Plant Optimization in the Process Industries E-Book

Marty Moran

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 660

Veröffentlichungsjahr: 2024

Beschreibung

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.

Leseprobe

Table of Contents

Cover

Table of Contents

Title Page

Copyright Page

Dedication Page

Foreword by Ron Lambert

About the Author

Process Optimization Experience

Asset Modeling/Optimization Experience

A Unique Perspective

Acknowledgments

Disclaimer

1 Optimizing a Process Plant

1.1 High‐Level Business Goals

1.2 Profit

1.3 Each Plant Is Unique

1.4 Plant Optimization Nirvana

1.5 Process/Asset Views of the Business Need Alignment

1.6 Optimization Technologies on the Process Side of the Business

1.7 Optimization Technologies on the Asset Side of the Business

1.8 Conclusion

1.9 Future Chapters

2 Gen 1 – Transitioning from Reliability to Asset Management

2.1 Reliability’s Early Days

2.2 Rebranding Reliability to be Asset Management

2.3 Changing the Reliability Management Structure

2.4 Where Did Gen 1 Fall Short?

2.5 Adoption of Monte Carlo Simulation Technology Has Struggled

2.6 Asset Optimization Nirvana – The Future

2.7 Conclusion

3 Gen 2 – Plant Optimization Using Asset Modeling Methodologies

3.1 Gen 2 Philosophy

3.2 Gen 2 Asset Optimization Applications

3.3 Conclusion

4 Selecting the Best Improvement Projects – Optimal Process Unit Availability

4.1 Industry Challenge

4.2 Improvement Projects

4.3 Asset Optimization Technologies

4.4 Optimizer Definition

4.5 Optimization Example

4.6 More General Optimization

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

4.8 Conclusion

5 Monte Carlo Simulation Overview

5.1 Reliability Block Diagram (RBD)

5.2 Rolling the Dice

5.3 Histories within a Model Run

5.4 Results

5.5 Submodel – Detailed Process Unit Model

5.6 What Level of Detail Is Required?

5.7 Definitions

5.8 RAM Software Tools

5.9 Challenge to Monte Carlo Simulation Vendors

5.10 Conclusions

6 Optimizer Overview

6.1 Independent Variables

6.2 Dependent Variables

6.3 Constraints

6.4 Objective Function

6.5 Optimizer Problem Definitions

6.6 Conclusions

7 The Consultation Process – The Main Work Process

7.1 Nobody Has Excellent Data in the Process Industries

7.2 Why Operating Conditions Are so Important in the Process Industries

7.3 Tapping into Your Company’s Innate Knowledge

7.4 Golden Opportunity To Test the Approach

7.5 Consulting Meeting Details

7.6 Monte Carlo Modeler Software Inputs

7.7 Data from Asset Management Systems

7.8 Data Storage/Structure

7.9 Conclusion

8 Turnaround Considerations

8.1 Example Problem Overview

8.2 Results Expectations

8.3 Solution Approach

8.4 First Problem – Fixed Start Date and Duration

8.5 Second Problem – Fixed Start Date, but Flexible Duration

8.6 Last Problem – Flexible Start Date

8.7 Conclusion

9 What About Process Conditions?

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

9.2 Operating Condition Effect on Failure Data

9.3 Example Incorporating Process Conditions into Our Problem Definition

9.4 Conclusion

10 Opportunistic Maintenance Optimization

10.1 Modeling Maintenance Plan Options

10.2 Example Problem Data

10.3 Single Equipment Opportunistic Maintenance Optimization

10.4 Intra Unit Opportunistic Maintenance Optimization

10.5 Inter Unit Opportunistic Maintenance Optimization

10.6 Conclusion

11 Spare Parts Optimization

11.1 Spares Parts Dependence Often Masks Other Equipment Issues

11.2 Typical Methods for Estimating Spare Parts

11.3 Logistical Challenges

11.4 Lead Times/Price/Vendor Issues

11.5 Prioritization

11.6 Example Problem Data

11.7 Effect of Failure Standard Deviation

11.8 Optimization Problems Overview

11.9 Single Equipment Spares Optimization

11.10 Intra‐Unit Spares Optimization

11.11 Inter‐Unit Spares Optimization

11.12 Common Spare Across Multiple Units

11.13 Full‐time Spare Parts Engineer Position

11.14 Conclusion

12 Task/Resource Optimization

12.1 Example Problem Data

12.2 General Approach

12.3 Single Equipment Task Optimization

12.4 Intra‐Unit Equipment Task Optimization

12.5 Inter‐Unit Equipment Task Optimization

12.6 Conclusion

13 Tankage Determination/Optimization

13.1 Why Tankage Size Matters

13.2 Example Problem Overview

13.3 Same Availability for both Upstream and Downstream Process Units

13.4 Downstream Availability Variable with Constant Upstream Availability

13.5 Conclusion

14 Improving Availability

14.1 Options to Improve Availability

14.2 How Reliability and Process Configuration Effects Availability Results

14.3 Which Option Is the Best?

14.4 Conclusion

15 Equipment Reliability Optimization

15.1 General Approach

15.2 Example Problem Data

15.3 First Impressions of Example Data – Impact on Problem Solution

15.4 Effect of Failure Standard Deviation

15.5 Single Equipment Design Optimization

15.6 Intra‐Unit Design Optimization

15.7 Inter‐Unit Design Optimization

15.8 Scenario Final Thoughts

15.9 Conclusion

16 Plant Optimization Within the Design Process

16.1 Combining Process Simulation with Monte Carlo Simulation

16.2 Balancing the Short/Long Term within the Design Process

16.3 Improvement Project

16.4 Debottlenecking Project

16.5 Changes to Plant‐Level Model for Grassroots Process Design

16.6 Grassroots Process Unit Design

16.7 Design Considerations

16.8 Conclusion

17 Combined Optimization

17.1 Combination of Improvement Projects and Crude Feed Mix Optimization

17.2 Combining Turnaround and Future Feed Composition

17.3 Conclusion

18 Mapping Models to Optimization Problems

18.1 Mapping Between Optimization Problem and Model(s) Required

18.2 Selection of Optimal Improvement Projects

18.3 Storage Optimization

18.4 Turnaround Timing/Duration and Equipment Restoration Selection

18.5 Maintenance Plan Options Optimization

18.6 Spares Optimization

18.7 Task Optimization

18.8 Asset Design Optimization

18.9 How to Kickstart Your Program

18.10 Standard Models or Not?

18.11 Process Unit Models

18.12 Site or Plant Models

18.13 Equipment Models

18.14 Responsibility for Equipment Models

18.15 Conclusion

19 Creating a Program Master Plan

19.1 Opportunity Assessment

19.2 Project Selection

19.3 Project Phases

19.4 Resources

19.5 Consultation Process

19.6 Data – and Its Implications

19.7 Technologies

19.8 Work Processes

19.9 Training

19.10 Conclusion

20 Conclusion

20.1 The Need for a Complex Asset Base

20.2 High‐Level Business Goals

20.3 Asset Decisions that Can Drive Optimal Profit

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

20.5 How to Move Forward with Your Program

20.6 Limitations of Asset Modeling

20.7 Comparing Process and Asset Optimization

20.8 The Future of Optimization

Appendix A: Nuts and Bolts of Monte Carlo Simulation

A.1 Example Projects

A.2 Monte Carlo Modeling Steps

A.3 Using Monte Carlo Modeling in Absolute vs. “Delta” Sense

Appendix B: Refinery Example Process Description

B.1 Crude Oil

B.2 Crudes Used in Problem Examples

B.3 Crude Unit

B.4 Saturated Gas Plant

B.5 Catalytic Reformer

B.6 Naphtha Splitter/Isomerization Unit

B.7 Diesel Hydrotreater

B.8 Vacuum Tower

B.9 Conversion Processes

Notes

About the Author

Chapter 3

Chapter 4

Chapter 7

Chapter 13

Chapter 14

Appendix B

Index

End User License Agreement

List of Tables

Chapter 3

Table 3.1 Original vs. Optimal Availability

Chapter 4

Table 4.1 Possible Optimal Process Unit Availabilities

Table 4.2 Hydrocracker Project Costs and Incremental Availability Increase...

Table 4.3 Incremental Project Costs per Availability

Table 4.4 Base Case Availabilities

Table 4.5 Assumed Market Prices

Table 4.6 Base Case Results

Table 4.7 Improvement Projects Costs at 98% Availability

Table 4.8 Results – 98% Availability

Table 4.9 Optimal Process Unit Availabilities

Table 4.10 Optimal Process Unit improvement Project Costs

Table 4.11 Optimum Case Results

Table 4.12 Coker Project Cost Changes

Table 4.13 FCC Project Cost Changes

Table 4.14 Hydrocracker Project Cost Changes

Table 4.15 Crude Unit Project Cost Changes

Table 4.16 Project Execution Order

Chapter 7

Table 7.1 Example Consultative Meeting Documentation

Chapter 8

Table 8.1 Equipment to be Considered for Turnaround

Table 8.2 Listed by Earliest Failures

Table 8.3 Including second and third Standard Deviations

Table 8.4 Add Cost and Repair Time Information

Table 8.5 All Relevant Equipment Information

Table 8.6 No Repairs

Table 8.7 Ages at Turnaround 1/2 End – No Repairs

Table 8.8 Repair All Equipment First Turnaround – Nothing Second Turnaround...

Table 8.9 Ages at Turnaround 1/2 End – All Equipment Repaired Turnaround 1...

Table 8.10 Repair All Equipment Second Turnaround

Table 8.11 Base Case – Results

Table 8.12 Base Case – Equipment Repair Decisions

Table 8.13 Turnaround 2 Cost Constraint – Results

Table 8.14 Turnaround 2 Cost Constraint – Equipment Repair Decisions

Table 8.15 $4.0 MM Turnaround 1 Cost Constraint – Results

Table 8.16 $4.0 MM Turnaround 1 Cost Constraint – Equipment Repair Decision...

Table 8.17 $2.5 MM Turnaround 1 Cost Constraint – Results

Table 8.18 $2.5 MM Turnaround 1 Cost Constraint – Equipment Repair Decision...

Table 8.19 $1.0 MM Turnaround 1 Cost Constraint – Results

Table 8.20 $1.0 MM Turnaround 1 Cost Constraint – Equipment Repair Decision...

Table 8.21 Base Case – Equipment Repair Decisions

Table 8.22 Fixed Start Date – Flexible Duration

Table 8.23 Possible Equipment D Repair Decisions – Flexible Duration

Chapter 9

Table 9.1 Crude A/B Material Balances

Table 9.2 Optimization Results

Chapter 10

Table 10.1 Example Problem Data

Table 10.2 Isom Problem Data

Table 10.3 Reformer Problem Data

Table 10.4 Example Problem Data

Chapter 11

Table 11.1 Standard Lead Time Groups

Table 11.2 Standard Pricing

Table 11.3 Example Problem Basic Data

Table 11.4 First Failure in Future

Table 11.5 Start Up Failures

Table 11.6 Common Spares Example Data

Table 11.7 Where Spare Installed and Age

Table 11.8 Unit Margins

Table 11.9 Maximum Production Loss per Equipment

Chapter 12

Table 12.1 Example Problem Basic Data

Table 12.2 Resource Pricing

Table 12.3 Example Problem Task Data

Table 12.4 Equipment C Optimization Results

Table 12.5 Hydrocracker Unit Optimization Results

Table 12.6 Hydrocracker Unit New Task Resource Allocations

Table 12.7 All Equipment Optimization Results – Unconstrained Cases

Table 12.8 All Equipment Optimization Results – Constrained Cases

Table 12.9 All Equipment New Task Resource Allocations

Chapter 15

Table 15.1 Example Problem Data

Table 15.2 Equipment Failure Standard Deviation

Table 15.3 Standard Deviation vs. Failure Times

Table 15.4 Equipment A Failure Data

Table 15.5 Isom Equipment Failure Data

Table 15.6 Relation Between Optimizer and Discrete Choices – Cost Constrain...

Table 15.7 Relation Between Optimizer and Discrete Choices – Margin

Table 15.8 Reformer Equipment Failure Data

Chapter 16

Table 16.1 Summary of Project Type Differences

Chapter 17

Table 17.1 Process Unit Historical Availabilities

Table 17.2 Assumed Market Prices

Table 17.3 Crude A/B Product Material Balance

Table 17.4 Equipment to be Considered for Turnaround

Table 17.5 Crude A Rearranged Information

Table 17.6 Crude A/B Failure Information Comparison

Table 17.7 Crude B Rearranged Information

Table 17.8 $60 Crude Turnaround – Results

Table 17.9 $60 Crude Turnaround – Equipment Decisions

Table 17.10 $65 Crude Turnaround – Results

Table 17.11 $65 Crude Turnaround – Equipment Decisions

Table 17.12 $67.50 Crude Turnaround – Results

Table 17.13 $67.50 Crude Turnaround – Equipment Decisions

Table 17.14 $70 Crude Turnaround – Results

Table 17.15 $70 Crude Turnaround – Equipment Decisions

Table 17.16 $72.50 Crude Turnaround Results

Table 17.17 $72.50 Crude Turnaround Equipment Decisions

Table 17.18 No Turnaround Cost Constraints – Results

Table 17.19 No Turnaround Cost Constraints – Equipment Decisions

Table 17.20 $50 MM Turnaround 2 Cost Constraint – Results

Table 17.21 $50 MM Turnaround 2 Cost Constraint – Equipment Decisions

Table 17.22 $30 MM Turnaround 2 Cost Constraint – Results

Table 17.23 $30 MM Turnaround 2 Cost Constraint – Equipment Decisions

Chapter 18

Table 18.1 Optimization Problem – Model Mapping

Chapter 19

Table 19.1 Question Summary Format

Table 19.2 Projects Not Suitable for Optimization

Table 19.3 Good Project Candidates for Optimization

Table 19.4 “Value, If Improved” Quantitative Ratings

Table 19.5 “Implementation Ease” Quantitative Ratings

Table 19.6

“

Implementation Cost” Quantitative Ratings

Table 19.7 Substituting Quantitative Values into Main Table

Table 19.8 Add Improved Rating

Table 19.9 Delta Improvement

Table 19.10 Final Ranking

Table 19.11 Who's Affected by Project Type

Appendix B

Table B.1 Crude A/B Material Balances

Table B.2 Process Unit Feed rates

Table B.3 Crude Unit Products

Table B.4 Material Balance after Processing in Sat Gas Plant

Table B.5 Cumulative Material Balance after Processing in Reformer

Table B.6 Cumulative Material Balance after Processing in Vacuum Unit

Table B.7 Cumulative Material Balance after Processing in FCC

Table B.8 Cumulative Material Balance after Processing in Aklylation Unit

Table B.9 Cumulative Material Balance after Processing in Coker

Table B.10 Cumulative Material Balance after Processing in Hydrocracker

List of Illustrations

Chapter 1

Figure 1.1 Plant Optimization – Process/Equipment Relationship

Chapter 3

Figure 3.1 Optimizer/Monte Carlo Hierarchy

Figure 3.2 Intermediate Calculations

Figure 3.3 Turnaround Optimization Overview

Figure 3.4 Process Unit Target Availability Optimization

Figure 3.5 Process Unit Total Project Cost vs. Availability

Figure 3.6 Incorporating Process Conditions into Optimization

Figure 3.7 Spare Parts Optimization

Figure 3.8 Pump Failure Rate vs. Oil Change Interval

Figure 3.9 Pump Oil Change Interval Optimization

Chapter 4

Figure 4.1 Example Refinery

Figure 4.2 Discrete Hydrocracker Improvement Projects

Figure 4.3 Regressed Hydrocracker Improvement Projects

Figure 4.4 Process Unit Total Project Cost vs. Availability

Figure 4.5 Optimizer/Monte Carlo Hierarchy

Figure 4.6 Optimization Sequence Steps

Figure 4.7 Process Unit Target Availability Optimization

Figure 4.8 Gasoline Price vs. Optimal Availability

Figure 4.9 Optimal Availability – Increased Hydrocracker Project Costs

Figure 4.10 Optimal Availability – Increased Crude Unit Project Costs

Figure 4.11 Optimal Availability – Combined Increased Project Costs

Figure 4.12 Optimization – Total Improvement Project Cost Constraint

Figure 4.13 Optimization – Gasoline Production Constraint

Chapter 5

Figure 5.1 Semi‐Regen Reformer

Figure 5.2 Reformer Reliability Block Diagram

Figure 5.3 History Run 1

Figure 5.4 History Run 2

Figure 5.5 History Run 3

Figure 5.6 Availability Probability Distribution

Figure 5.7 Largest Contributors to Unavailability

Figure 5.8 Debutanizer Reliability Block Diagram

Chapter 8

Figure 8.1 Turnaround Optimization Overview

Figure 8.2 Turnaround 2 Start Time Adjustment vs. Margin

Chapter 9

Figure 9.1 Failure Rate Increase vs. Crude B%

Chapter 10

Figure 10.1 Discrete Maintenance Plan Options

Figure 10.2 Continuous Maintenance Plan Options

Figure 10.3 Maintenance Options per Equipment – Continuous

Figure 10.4 Naphtha Splitter Repair Time vs. Margin

Figure 10.5 Isom Repair Time vs. Margin

Figure 10.6 Reformer Repair Time vs. Margin

Figure 10.7 All Units Repair Time vs. Margin

Figure 10.8 All Units – Cost Constraint

Chapter 11

Figure 11.1 Inventory/Production Loss vs. Failure Std. Deviation

Figure 11.2 Relationship Between Average/Max Production Losses

Figure 11.3 Spare Price vs. Optimal Inventory – Single Equipment

Figure 11.4 Spare Inventory vs. Reformer Margin

Figure 11.5 Production Loss vs. Reformer Margin/Total Spares Cost Limit

Figure 11.6 Spares Cost/Production Loss vs. Spares Inventory

Figure 11.7 Increased Failure Time Standard Deviation

Figure 11.8 Increase Mean Failure Time

Figure 11.9 Production Losses vs. Lead Time

Chapter 13

Figure 13.1 50,000 bbl Tank

Figure 13.2 300,000 bbl Tank

Figure 13.3 Tankage Level Violations – Equal In/Out Availabilities

Figure 13.4 Tankage Level Violations – Variable Outgoing Availabilities

Chapter 14

Figure 14.1 Example Series Availability – Two Units

Figure 14.2 Example Series Availability – Five Units

Figure 14.3 Example Parallel Availability

Chapter 15

Figure 15.1 Design Options – Discrete Choices

Figure 15.2 Design Options – Continuous Choices

Figure 15.3 Design Options per Equipment – Continuous

Figure 15.4 Naphtha Splitter Availability vs. Failure Time

Figure 15.5 Naphtha Splitter Margin vs. Optimal Failure Time

Figure 15.6 Isom Design Costs – Constrained Availability

Figure 15.7 Isom Design Costs – Cost Constraint

Figure 15.8 Isom Design Costs – Margin

Figure 15.9 Reformer Design Costs – Margin

Figure 15.10 Reformer Design Costs – Cost Constraints

Figure 15.11 Reformer Equipment D/E Total Design Costs

Figure 15.12 Reformer Total Design Costs – Tighter Standard Deviation

Figure 15.13 All Units – Total Design Cost Constraint

Chapter 17

Figure 17.1 Process Unit Total Project Cost vs. Availability

Figure 17.2 Failure Rate Increase vs. Crude B%

Figure 17.3 Gasoline Yield vs. Crude B Price

Figure 17.4 Financial Results vs. Crude B Price

Figure 17.5 Project Costs vs. Crude B Price

Figure 17.6 Optimal Process Unit Availability vs. Crude B Price

Appendix A

Figure A.1 Catalytic Reformer Reliability Block Diagram

Figure A.2 Debutanizer Reliability Block Diagram

Figure A.3 Availability Probability Distribution

Figure A.4 Largest Contributors to Unavailability

Appendix B

Figure B.1 Crude Unit

Figure B.2 Add Sat Gas Plant

Figure B.3 Add Reformer

Figure B.4 Add Naphtha Splitter and Isom

Figure B.5 Add Hydrotreater

Figure B.6 Add Vacuum Tower

Figure B.7 Add FCC

Figure B.8 Add Alkylation

Figure B.9 Add Coker

Figure B.10 Add Hydrocracker

Guide

Cover Page

Table of Contents

Title Page

Copyright Page

Dedication Page

Foreword by Ron Lambert

About the Author

Acknowledgments

Disclaimer

Begin Reading

Appendix A Nuts and Bolts of Monte Carlo Simulation

Appendix B Refinery Example Process Description

Notes

Index

WILEY END USER LICENSE AGREEMENT

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Plant Optimization in the Process Industries

Incorporating Equipment/Assetsin the Decision‐Making Process

Marty Moran

Copyright © 2025 by John Wiley & Sons, Inc. All rights reserved, including rights for text and data mining and training of artificial intelligence technologies or similar technologies.

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This book is dedicated to those who struggle with either MS or transverse myelitis. Let’s hope science can eventually solve these horrible diseases.

Foreword by Ron Lambert

It is with great pleasure that I have been asked to write the foreword for this exceptional book on Process Plant Optimization, authored by a distinguished individual who has significantly impacted the industry with his innovative ideas and forward‐thinking approach.

I first crossed paths with Marty Moran during our time at Meridium (GE Digital), where we both worked as consultants. Marty’s expertise in Reliability, Availability, and Maintainability (RAM) studies made him an invaluable asset to the product team, as he provided groundbreaking ideas and conducted crucial RAM studies using the Meridium product. However, what truly set Marty apart was his ability to see the “Big Picture” and project into the future the benefits gained applying his diverse skill set. It was this exceptional talent that led me to hire him at Sadara for a completely different role – Instrument Reliability Engineer, knowing that his capabilities and contributions would extend far beyond Instrument Reliability and where he made remarkable contributions to our team.

During Marty’s tenure at Sadara, he played a pivotal role in several high‐stakes projects, using his expertise to solve complex engineering challenges. His unique background in process control and his ability to think beyond single pieces of equipment made him an invaluable resource for our team. I distinctly remember the crucial role he played in determining the root cause of a significant incident within an ethylene furnace, as well as his invaluable contributions applying his advanced multivariable control knowledge to lead the team optimizing the ethylene unit.

Marty’s dedication to obtaining data through consultation with those knowledgeable about the unit is a philosophy that resonates deeply with me. The relationships he built displaying shared concern and curiosity with those needing help greatly increased the influence he had in seeing that correct actions would be seen through to their completion and results. His emphasis on using engineering skills and experience to make reasonable assumptions, rather than blindly relying on data, is an approach that is both practical and invaluable in our industry.

Having had the opportunity to review the early stages of this book, I was struck by the innovative concepts presented within its pages, particularly the integration of optimization similar to advanced control algorithms coupled with Monte Carlo reliability modeling. Marty’s emphasis on including constraints within all optimization problems, and his unwavering focus on business financial optimization, truly represents the direction in which our industry should be heading. It is my firm belief that this book will make a significant contribution to the field of process plant strategy and optimization, and will undoubtedly shape the way we approach these critical concepts in the future.

In conclusion, Marty’s groundbreaking ideas and forward‐thinking approach have paved the way for a new era in Reliability and Optimization. It is my sincere hope that this book will inspire and empower the next generation of engineers, and lead our industry to new heights.

Ron Lambert

Engineering ManagerAtlantic Methanol Production Company

About the Author

I grew up in an ethnic Irish neighborhood on the west side of Chicago in the 1960s/1970s and hold both American and Irish citizenship. I attended college in downtown Chicago at the University of Illinois and graduated with a BS in Chemical Engineering in March 1982. I am currently a Chartered Engineer in Engineers Ireland.

At a high level, my career has concentrated on using Advanced Manufacturing Technologies, such as Advanced Process Control/Optimization and Asset Optimization Technologies that helped customers in the process industries increase throughput, reduce manufacturing costs, increase asset availability, and reduce risk with progressive roles in technical, consulting, business, and marketing.

Due to the fact that I worked for Advanced Manufacturing Technology vendors for many years, I have spent considerable time in sales/marketing roles. I have been fortunate in that I have worked in over 60 plants in 25 countries on 6 continents.

Most of my “process” experience has been concentrated in the worldwide oil refining industry. However, I have also worked on projects in midstream, a couple ammonia plants, and in a large‐scale petrochemical plant.

While I spent the majority of my career working for vendors, my most recent experience was working directly for a massive petrochemical plant in the Middle East, filling a role in a group of technical experts, leveraged across more than 20 petrochemical plants. During my last year there, I requested and accepted a role to be the technical lead for the Advanced Control/Optimization project. For the three years prior to that assignment, I was a Senior Reliability Engineer providing consulting for the strategic portion of the program, in addition to being used for special projects when a seasoned engineer was required. Ultimately, I got to play significant roles on both the “process” and “asset” sides of the business, in addition to using my marketing and writing skills.

My personal interest has always been in the area of optimization. To that end, I have principally worn two hats in my career. One was to “optimize” the process side of the business using Advanced Process Control/Optimization, while I spent the second half of my career trying to optimize assets using various technologies. The essence of this book is to couple optimization of the processes with optimization of the assets thereby multiplying the benefits to the business.

While I am proud of my technical capabilities, I am business orientated and believe that technology should only be used if it achieves a specific business result. In my mind, business comes first. Technology only exists to further our business needs.

For that reason, the focus of this book has been generally on Optimization and, more specifically, on how Optimization and Monte Carlo simulation can be pragmatically combined together to optimize profitability in the process industries through better asset decisions. This book has walked the balance between business needs and how that can be achieved technically. In addition, it has described what organizational changes must be made for a company to effectively adapt modeling/optimization technologies. It has suggested a Program Master Plan.

To better appreciate the thoughts in this book and how I came to these conclusions, it’s important to traverse the path of how I became involved with asset optimization technologies and specific experiences I had earlier in my career.

Process Optimization Experience

While I will concentrate principally on my asset optimization experience in “About the Author,” it’s important to highlight what I learned during my early years as an Advanced Control and Optimization (ACO) engineer. From late 1987 until the early 2000s, I was an ACO engineer. I worked for SETPOINT until early 1994, where I learned both Advanced Control and Optimization. The latter is especially important as that is the focus of this book.

At that time, we were using closed equation optimization technologies. I was quite familiar with GRG2, which is a reduced gradient, nonlinear optimization, originally developed at the University of Texas. Many of you may have already worked with it, though you probably didn’t know it. That’s because it’s now embedded within Excel as the Solver. That’s what we will use for the majority of optimization problems in this book.

When I left SETPOINT, I joined Continental Controls, where I developed their multivariable controller, for which I received a US patent.ix I was so impressed with GRG2 during my time working at SETPOINT that I used it as the mathematical engine for this new multivariable controller. What I did was fundamentally write a sophisticated “wrapper” that called the GRG2 mathematical algorithm to perform nonlinear multivariable control.

Those early years working with optimization were important because I became convinced that one essential missing ingredient in asset management was the lack of an emphasis on true optimization. What you’ll see in this book is that we will be now combining, and augmenting, system modeling with optimization technologies.

Another thrust during my years optimizing the process side was the emphasis that we placed on driving business benefits. I had the opportunity to lead many process studies where we had to justify our projects based on business benefits. As I moved to the asset side of the business, that justification seem to be lacking, or only haphazardly done.

I should also note that while at SETPOINT, I served as the Hydroprocessing Technology Manager. That will explain why you’ll see so many hydroprocessing examples in the book.

Asset Modeling/Optimization Experience

In early 2003, my career changed significantly. In the 15 years proceeding that, I had been focused on Advanced Process Control and Optimization. However, due to a rather dramatic and unanticipated career shift, I found myself as the Director of Sales and Marketing at Clockwork Solutions.

Clockwork was a pure play RAM high‐end software modeling company. I was tasked with selling projects to clients to improve their ability to make better design and spare parts decisions using RAM, as well as other standard uses of the technology that we now take for granted. Unfortunately, what I soon learned was that up to this point in time, there had been little adoption of this technology within the process industries. In fact, there had been more projects executed within the military aerospace industry, rather than in refining and petrochemicals. As a new salesperson, this was especially disconcerting, to say the least.

Furthermore, the people attracted to a company like Clockwork were technical types and not nearly as business focused as I had grown accustomed to in my career. Even more troubling was that RAM modeling was still largely a “cool” technology in search of some practical business problems to solve.

In fact, the technology was not even being sold properly. Everything was backward. Normally, one starts with a business problem and evaluates different technologies and their potential unique abilities to solve a particular problem. However, in this case, it was just the opposite. Here, the technology already existed, but it wasn’t clear at all as to what business problems this technology could be used to solve. If I were to have any hope of surviving in my new sales role, I needed to articulate that – and quickly.

Of course, from a sale person’s perspective, the problems the technology can solve must occur frequently enough that a company would be interested in purchasing software and accompanying services. In addition, the cost/benefit of solving those problems needs to be such that that a company would be interested in spending money on them. Plus, the sales process needs to occur in a timeframe where I could close enough deals so my employer would keep pursuing this business area. I knew I was in trouble.

For all those reasons, I decided to write a paper and give a presentation at the National Petrochemical and Refining Association (NPRA) conference in March 2004 that detailed how RAM modeling driven by an optimization technology could be used by a refiner to increase the quantity of gasoline being produced based on the availability of the different units producing gasoline components, balanced against the cost of various potential improvement projects.x

In essence, this presentation showed how to answer an important business question. That is, where should I spend my next dollar to optimally increase availability? That entire presentation was eventually republished by Petroleum Technology Quarterly in April 2004.xi

As is so often the case with marketing, it takes times for an idea to gain acceptance. Based on the response from that conference, it became clear that the industry wasn’t ready for this technology any time soon and that it would likely take years for large‐scale interest and/or adoption.

Since Clockwork would not be moving fast enough for my interests, I decided there might be a niche use for this technology that I could exploit as an entrepreneur. To that end, I started writing a VBA‐based program on my own using Monte Carlo simulation modeling that would determine the optimal turnaround timing between units based on the reliability performance of different equipment within the units. To garner funding for the idea, I applied for a grant from the National Science Foundation (NSF) for a small company I had formed.xii Unfortunately, the NSF review panel split their votes 3‐3 and the idea was not approved.

Given that setback, I eventually accepted a position at Meridium where I played numerous roles, For the first 18 months, I taught training classes on how to use their software to solve technical problems and generate business benefits. While I could teach all their classes, I was used primarily for engineering related subjects. For example, I taught the Reliability Analytics class many times, which included a portion dedicated to RAM modeling.

Another role I had was being an internal product consultant for their RAM modeling product. In that job, I worked with the product manager over several product development cycles to make numerous changes to their RAM modeling product. For example, we dramatically improved RAM analytics and added spare parts functionality, as well as tighter integration with other parts of the Meridium reliability suite.

Eventually, I became a functional consultant and I got a chance to implement several RAM modeling projects. One was for an Australian mining company, who were desperately trying to increase capacity to meet iron ore demand from China. Another was for a large refiner evaluating different electrical grid configurations.

While at Meridium, I was selected by the company president to help create the Meridium Value Assurance Program (MVAP).xiii which was a comprehensive reliability assessment technology that graded a client in 30 different elements of a reliability program. 10 of the 30 elements were specific Reliability Work Processes, one of which was Reliability in Design. This work process encompassed RAM as one of its principal components. Each element contained approximately 10 different assessment questions ranked against standards for each of 4 different maturity levels.

Another important aspect of the MVAP program was the ability to estimate a client’s benefits when making improvements in their maturity levels. For example, I could estimate a client’s benefits from improving their Reliability in Design program using just client supplied data and the MVAP program methodology.

Later, when I returned to my personal consulting practice, I created and implemented a large‐scale RAM model for a multi‐billion‐dollar, coal gasification plant in late 2014/early 2015.xiv The completed model would show that the plant could not achieve the specified technical and business objectives and would later become evidence supporting legal action brought by the taxpayers against the utility.

At Sadara, I was hired as an Instrument Reliability Engineer, so I never worked directly with their RAM modeling technology or associated data. That program had been used to determine the exact processes, plant configuration, and equipment size and had been completed in Houston years before I started working for the company at their Saudi plant site. (I hadn’t started until about 35% of the plant was already running with the remainder nearing ‘Mechanical Completion’.)

However, I eventually morphed into being the group ‘marketing’ person due to my experience writing booksxv and working in sales/marketing organizations. (Every reliability program should have a marketing person in the group that can help publicize their successes or lest they be forgotten about or be taken for granted.) For that reason, the Reliability Director asked me to write a Sadara success story article that would later appear in Hydrocarbon Processing.xvi This was my preferred publication, since I had been an author there frequently in the past. xviixviiixixxx This article would be a typical ‘Case Study’, of which I had written many during my marketing career.

When I interviewed the Reliability department management for the article, they all felt strongly that Sadara’s RAM effort was one important reason why their plant availability was so high upon start up. While I didn’t have an opinion since I hadn’t participated in the program, nor had access to the specific software or required training needed for the program, their response didn’t surprise me since I had known the power of modeling from my previous 15 years of experience with the technology.

What Sadara claimed was what we, as vendors, had been preaching for many years. It was just that Sadara was the first grassroots plant to use this technology on such a grand scale. Sadara’s experience and recent changes in the vendor landscape are encouraging because we may have finally gotten to the point where more executives throughout industry might now be open to the idea of using this technology.

As impressive as that effort was, the new intellectual property introduced in this book will move beyond just RAM models by layering optimization on top of it. A useful analogy is that Monte Carlo modeling is like a dial telephone, while the technology introduced in this book more akin to a smartphone. It extends it is new direction.

A Unique Perspective

To summarize, there were four major events that occurred throughout my career that allowed me to provide a unique perspective on the thoughts articulated in this book as noted below:

1996 US Multivariable Control Patent

2004 NPRA Paper

2004 NSF Overhaul Optimization Submission

2014/2015 Plant‐Wide Modeling Project

Let’s take each of these one at a time to understand how they added value.

The multivariable controller that I wrote in 1994 used GRG2 as it’s “heart and soul.” Fundamentally, I wrote software around this core optimization technology to implement multivariable control. This application was both a traditional closed equation optimizer and a pure multivariable controller. This is important because those on the reliability/asset management side of the business are not trained to think in “optimization” terms. I had an advantage because those of us who worked on the “process” side of the business had been heavily exposed to this technology.

The 2004 NPRA paper was the first detailed application that combined standard optimization technology with Monte Carlo modeling. Once that groundwork had been laid, it would be easy to extend it to other applications. In fact, that would occur later that same year.

Turnaround Optimization was the second application attempted. While the NSF application in December 2004 for “Overhaul Optimization” was not funded, the intellectual foundation had been created. (In fact, I had already written much of the code for that application, though it was never used in any commercial application.)

The last major experience was the plant‐wide model that I had created for a US coal gasification plant in late 2014/early 2015. While I had been using RAM modeling on a number of important projects in prior years, this was my first large‐scale attempt at using a “Consulting” approach to generate the required data for the models. The Consulting approach is also the main work process used for this methodology.

These four major experiences laid the groundwork for easily extending optimization to the other applications as cited in the book, as well as the methodologies proposed.

Acknowledgments

I start by thanking all my colleagues in the process industries. Over the past 40+ years, I’ve had the opportunity to work with so many talented professionals. As my LinkedIn page can attest, the list is long and impressive. At times when I reflect on my career, I realize how blessed I am not only to have had so many unique experiences all around the world but also to have shared that time with so many exceptional individuals. Given how many high‐quality people I have interacted during my career, in this section, I will only call out a few key individuals that significantly impacted this book in some manner. Otherwise, this section would become far too long.

While I thoroughly enjoyed learning about chemical engineering while in college, my career got off to a rocky start. I had the unfortunate distinction of graduating at the worst possible time in the history of chemical engineering – March 1982.

I always remember a discussion in a Starbucks a few years ago with a gentleman roughly my age. When he learned that I was a chemical engineer of his era, and was still practicing chemical engineering, he was astonished. He couldn’t believe that I could have somehow navigated the layoff minefield of the mid‐1980s. It wasn’t always fun.

I started working professionally in November 1982 when I accepted a job at a company on the west side of Chicago that manufactured onstream process analyzers for the refining industry. It would be the start of a career anchored in oil refining that would ultimately be exceptionally good to me.

My career blossomed when I joined SETPOINT in Houston in November 1987. While I did well in college scholastically, SETPOINT was a humbling experience because I realized what it was like to a “C” student. That is, I was around so many talented people that I was often in awe of them. It took some years to get to their level, but their mentoring must have worked well because I now knew the industry well. It’s the experience gained at SETPOINT that I would leverage during the remainder of my career – and is certainly reflected in this book.

I was taught the business/process side of the refining industry by many people. However, one person stands out: Walter Bare took my aside and taught me the refining industry in a way that few others could. I would not be where I am without his help. SETPOINT was instrumental in many other ways since that is where I really got to practice optimization, which is the central element of this book.

As for this book specifically, I thank Ron Lambert, who has contributed in many ways. Ron and I first met when we were both working at Meridium. While we crossed paths occasionally in those years, we really got to know one another when I joined Sadara, where Ron was the Reliability Department Director.

Philosophically, both Ron and I believed that Reliability had become too enamored with its own technologies/methodologies and often forgot that the goal of a plant was to make product – not care for individual assets. We were both pragmatists, who had a conviction that one didn’t need to have perfect data to make good decisions. Rather, we strongly agreed that we should lean heavily on utilizing engineering assumptions as one of the better sources of data for this type of program from the many different people who had intimate knowledge of the unit.

I thank Greg Weber. A former Turnaround and Reliability Manager at one the world’s largest integrated oil companies, we first met some years ago through a colleague who had recommended me for a project that Greg was working on. He eventually contracted me to put together a Monte Carlo simulation model for a large plant for one of his better US clients. As we worked together on that project, I educated him on how Monte Carlo technology could pragmatically be used to make better asset decisions.

In fact, Chapter 7 largely describes the process we used during that project to obtain modeling data. I had had this idea floating in my head for years, but this was the first project where we used that idea in a major way. Greg and his colleague conducted the client workshop to generate the data for the model I developed, independent of my involvement. What amazed me was how quickly they achieved that goal and the quality of the information that they gathered during this process. This gave me the confidence to know that the consulting methodology that we highlight later in the book was not only possible, but absolutely the right approach for asset modeling.

Disclaimer

The information and insights presented in this work are based on optimization, combined with Monte‐Carlo simulation. As an author, I acknowledge the following: (i) the content within this work has been developed through rigorous analysis, empirical data, and industry expertise. All examples were developed within Excel™ using Excel’s Solver as the optimization engine and Excel’s data tables to simulate Monte‐Carlo principles; (ii) the optimization techniques described herein are not based on any proprietary methodology; and (iii) the ideas developed herein, in general, are based on the author’s experience using Advanced Control and Optimization, including a US patent owned by the author.

The publisher and the author make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation warranties of fitness for a particular purpose. No warranty may be created or extended by sales or promotional materials. The advice and strategies contained herein may not be suitable for every situation. This work is sold with the understanding that the publisher is not engaged in rendering legal, accounting, or other professional services. If professional assistance is required, the services of a competent professional person should be sought. Neither the publisher nor the author shall be liable for damages arising here from. The fact that an organization or Website is referred to in this work as a citation and/or a potential source of further information does not mean that the author or the publisher endorses the information the organization or Website may provide or recommendations it may make. Further, readers should be aware that Internet Websites listed in this work may have changed or disappeared between when this work was written and when it is read.

1Optimizing a Process Plant

Fundamentally, this book is about optimization. To be more specific, optimizing a process plant.

However, like love, where beauty is in the eye of the beholder, optimization can, and often does, mean different things to different people. It all depends upon your vantage point.

While engineers like to focus on all the technical details in a process plant, and often lose sight of the big picture, it’s important to understand that oil refining, petrochemical manufacturing, and specialty chemical, food, and pharmaceutical manufacturing enterprises are profit‐seeking businesses.

1.1 High‐Level Business Goals

Maximizing profit entails many factors, but for the most part, the high‐level business goals of most process plants are well known and straightforward:

Maximize production, when able to do so, using the existing asset base

Keep the plant running as long as possible between turnarounds. However, when choosing turnaround timing, duration, and what equipment to be restored balance the increased production of longer run rates against exposing the plant to unacceptable safety or production failure risks

Carefully manage capital dollars to earn the best return on those investments

Perform only as much day‐to‐day maintenance that balances short‐ and long‐term production needs in the most cost‐efficient manner

Maintain enough spare parts to adequately service equipment, especially to prevent any production disruptions, but do not overstock

When new capital projects are proposed, consider designs within budget constraints, with higher reliability if they can be cost‐justified

Utilize outside resources to supplement internal resources when it is in the plant’s business interests to do so

Let’s dig a bit deeper into plant profit and its two main components – revenue and costs.

1.2 Profit

When we refer to optimization in this book, we will be focused on helping make decisions using Advanced Manufacturing Technologies to maximize the profit of a facility, balanced across all the important time horizons – short, medium, and long term. While this book is principally focused on using asset modeling technologies to achieve that goal, we will also compare and contrast that with other advanced manufacturing technologies that have been used in other aspects of the business, principally on the process side, when appropriate.

Profit is defined as revenue minus costs. Obviously, senior management is heavily compensated to maximize profit, and there are many factors at work in being able to achieve that goal.

1.2.1 Revenue

One aspect of maximizing profit is to maximize revenue.

For a process plant, revenue is simply the amount of product produced multiplied by its sales price. A plant will always have production targets that it must meet chosen based on market demand, current product margins, plant configuration, and potential feedstock availability. If there is sufficient market demand, there may also be an incentive or opportunity to maximize production, beyond so‐called nameplate capacity, or what has been achieved in the plant in the past.

To generate revenue, there are two major components that are necessary to achieve that goal.

The first is process technology, whereby raw materials can be converted into final products. In an oil refinery, this might be a fluid catalytic cracking (FCC), alkylation, or a hydrocracker process unit. Sometimes, the process technology might have been developed internally, but more often in today’s world, it’s licensed directly from a process technology company. Process technology includes the process flow, catalysts, high‐level equipment requirements, and process “know‐how” of how to generate products in the real world.

The second part necessary is the actual real‐life equipment itself – pumps, compressors, distillation towers, heat exchangers, instrumentation, etc.

Thus, to drive revenue, process technology, and “know‐how” are required in the right combination with the proper collection of assets. Neither exists independent of one another. Both are necessary. In many ways, that is the thrust of this book and probably more important that you can appreciate at this point. However, as the chapters proceed, it will become more obvious why it matters so much. Also, that statement has major implications for the technology solutions that we will ultimately employ.

1.2.2 Costs

Costs that influence plant profitability originate in many different departments, such as operations, maintenance, engineering, reliability, process control, and HR. These include costs for personnel, raw materials, utilities, capital projects, and spare parts, among others. Since these span such a wide spectrum, to remain focused, we will only consider those that advanced manufacturing technologies could influence or provide guidance upon. There are definitely places where these technologies can help, but many others where they cannot.

For example, these technologies won’t be able to tell you what the right salary range should be for maintenance personnel. However, once we know what that figure is, we can use it to determine what the proper maintenance interval might be to optimize a given task. In addition, once costs are known, the optimization might determine that we can do more frequent maintenance since labor costs are lower. Or it could be just the opposite.

These technologies won’t be able to tell you what the proper spare parts costs should be. However, once we know those figures and normal delivery times, they will be able to determine the optimal quantity of spare parts to hold.

It would also be able to determine whether we should consider purchasing more reliable equipment because the incremental production is significantly greater than the increased asset cost. Using these technologies, we could also determine when it makes economic sense to spend money on an improvement project, and which is the best choice from a pool of potential projects.

These examples all demonstrate how optimizing assets can improve plant profitability. While these focus on how better asset decisions could improve profitability, there are also examples from the process side where advanced manufacturing technologies add value and increase a plant’s bottom line.

For example, in a similar thought process to what has already been presented, advanced manufacturing technologies won’t be able to determine what utility costs should be, but they will likely be able to determine what the optimal utility utilization should be for a given situation that balances increased production against increased utility usage.

1.3 Each Plant Is Unique

You might have noticed a pattern from the previous discussion. That is, these optimization methodologies can’t determine the actual prices of raw materials, spare parts, or capital equipment, but rather how we should react to a certain price environment. That’s important because each plant is unique and operates in its own specific environment.

For example, local labor markets differ between plants. Thus, wages will vary from plant to plant as the availability of different skill sets varies, and the costs for those skill sets within a given geographical region. In addition, while many plants will be using the same large vendors that most of us are familiar with, there will always be other significant local supplier variations that influence a plant’s economics.

The bottom line is that the optimal way to manage plant costs on the US Gulf Coast is almost always going to be totally different from how we manage plants in the Middle East. Moreover, even within a given region, such as the US Gulf Coast, there will be differences among plants literally just down the street from one another.

In other words, everybody’s situation is different. Understanding that nuance is important. Those who can adapt to that reality and exploit its advantages will thrive and benefit. On the other hand, those who can’t take advantage of their unique environment will be less profitable. That is why optimization can play a significant role.

1.4 Plant Optimization Nirvana

True plant optimization should, by definition, consider all variables that influence either plant revenue or costs and allow those variables to be manipulated within the optimization such that we eventually find that set of values whereby profit is maximized. However, given the large number of variables that do influence plant profitability, it hasn’t historically been possible to frame the problem in such a manner since the problem becomes too unwieldy and overwhelming. So, to make the problem easier to solve, we have normally bucketed it into its major categories and solved it independently of one another.

This simplification approach has pluses and minuses. While it makes the problem easier to solve, it also compartmentalizes the problem more. This implies that one group, who is trying to do the right thing, may not know about what another group is doing. This makes it possible, maybe even likely, that they will be working at counter purposes to one another without ever knowing it.

For example, during my career, my observation is that there seems to be an imaginary brick wall between those responsible for the “process” side of the business and those who are focused on the equipment side. They have different worldviews and are not necessarily comfortable in each other’s world. It’s been my impression that it’s like one group speaking French and the other Swahili. They often talk right past one another.

That’s a major problem. Ultimately, it doesn’t benefit either side – neither the equipment people nor the process people, and certainly not the business. We all know that you can’t make a product without the corresponding equipment. On the other hand, we also know that equipment doesn’t exist on its own. It’s there solely to make products.

True plant optimization nirvana would be to meld those two worldviews into one overarching comprehensive view, where you could perform the optimization of all relevant variables from both sides seamlessly in one technology package. Unfortunately, we are not there yet – and won’t be anytime soon. It will take time and there will be intermediate stepping stones on the way to that goal.