20.6 Limitations of Asset Modeling 281\u003c\/p\u003e \u003cp\u003e20.7 Comparing Process and Asset Optimization 281\u003c\/p\u003e \u003cp\u003e20.8 The Future of Optimization 282\u003c\/p\u003e \u003cp\u003eAppendix A Nuts and Bolts of Monte Carlo Simulation 283\u003c\/p\u003e \u003cp\u003eAppendix B Refinery Example Process Description 294\u003c\/p\u003e \u003cp\u003eNotes 308\u003c\/p\u003e \u003cp\u003eIndex 311\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eMarty Moran\u003c\/b\u003e 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.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eOptimize asset decisions and improve the financial and technical operation of process plants\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003ePlant Optimization in the Process Industries\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eReaders will also find: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eComparison and contrast of different technologies on the process and asset side of the industry\u003c\/li\u003e\n\u003cli\u003eDetailed discussion of constrained, non-linear optimization technology, along with basic functioning of Monte Carlo modelling \u003c\/li\u003e\n\u003cli\u003eA real-world case study followed through the book to facilitate understanding\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eThis 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989798502629,"sku":"NP9781119707738","price":145.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119707738.jpg?v=1761785510","url":"https:\/\/k12savings.com\/products\/plant-optimization-in-the-process-industries-isbn-9781119707738","provider":"K12savings","version":"1.0","type":"link"}