Problem Solving Approaches for Maintaining Operational Excellence in Process Plants E-Book

Joseph M. Bonem

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

Seitenzahl: 653

Veröffentlichungsjahr: 2025

Beschreibung

Comprehensive reference providing methods for process engineers and operators to solve challenging process problems and develop working hypotheses for typical process equipment

Problem Solving Approaches for Maintaining Operational Excellence in Process Plants provides a template for achieving an enhanced level of operating efficiency in chemical processing plants and refineries. With examples included throughout to demonstrate key concepts, this book includes methods for formulating working hypotheses for typical process equipment such as pumps, compressors, heat exchangers/furnaces, fractionating towers, and reactors, with additional information on defining and setting metrics and the application of the techniques in unusual situations, as well as the application of these techniques in view of commercially available computer simulation programs.

This book covers topics including initial considerations in problem solving, basic steps in problem solving, and verification of process instrument data, with solved problems showing how techniques can be applied to prime movers, plate processes, kinetically limited processes, and unsteady state problems. This newly revised and updated Second Edition includes coverage of the latest research and developments in the field.

Written by a team of highly qualified industry professionals, Problem Solving Approaches for Maintaining Operational Excellence in Process Plants includes discussion on:

• Lumped parameters as the ideal approach to determine values for key performance indicators (KPIs)
• Theoretical KPIs in comparison to actual operation as a method to find “hidden problems”
• Situations where experience-based solutions are unavailable due to lack of technically trained personnel
• Solutions to problems where a previous analysis has confirmed a need for new equipment or enhanced operating procedures
• Digital twins and their usefulness in predicting yields, executing plant operations, and training operating and technical personnel

Problem Solving Approaches for Maintaining Operational Excellence in Process Plants is an essential reference on the subject for chemical engineers, industrial engineers, process operators, process shift supervisors, chemical engineers with minimal exposure to industrial calculations, and industrial managers who are looking for techniques to improve organization problem solving skills.

Leseprobe

Table of Contents

Cover

Table of Contents

Title Page

Copyright

Preface

1 Focus of Book

1.1 Introduction

1.2 Metrics and KPIs

1.3 Finding Hidden Problems

1.4 Experience-Based Solutions

1.5 Achieving and Maintaining Operational Excellence

2 How to Achieve and Maintain Operational Excellence

2.1 What is Operational Excellence?

2.2 What is the Value of Operational Excellence?

2.3 What are the Limitations to Achieving and Maintaining Operational Excellence?

2.4 Achieving and Maintaining Operational Excellence

3 Initial Considerations

3.1 Introduction

3.2 An Electrical Problem

3.3 A Coffeemaker Problem

3.4 Applications of Concepts to Plant Problem Solving

3.5 Limitations to Problem Solving in Process Plants

4 Successful Plant Problem Solving

4.1 Introduction

4.2 Finding Problems with a Daily Monitoring System

4.3 Solving Problems with a Disciplined Learned Problem-Solving Approach

4.4 Determining the Optimum Technical Depth

4.5 Using the Directionally Correct Hypothesis Approach

5 Examples of Plant Problem Solving

5.1 Industrial Examples

5.2 Polymerization Reactor Example

5.3 Application of the Disciplined Problem-Solving Approach

5.4 Lessons Learned

5.5 Multiple Engineering Disciplines Example

5.6 Application of Disciplined Problem-Solving Approach

5.7 Lessons Learned

5.8 A Logical, Intuitive Approach Fails

5.9 Lessons Learned

Nomenclature

6 Development of Working Hypotheses

6.1 Introduction

6.2 Areas of Technology

6.3 Formulating Hypotheses Via Key Questions

6.4 Beauty of a Simplified Approach

6.5 Verification of Proposed Hypotheses

6.6 One Riot—One Ranger

7 Application to Prime Movers

7.1 Introduction

7.2 Kinetic Systems

7.3 Pump Calculations

7.4 Centrifugal Compressor Calculations

7.5 Displacement Systems

7.6 Displacement Pump Calculations

7.7 Calculations for Positive Displacement Compressors

7.8 Problem-Solving Considerations for Both Systems

7.9 Example Problem 7.1

7.10 Lessons Learned

7.11 Example Problem 7.2

7.12 Lessons Learned

7.13 Example Problem 7.3

7.14 Example Problem 7.4

7.15 Lessons Learned

7.16 Example Problem 7.5

7.17 Example Problem 7.6

7.18 Lessons Learned

7.19 Example Problem 7.7

7.20 Lessons Learned

7.21 Example Problem 7.8

7.22 Lessons Learned

Nomenclature

8 Application to Plate Processes

8.1 Introduction

8.2 Fractionation with Sieve Trays

8.3 Problem-Solving Considerations for Fractionating Towers

8.4 Development of Theoretically Sound Working Hypotheses

8.5 Problem-Solving Reboiler Circuits

8.6 Example Problem 8.1

8.7 Lessons Learned

Nomenclature

9 Application to Kinetically Limited Processes

9.1 Introduction

9.2 Kinetically Limited Models

9.3 Limitations to the Lumped Parameter Approach

9.4 Guidelines for Utilization of this Approach for Plant Problem Solving

9.5 Example Problem 9.1

9.6 Lessons Learned

9.7 Technique for Estimating Polymer–Volatile Equilibrium

9.8 Example Problem 9.2

9.9 Lessons Learned

9.10 Example Problem 9.3

9.11 Lessons Learned

Nomenclature

10 Application to Unsteady State

10.1 Introduction

10.2 Approach to Unsteady State Problem Solving

10.3 Example Problems

10.4 Problem 10.1

10.5 Lessons Learned

10.6 Example Problem 10.2

10.7 Lessons Learned

10.8 Final Words

Nomenclature

11 Application to Other Plant Improvements

11.1 Introduction

11.2 Debottlenecking Reactors

11.3 Real-World Hydraulic Debottleneck

11.4 Debottlenecking By Improving Operating Procedures

11.5 Trust Creating a Disaster—Heat Exchanger Corrosion from Improper Cooling Water System Operation

Nomenclature

12 Applications of Novel Process Engineering Fundamentals to Plant Problem Solving

12.1 Introduction

12.2 Novel Approaches to Plant Problems

12.3 Mostly used Engineering Fundamentals to Solve Plant Problems

12.4 Application of New Engineering Fundamentals to Plant Maintenance Problems (Example Problem 12.1)

12.5 Application of the Disciplined Problem-Solving Approach

12.6 Lessons Learned

12.7 Tank Roof Raising for Maintenance Example

12.8 Application of the Disciplined Problem-Solving Approach

12.9 Lessons Learned

Nomenclature

13 Verification of Process Instrumentation Data

13.1 Introduction

13.2 Data Verification Via Technical Resources

13.3 Flow Measurement

13.4 Temperature Measurement

13.5 Pressure Measurement

13.6 Level Measurement

13.7 Data Verification Via Human Resources

13.8 Example Problems

13.9 Example Problem 13.1

13.10 Lessons Learned

13.11 Example Problem 13.2

13.12 Lessons Learned

13.13 Example Problem 13.3

13.14 Example Problem 13.4

13.15 Lessons Learned

Nomenclature

14 Successful Plant Tests

14.1 Introduction

14.2 Ingredients for Successful Plant Tests

14.3 Pretest Instrument and Laboratory Procedure Evaluation

14.4 Statement of Anticipated Results

14.5 Potential Problem Analysis

14.6 Explanation to Operating Personnel

14.7 Formal Post-Test Evaluation and Documentation

14.8 Examples of Plant Tests

14.9 Example Plant Test 14.1

14.10 Lessons Learned

14.11 Example Plant Test 14.2

14.12 Lessons Learned

14.13 More Complicated Plant Tests

14.14 Other Uses for Plant Tests

14.15 Key Plant Tests Considerations

15 Utilization of Commercially Available Simulation Tools

15.1 Process Simulation and Modern Chemical Engineering

15.2 Breaking Down the Problem

15.3 Green Field Problem Example

15.4 Brown Field Problem Example

15.5 Do Not Gamble with Physical Properties for Simulations

15.6 Examples—Effects of Equation of State on the Required Compression Power and Cooling Duty

15.7 Be Skeptical with your Initial Design Assumptions

15.8 Obtaining a High-Quality Plant Data for your Process Model

15.9 Verifying your Plant Data

15.10 Example—Heat Balance of Heavy Gas Oil Pumparound

15.11 Reconciling your Data

15.12 Example—Hydrocracking Catalyst Testing

15.13 Model Calibration

15.14 Process Simulation as a Training Tool

Nomenclature

16 Putting it Altogether

16.1 Introduction

16.2 Do Not Forget to Use Fundamentals

16.3 Example Problem 16.1: Do Fundamental Processes Developed in the United States Translate to Europe?

16.4 Lessons Learned

16.5 Example Problem 16.2: An Embarrassing Moment

16.6 Lessons Learned

16.7 Example Problem 16.3: Prime Mover Problems are not Always What They Appear To Be

16.8 Lessons Learned

16.9 Example Problem 16.4: The Value of a Potential Problem Analysis

16.10 Lessons Learned

16.11 Example Problem 16.5

16.12 Lessons Learned

Nomenclature

17 A Final Note

Appendix Conversion Factors from English Units to CGS Units

References

Index

End User License Agreement

List of Tables

Chapter 4

Table 4.1 Sources of historical data.

Table 4.2 Suggested trigger points.

Table 4.3 Problem specification example.

Chapter 5

Table 5.1 Hypothesis conclusions.

Table 5.2 Mechanical history.

Table 5.3 Statistical data.

Chapter 7

Table 7.1 Minimum NPSH requirements.

Table 7.2 Compressor data.

Table 7.3 Compressor calculation steps.

Table 7.4 Questions/comments for Problem 7.1.

Table 7.5 Plant test data.

Table 7.6 Performance calculations.

Table 7.7 Questions/comments for Problem 7.2.

Table 7.8 Pump calculations for Problem 7.2.

Table 7.9 Calculation procedure.

Table 7.10 Questions/comments for Problem 7.4.

Table 7.11 Hypothesis comparison.

Table 7.12 Analysis of the compression exponent for Problem 7.4.

Table 7.13 Design and current operations.

Table 7.14 Questions/comments for Problem 7.6.

Table 7.15 Calculation of the polytropic compression exponent.

Table 7.16 Calculation of steam turbine efficiency.

Table 7.17 Compressor horsepower calculations.

Table 7.18 Calculation of BHP delivered to the compressor with increased ste...

Table 7.19 Compressor operating data for Problem 7.7.

Table 7.20 Questions/comments for Problem 7.7.

Table 7.21 Calculations of compressor efficiency.

Table 7.22 Calculated horsepower load versus suction pressure.

Table 7.23 Questions/comments for Problem 7.8.

Table 7.24 Calculation results for normal case and problem case.

Chapter 8

Table 8.1 Evaluation of trays by sampling/data analysis.

Table 8.2 T-1 operating conditions.

Table 8.3 Questions/comments for Problem 8.1.

Table 8.4 Estimated material balance rates.

Table 8.5 Estimated vapor and liquid rates in the top of the tower.

Table 8.6 Top vapor rate based on heat input to the tower bottom.

Table 8.7 Vapor rates calculated from material balances and heat balances.

Chapter 9

Table 9.1 Operating conditions with the old and new catalyst.

Table 9.2 Questions/comments for Problem 9.1.

Table 9.3 Results of numerical integration.

Table 9.4 Furnace data.

Table 9.5 Questions/comments for Problem 9-2.

Table 9.6 Questions/comments for Problem 9.3.

Table 9.7 Data for evaluation.

Chapter 10

Table 10.1 Questions/comments for Problem 10.1.

Table 10.2 Questions/comments for example Problem 10–2.

Chapter 11

Table 11.1 Examples of equivalent lengths for piping components based on maj...

Table 11.2 Test run data for a new preheater design.

Table 11.3 Hydraulic calculation results based on the valve performance char...

Table 11.4 Test-run data and calculation results for the wash water system r...

Table 11.5 Typical operating steps for automatic sand/media filters.

Table 11.6 Correction factors for typical filter media at various temperatur...

Table 11.7 A typical control parameter for the cooling water system.

Table 11.8 Minimum tube velocities for cooling water used by different compa...

Chapter 12

Table 12.1 Questions/comments for Problem 12.1.

Table 12.2 Questions/comments for Problem 12.2.

Chapter 13

Table 13.1 Typical sources of primary element errors.

Table 13.2 Data verification via technical resources.

Table 13.3 External flow measuring devices.

Table 13.4 Questions/comments for Problem 13.1.

Table 13.5 Questions/comments for Problem 13.2.

Table 13.6 Specification and actual data.

Table 13.7 Summary of test basis.

Table 13.8 Test run results.

Table 13.9 Questions/comments for Problem 13.4.

Chapter 15

Table 15.1 Examples of process simulators and their applications.

Table 15.2 Typical required process information.

Table 15.3 Feed gas composition—dry basis.

Table 15.4 Final summary of the gas injection compression unit design.

Table 15.5 Feed gas composition—wet basis.

Table 15.6 Different compression power and cooling load obtained from differ...

Table 15.7 Measured process data from a crude preheat exchanger.

Table 15.8 A raw data set from a hydrocracking catalyst test.

Table 15.9 Raw mass balance summary of a hydrocracking catalyst testing.

Table 15.10 Differences in chemical hydrogen consumption, raw versus reconci...

Table 15.11 Typical column efficiencies for crude oil fractionation.

Table 15.12 Calibration results of a single-stage hydrocracker model.

Chapter 16

Table 16.1 Questions/comments for Problem 16.1.

Table 16.2 Chloride balances.

Table 16.3 Steam pressure measurements.

Table 16.4 Questions/comments for Problem 16.2.

Table 16.5 Steam flow and pressure drops.

Table 16.6 Blower capacity tests.

Table 16.7 Questions/comments for Problem 16.3.

Table 16.8

Calculated efficiencies

. for test runs.

Table 16.9 Calculation results.

Table 16.10 Questions/comments for Problem 16.4.

Table 16.11 Operating parameters before and after test runs.

Table 16.12 Calculated parameters before and after test runs.

Table 16.13 Questions/comments for Problem 16.5.

List of Illustrations

Chapter 2

Figure 2.1 Operational excellence program with feedback loop.

Chapter 3

Figure 3.1 Coffee pot schematic.

Figure 3.2 Basket comparisons.

Figure 3.3 An example of improper problem solving.

Figure 3.4 An example of improper level design.

Chapter 4

Figure 4.1 Essential variable (reaction kinetics) percent of theory versus t...

Figure 4.2 Optimum technical depth schematic.

Figure 4.3 Probable and required confidence levels.

Figure 4.4 Detailed study approach compared to multiple attempts.

Chapter 5

Figure 5.1 Reactor schematic.

Figure 5.2 Rotary filter schematic.

Figure 5.3 Hypothetical baffle deformation.

Chapter 7

Figure 7.1 Flow path in centrifugal equipment. 1. Fluid enters the eye of th...

Figure 7.2 Characteristic centrifugal pump or compressor curve.

Figure 7.3 Typical NPSH required for the centrifugal pump.

Figure 7.4 Reciprocating flow path in double-acting equipment.

Figure 7.5 Clearance pocket reciprocating compressor.

Figure 7.6 Positive displacement compressor load point.

Figure 7.7 Compressor performance curve.

Figure 7.8 Compressor efficiency versus time.

Figure 7.9 Pump curve for Problem 7.2.

Figure 7.10 Schematic flow for Problem 7.2.

Figure 7.11 Steam turbine efficiency versus time.

Figure 7.12 Schematic flow for Problem 7.7.

Figure 7.13 Schematic flow.

Figure 7.14 Blower flow versus time.

Chapter 8

Figure 8.1 Typical fractionation tray.

Figure 8.2 Typical tray stability diagram.

Figure 8.3 Examples of tray problems detectable by X-rays.

Figure 8.4 Thermosiphon reboiler operating principles.

Figure 8.5 Schematic flow for Problem 8.1.

Figure 8.6 Tray stability diagram for Problem 8.1.

Chapter 9

Figure 9.1 Schematic sketch of the dryer.

Figure 9.2 Dryer calculation segment.

Figure 9.3

K

value versus time.

Figure 9.4 Furnace schematic.

Figure 9.5 Percent LEL versus time.

Chapter 10

Figure 10.1 Reactor temperature versus time.

Figure 10.2 Simulation of actual change.

Figure 10.3 Projected steady-state reaction rate versus CO

2

concentration.

Chapter 11

Figure 11.1 Temperature profile with existing and new catalyst.

Figure 11.2 (a) A tube bundle heavily fouled by asphalt and coke. (b) The in...

Figure 11.3 A pathway to maximizing success rate and minimizing CAPEX.

Figure 11.4 Typical inherent valve characteristics.

Figure 11.5 The scheme of the existing diesel draw-off system with the 3rd p...

Figure 11.6 Graphical illustrations of...

Figure 11.7 Feed system with its new feed preheater.

Figure 11.8 Graphical determinations of

C

v,base

and %Travel

revamp

: (a) Valve...

Figure 11.9 Illustration of a wash water injection system of a heavy oil hyd...

Figure 11.10 Graphical determinations of %Travel

revamp

for a full-bore 2-in....

Figure 11.11 Illustration of the crude distillation overhead system.

Figure 11.12 Unloading compressor by changing suction pressure.

Figure 11.13 A simplified raw water treatment process.

Figure 11.14 Mixed-media filter and simplified loading diagram.

Figure 11.15 A typical sand/media filter setup.

Figure 11.16 Water turbidity profiles during backwash operation.

Figure 11.17 Plugged filter media.

Figure 11.18 Determining Δ

H

design

.

Figure 11.19 A typical backwash system.

Figure 11.20 A recommended tuning strategy for varying water level.

Figure 11.21 Blocked distributor.

Figure 11.22 Turbidity and total iron (T-iron) during the pre-commissioning ...

Figure 11.23 Fouling and under deposit corrosion of the heat exchanger’s tub...

Figure 11.24 Under deposit corrosion in heat exchanger tubes.

Figure 11.25 Corrosion rate versus flow velocity Mokhtar et al. (2018).

Figure 11.26 Spikes in turbidity and T-iron when 3 pumps were in service.

Chapter 12

Figure 12.1 Storage tanks.

Figure 12.2 Tank relocation.

Figure 12.3 Schematic of physics involved.

Chapter 13

Figure 13.1 Potential flowmeter errors.

Figure 13.2 Estimating flow by concentrations.

Figure 13.3 Ethylene surge/knockout drum.

Figure 13.4 Fuel gas balance.

Figure 13.5 Impact of fuel gas density on calculated fue gas consumption.

Chapter 14

Figure 14.1 Reactor/overhead condenser schematic.

Chapter 15

Figure 15.1 Definition of (a) green field problem (b) brown field problem.

Figure 15.2 Process flow diagram for gas injection compression unit.

Figure 15.3 Centrifugal compressor performance curve.

Figure 15.4 A feedback loop of process and equipment design.

Figure 15.5 Feed synthesis from three product streams.

Figure 15.6 Crude oil distillation system.

Figure 15.7 Data reconciliation process.

Figure 15.8 A typical process modeling workflow.

Figure 15.9 A hydroprocessing model consisting of reaction and separation se...

Figure 15.10 Positions of multi-points thermocouples in a hydrocracking reac...

Chapter 16

Figure 16.1 TEG system.

Figure 16.2 Actual and projected Cl buildup.

Figure 16.3 Schematic of three-stage steam ejector.

Figure 16.4 Pressure drop versus flow rate squared.

Figure 16.5 Ethylene refrigeration schematic.

Figure 16.6 Blower capacity curve.

Figure 16.7 Simplified sketch of the fractionation system.

Figure 16.8 Venturi sketch.

Figure 16.9 Simplified sketch of the condensing turbine surface condenser sy...

Figure 16.10 The pressure at the surface condenser started to gradually incr...

Figure 16.11 The problem statement.

Figure 16.12 The dissolved oxygen analysis.

Guide

Cover

Table of Contents

Title Page

Copyright

Preface

Begin Reading

Appendix Conversion Factors from English Units to CGS Units

References

Index

End User License Agreement

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Problem Solving Approaches for Maintaining Operational Excellence in Process Plants

Joseph M. Bonem

Engineering Consultant

ExxonMobil Chemicals, US

Nattapong Pongboot

Project Leader

Refinery Catalyst Testing Group at Avantium

Wiroon Tanthapanichakoon

Engineering Consultant and CEO

Global R&D Co. Ltd., Thailand

Second Edition

Copyright © 2025 by the American Institute of Chemical Engineers, Inc. All rights reserved.

A Joint Publication of the American Institute of Chemical Engineers and John Wiley & Sons, Inc.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data Applied for:

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Cover Design: WileyCover Image: © Joseph M. Bonem

Preface

Whenever one starts the process of writing a book, consideration must be given to the important question—Does this book create any value added for society?

This book answers that question with a resounding YES. The book addresses the question of how can the Operational Excellence of process plants (refineries, gas plants, and chemical plants) be improved and maintained at this higher level of excellence. Mastering the concepts in this book will improve the efficiency, profitability, and safety of process plants. While the concepts are focused on process plants, the concepts are general enough that they can be applied to almost any industrial operation. In order to show how the general concepts are put into detailed application, the examples are real-life illustrations from process plants.

This book makes frequent use of an earlier book by the editor who is also one of the authors of this book. The previous book was entitled PROCESS ENGINEERING PROBLEM SOLVING: Avoiding “The Problem Went Away But It Came Back” Syndrome. The previous book and this book are focused on a calculation-based approach to problem solving. This approach uses first principles of engineering and is based on fundamental concepts. However, it also recognizes the time pressures that most operational problems create. As such, the fundamental approach utilizes relatively simple first principles that are theoretically correct. The previous book has been expanded by defining what “Operational Excellence” means, by indicating the incentive for achieving this level of performance, by summarizing an approach to achieving Operational Excellence, by providing insight into the use of available software programs, and by including additional example problems. The focus remains on using a structured methodical calculation-based problem-solving approach in order to achieve and maintain Operational Excellence.

While this book is not a management book, it does make the assumption that management has set as a priority improving/maintaining Operational Excellence and that management will provide the necessary resources to implement the concepts recommended in this book. These additional resources will increase the expense side of the P/L, BUT they will more than pay for themselves by reduced operating costs, increased production capability (revenue), and increased safety.

The book can be thought about as four sections. The initial section deals with a general approach to problem solving that can be used in all areas of life (work, home, hobbies, or volunteer activities). The second section deals with application of this approach to process operating plants, the third section deals with successful plant tests including confirmation of data, and the last section covers concluding comments.

In preparing any book like this, the need to use personal pronouns is almost mandatory. The use of personal pronouns helps to make the book more readable and minimizes the more complex ways of expression. Even though I have worked with very talented female engineers, operators, and mechanics, it was my choice to use the personal pronoun “he” to cover all gender possibilities.

September 12, 2024               

Joe Bonem

Ft. Worth Texas

1Focus of Book

1.1 Introduction

The book is focused on what technical concepts and calculation-based approaches are required to improve and maintain Operational Excellence. The types of problems that are envisioned are either chronic operating problems or operating problems for which the problem solver has no experience-based solution. The example problems discussed are for the most part real-life problems that actually occurred, and solutions were those that were developed from a careful analysis and calculations. These problems and solution concepts are enhanced by a limited discussion on setting metrics and uncovering hidden problems.

While if experience-based solutions are available, they can be of great value. However, not every problem encountered has been experienced before by the problem solver, and as such these problems are rarely solved with experience-based solutions. In fact, the attempt to solve them based on extrapolated experience often results in delaying the true solution. For example, an impurity problem associated with ethylene used in a polyethylene process cannot be extrapolated to a similar impurity problem associated with propylene used in a polypropylene process. While this seems obvious, time pressures associated with poor operating performance often create the management decree to “Do something even if it is wrong.”

In reading Sir Arthur Conan Doyle’s late 1880s fiction about the famous detective Sherlock Holmes and his companion Dr. Watson, I see a parallel to current industrial problem solving techniques. Watson often quickly jumped to conclusions based on a few facts and his experience. On the other hand, Holmes took a thoughtful and methodical approach and used all the available data to obtain the right solution. Holmes’ approach being methodical appeared to be too slow, but it avoided the endless detours caused by jumping to conclusions. A quote from Sherlock in the novel The Valley of Fear illustrates this well—“The temptation to form premature theories upon insufficient data is the bane of our profession.” For the complicated or chronic process problem, the successful problem-solver is a modern-day Sherlock Holmes.

1.2 Metrics and KPIs

Before one decides that a problem exists, there must be a deviation from some sort of standard or metric. These metrics are often referred to as key performance indicators (KPIs). This terminology KPIs will be used in the book to refer to the anticipated value of a key variable. An Operational Excellence deviation is one that is significantly different than the anticipated value of a key operating variable or lumped parameter. The key variable or lumped parameter can be as simple as pressure of a key utility or as complicated as a reaction rate constant. Before one determines that a key operating parameter has significantly deviated from the target value, the target value must be known. The reported analysis of blood of a medical patient uses this concept well when the technician reports actual values and the expected range.

The first step in problem-solving is to determine values for the KPIs. The approach of employing a lumped parameter should be utilized. For example, the lumped parameter for a heat exchanger is the well-known heat transfer coefficient. This includes temperatures of both hot and cold sides, flow rates of both hot and cold materials, and thermal characteristics of the materials in a single value. A similar approach could be used for a reaction rate constant. The calculation of this lumped parameter will provide a value of this KPI at any point in time, which can be compared to the theoretical value. In the case of a heat transfer coefficient, the theoretical value is the design coefficient. This comparison can then be expressed as a percent of the theoretical value. A similar approach can be used for equipment such as pumps, where the pump curve serves as the theoretical data source. For example, a key pump operating at the design flow rate, but below the theoretical head, could be flagged as an operating problem using the design head as the theoretical KPI.

Regardless of the terminology (metrics or KPIs), the most important thing is how the theoretical value is determined. It can be determined by design calculations such as heat/material balances, equipment specifications, or pilot plant/laboratory results. They can also be set by actual plant performance. It is often true that process plants can be operated at 10% or more above design capacity for extended periods of time. This capability should be used to determine new KPIs rather than using design calculations.

These KPIs must be theoretically obtainable, achievable (with often significant effort), and sustainable. There is often a temptation to set the KPIs too low so that they can be easily obtained. This temptation must be avoided if the goal is to achieve Operational Excellence. The successful military mission or airplane journey represent examples that are theoretically possible, achievable, and sustainable.

Now considering this terminology in more detail:

Theoretically obtainable KPIs are those set by the physical limits of the operating unit. These are metrics that would be achieved if there were no limits caused by failures, errors, or uncontrollable events.

Achievable KPIs are those that can be achieved with existing equipment and available raw materials.

Sustainable KPIs are those that can be achieved every day of the year. Again, referring to the airline industry and assuming that one is traveling from Tokyo to DFW (Dalllas-FT. Worth International Airport) with an

exceptionally

strong tail wind, the flight may be as much as an hour shorter than planned. However, this does not mean this shorter flight time is sustainable day after day.

KPIs can include things such as operating rates, reaction kinetics/rate constants, heat transfer in critical exchangers, tower performance, and/or dryer performance. To achieve improved Operational Excellence, these KPIs should be established during good plant operations. The approach to establishing them during good operation frequently at greater than design capacity will often create concerns about the number of chronic problems that are uncovered. This uncovering of what are referred to as hidden problems should be considered a valuable source of potential operational improvements. The other extreme of setting metrics so low that they are easily achieved is not the focus of improved Operational Excellence. While setting KPIs too low does reduce the number of perceived operational problems, it does limit the KPIs or however metrics are measured to levels that can be easily achieved. A quote from Frederick Bonfils, the founder of The Denver Post, is certainly appropriate for the manufacturing industry—“There is no hope for the satisfied man.” It is only by setting high standards and tight KPIs that Operational Excellence can be improved.

1.3 Finding Hidden Problems

Comparing these theoretical KPIs to actual operation is a method for finding “hidden problems”. Finding these problems is one of the keys to improving Operational Excellence. These hidden problems exist for a multitude of reasons. They can be understood as operating problems that have become accepted and hence ignored as “just the way that it is.” Since these problems are accepted, the necessary analysis to fully understand and to eliminate the problem is not done. Some typical areas are as follows:

It is often difficult to convince a process control engineer to make a programming change in a process that is computer-controlled. This results in less than excellent adaptive solutions.

In processes that are not computer-controlled, operators at shift change often “tweak” set points to their personal preferences. While the process will operate at either set point, one of the set points will be less than optimum.

Often a process engineer does not understand mechanical equipment such as pumps and compressors. This lack of understanding limits the ability to make any potential recommended changes that might improve process operations. The process will operate, but not at an optimum.

There is confusion between using standard deviations for control and using them for defining if a situation is likely a real problem. The well-known ±3σ has become a benchmark for process control. However, for operating problems, problem-solving should begin before that point. This concept along with the risks is described in a later chapter.

These hidden problems or ones that are and have been blatantly in the open for either short or extended periods of time are referred to as chronic problems, and solving them is what the book covers. These operating problems are almost always related to some aspect of technology. This technology could be equipment technology or process technology. Unfortunately, attempts to solve these problems are often based on operating experience rather than an understanding and analysis of the problem. They are defined as chronic because these attempts have failed to resolve the problem. Many of the example problems included in this book fit the definition of chronic operating problems. They were long-standing problems with many trial-and-error attempted solutions. The attempted solutions were based on limited experiences, intuition, or guesses. The time period to attempt these multiple solutions almost always exceeds the time that would have been required for a more in-depth analysis of the problem and equipment involved at an early stage. For unsuccessful solutions that require new equipment, the pain associated with the failure is especially damaging.

1.4 Experience-Based Solutions

Operating problems where there is no experience-based solution are an increasing significant problem in today’s environment. This is due to several factors given as follows:

In developed countries, there is a loss of experience in the technical, operational, and mechanical areas. Thus, even in a process that has been in operation for many years, these chronic problems have no apparent solutions because of this loss.

This loss of experience is made even more noticeable in developed countries where technical staff have been reduced with the belief that experienced operation and mechanical personnel can handle all situations.

Developing countries have similar problems compounded by the fact that they are just now developing experience.

Educational institutions in both developed and developing countries are continuing to emphasize research and the utilization of computer algorithms as opposed to the understanding of calculation-based problem solving or design.

In both types of countries, there is an increased movement of technically trained personnel into business, management, marketing, and supply chain activities.

Looking for proof that these factors are negatively influencing Operational Excellence, consideration could be given to increased Force Majeures, major fires/safety incidents, supply chain disruptions, and derating of industrial plants.

1.5 Achieving and Maintaining Operational Excellence

While this book discusses the setting of operating metrics and finding hidden problems, the main focus is a problem-solving technique for maintaining plant Operational Excellence along with real life examples. A five-step procedure including theoretical-based calculations is proposed. The calculations can be described as fundamental and using first principles. They also recognize the urgent need that management often feels when the problems impact the bottom line. These pressures are often extended to those working the problem. Thus, the calculations are those that can be done relatively quickly while still remaining fundamentally accurate. The example problems given in the book are real-life problems that were solved by the techniques, and the calculations are shown.

The authors of the book have over 100 years of industrial experience and have seen how an emphasis on setting good operating metrics and having an accurate and fast response problem-solving technique can improve Operational Excellence, which leads to improved profitability. As indicated in the preface, the content of the book can be thought of as having four sections. The initial section deals with a general approach to problem-solving that can be used in all areas of life (work, home, hobbies, or volunteer activities). The second section deals with the application of this approach to process operating plants, the third section deals with successful plant tests including confirmation of data, and the last section covers concluding comments.

The initial section (preface through Chapter 4) of the book includes items such as

Operational Excellence—definition, value, limitations, and achieving/maintaining this level of performance.

Components of a fast response problem-solving approach.

The five-step problem-solving technique that works in process plants.

The second section (Chapters 5 through 14) contains examples of application of the five-step approach to process plants and process equipment, development of working hypotheses, and verification of plant instrument data. The second section also deals with solutions to problems where a previous analysis has confirmed a need for new equipment or enhanced operating procedures. This section provides comments on the utilization of plant tests as a mechanism to test a working hypothesis (Step 4 of the five-step approach).

The third section (Chapter 15) deals with simulation tools and the building of “digital twins” to simulate plant operations. An accurate digital twin can be used for predicting yields and plant operations as well as for training operating and technical personnel.

The last section (Chapter 16) is a summary along with some additional more complicated examples of the application of the five-step approach. A final note is provided in Chapter 17.

2How to Achieve and Maintain Operational Excellence

2.1 What is Operational Excellence?

While the focus of this book is maintaining Operational Excellence, it is necessary to know both what Operational Excellence is and how to achieve that level of operation. Operational Excellence is a well-known phrase to those involved in many aspects of the worldwide society. In one form or another, it seems to be present from the academic world to heavy industry. While there are many books focused on achieving Operational Excellence by good management practices, there are few that discuss what technical approaches are required to both achieve and maintain this level of operations.

In order to provide the necessary background, this chapter provides a brief summary of the aspects of Operational Excellence as follows:

The meaning of the term.

The value of achieving this level of operations.

Current limitations to achieving and maintaining Operational Excellence.

As a starting point, what is meant by the term “Operational Excellence”? In understanding what is meant by this term, one might use dictionary terminology, cliches, organization goals, or mission statements. Some examples of these different ways to define the term are as follows:

Dictionary

: The idea is that in a company with Operational Excellence, operations would be superior to others operating the same facilities or doing the same work.

Cliches

: Some of the more common ones are – “Be all that you can be,” “Don’t leave anything on the court or track,” “Give it all you got,” or “If a job is worth doing at all it is worth doing well.”

Organization goals

: “Be the highest quality producer” or “Reduce utility consumption by 10%.”

Mission statements

: These are often organization goals with fancy wording.

Regardless of lofty ambitions expressed in these examples, they all fail to achieve a satisfactory level of Operational Excellence because they are not specific enough and do not include measurable KPIs, or in the case of organizational goals such as increased profits and/or reduced costs, they do not provide specific tactics to allow this goal to be achieved. Well-defined KPIs on a select number of key variables will provide these tactics for strategic organization goals.

While each individual company will have to define exactly what these key variables are, this book takes the approach that any deviation greater than ±5% from these selected KPIs should be investigated as a means to improve Operational Excellence. A positive deviation should be considered. For example, a calculated heat transfer coefficient that shows greater than the design value may indicate that a critical exchanger has more capacity than design and that the process could be operated at a higher rate. Conversely, the apparent higher coefficient might be due to an instrument error. Either way, additional evaluation would be of value.

2.2 What is the Value of Operational Excellence?

There is no doubt that developing and communicating these KPIs will require a significant amount of time and effort. This leads to the logical question of what is the incentive for developing these target KPIs. Developing these KPIs with a method described in Chapter 1 is key to improved operations. The value of Operational Excellence can be measured in terms of improved economics, enhanced product quality, improved safety, and greater employee morale and engagement.

Industry/business is concerned about making and/or increasing profits. This can be achieved by increasing net revenue and/or decreasing costs. Profits can always be increased by raising prices if all other things are constant. Since this book is primarily associated with operations, only increased sales or decreased costs will be considered a means to improve profits. Sales can be increased by either increased production or increased market share.

When considering revenue, the focus is on net revenue. This can be defined as the difference in increased revenue less increased operating costs associated with an increase in production. The marginal increase in production associated with Operational Excellence will generate more profit on a unit basis than the average profit on the average production. For example, a typical polyolefin plant might be generating a profit of 10 cents/pound based on a sales price of 50 cents/pound. By difference, the total operating cost is 40 cents/pound. Of this operating cost, 20% is fixed and thus does not increase if production is increased. The remainder of the costs are variable and consist of feed, chemicals, catalysts, and utilities. Based on these values, the profit on the marginal increased production is 18 cents/pound or 80% higher than the average cost prior to production increase.

The source of this increased production can be due to a scenario where the operating plant is running at an alleged maximum capacity. It can also be due to enhanced product quality, which allows a company to obtain a higher market share during times when production is limited by demand. In either of these scenarios, Operational Excellence techniques can be utilized to increase production and net revenues. It is highly likely that the effort required to improve operations will be more than justified by the increase in profits (net revenue).

In the case of reduced operating costs that are associated with Operational Excellence, the incentive is not as easy to generalize. The goal of reducing operating costs can be associated with a reduction in demand. In this case, there is a desire to keep the variable cost (cents/pound) at reduced production as close as possible to that at full rates. In the case of a polyolefin plant where 80% of the costs are variable, there are at least two major limits to reducing costs as production is reduced. These are as follows:

The plant is often not designed for a significant turndown.

Plant operators find it easier to operate at conditions that are not optimum. An illustration from domestic situations is that it is easier to leave the lights on than turn them off and have to turn them on again.

As an example of the first limit described above, consider the cycle gas compressor of a gas-phase polyolefin plant, and assume that this compressor is supplied with a fixed-speed electric motor. In this process, the exothermic heat of reaction is removed by the circulating gas. With this type of compressor design, there is a minimum flow rate that must be maintained to avoid the surging of the compressor. This limitation can be mitigated by the utilization of a variable-speed motor or steam turbine which will reduce the surge point of the compressor. However, while this design may mitigate the problem, there are other limitations associated with the gas rate. At reduced production rates, the minimum gas rate will likely be set by the fluidization rate requirements or the necessary superficial velocity to achieve good individual particle heat removal rather than overall heat load on the reactor.

In addition to the case of reduced production due to demand limit, there is a case where at full rates, it is desirable to reduce variable costs for closer approach to a theoretical limit. This will generally be a situation where operating parameters have drifted away from design values. If engineering calculations confirm the validity of the design values, then the second limitations described above are the limiting factor. This situation is usually driven by the operating personnel’s negative experience such as gas-phase reactor fouling as gas rates were reduced. This limitation can be overcome by careful technical analysis and strong management. While this example is highly simplified, it illustrates the difficulty in achieving an operational goal of maintaining a constant variable cost as production rates are decreased. It will always be necessary to consider both engineering details and possible previous experience in order to be successful in this cost reduction goal.

There are real, but somewhat less tangible benefits associated with Operational Excellence. As indicated earlier, these intangible benefits are in the areas of improved product quality, safety, and greater employee job satisfaction and engagement. Product quality can be both a tangible and intangible benefit. Thus, it shows up in both areas. These areas are intertwined and are all related to the smoother operation of a process that has a high degree of Operational Excellence. This interdependence of these attributes is shown in Figure 2.1.

As indicated in this figure, Operational Excellence results in more steady-state operations, minimal shutdowns, minimal rate reductions due to process problems, and minimal upsets. In addition, this improved operation reduces the temptation to take on risks that might be associated with undesirable plant shutdowns. These risks will eventually lead to a safety incident. The figure also includes a feedback loop where improvements in product quality, safety, and job performance also improve Operational Excellence. If the impact of improvements, whether tangible or somewhat intangible, is communicated adequately to the workforce, this feedback loop will be very strong. Conversely, if the communication is not done or not done effectively, this feedback loop will be weak or nonexistent. This feedback loop is present because there is almost always a sense of pride in the operations, mechanical, and technical workforce when the process they are responsible for is operating well. This leads to a desire to make even more improvements.

Figure 2.1 Operational excellence program with feedback loop.

Conversely, poor operations or lack of good communications can lead to an attitude of nonchalance and poor job performance. Verbal exchanges can often give a clue that this is occurring. With comments such as “management doesn’t care about this process,” “No one pays any attention to what I say,” or “This place is going downhill at a fast pace.” A happy engaged worker that feels safe can be a positive influence on Operational Excellence.

2.3 What are the Limitations to Achieving and Maintaining Operational Excellence?

Since Operational Excellence does not occur automatically, an analysis of the limitations to obtaining this excellence will be of value. While each company has somewhat different limitations, there are four general areas that tend to be present in all areas of the world whether the areas can be classified as “developed nations” or “developing nations.” These are as follows:

Limited experience

Changing academic focus

Increasing complexity and size of industrial processes

Emphasis on cost reduction

These areas are discussed in more detail in Chapter 3.

2.4 Achieving and Maintaining Operational Excellence

Given the industrial environment that many operating facilities are surrounded by today, what are the steps to achieve and maintain Operational Excellence. These steps must touch on both technical and management issues. Since this book deals primarily with technical issues, the need for strong management is only briefly considered.

The division between technical and management varies from company to company. One often used division is that the technical workforce is responsible for developing operating procedures/directives and solving both chronic and complicated operating problems. On the other hand, management is responsible for the assuring that operating directives/procedures are both adequately developed and followed.

As discussed in Chapter 1, the means for measuring Operational Excellence is carefully developing, following, and reacting to KPIs. The initial step in achieving Operational Excellence is to develop these metrics for the specific process. While KPIs measure how close a given process is to Operational Excellence, they do not provide a means to achieve this desired level of performance. To achieve the target KPIs requires well-defined operating procedures and operating directives. It is also important to provide sufficient technical detail so that operating and mechanical personnel can understand the importance of the directives and procedures. The differentiation between operating directives and procedures is that operating directives are used to determine how a process should operate at steady state. Operating procedures include both what steps are required to reach steady state (start-up procedures, for example), procedures to conduct batch operations, and procedures associated with interim operations (such as replacing the catalyst in reactors). These procedures and directives should be consistent with the KPIs discussed earlier.

Even with well-developed and established directives and procedures that are being well followed, there will always be external events that create problems that must be solved. Some examples of these external factors are as follows:

Changes in supplier feedstock

: While most process plants have feedstock specifications, sometimes, a change of suppliers or a change in the supplier’s operations will introduce an impurity that is not specified.

Changes in climatic conditions

: A sudden change in ambient temperature may introduce a change in gas density in a process drum. This may cause an occasional process upset. While the frequency is minimal, the result maybe a chronic operating problem.

The marketing needs to operate a process for an extended period without a periodic downtime to inspect equipment may create the failure of a critical piece of equipment.

The expansion of a process fails to deliver as promised because the limitation of a minor piece of equipment was not considered by the contract engineering company.

These are examples that the techniques described in this book would have prevented and/or quickly eliminated if they did occur.

Since this book is focused on technical problem solving, a reasonable question is what attributes are required to achieve excellence in this goal and thus assist in maintaining Operational Excellence. The following attributes are mandatory to obtain excellence in the area of problem solving:

Technical personnel should have a good understanding of the process and process equipment before beginning problem solving. This likely means that initially, the problem solver will spend time learning the process and/or a specific piece of equipment.

The problem solver will not only react to problems brought to them but also be proactive and seek to find potential problems and hidden problems. The problem solver does this daily or more frequently monitoring KPIs. The problem solver will know and understand the KPIs and how to monitor them.

In solving problems, technical personnel will normally be recommending new directives/procedures or new equipment. These recommendations should be based on a firm technical basis including a potential problem analysis associated with proposed changes or new equipment. This requirement is illustrated by the cliché—do not create another problem while solving the current problem.

The problem-solver will assess the problem in a calm and deliberate fashion. This is in contrast with problem-solving sessions where the loudest expression is the chosen path.

There will always be time pressures associated with problem solving. It is important to establish a good compromise between expediency and accuracy.

A thoughtful examination of these attributes will lead to the conclusion that while meetings are necessary to table problems, develop understanding of problems, and to set priorities, the real problem solving of chronic and complicated problems occurs as more understanding is developed and as thoughtful calculations or careful examination of the data is done.

As indicated in Chapter 1, the remaining chapters are dedicated to the techniques of problem solving in process plants. Besides the techniques, examples are given to illustrate the use of these techniques.

3Initial Considerations

3.1 Introduction

Chapters 1 and 2 discussed the generalized approach to achieving and maintaining Operational Excellence including the existence of a fast response problem-solving approach. This chapter and subsequent chapters focus on this fast response approach. These chapters cover questions such as—What is this approach? What are the limitations? And are there real-life examples of this approach? This chapter specifically focuses on limitations to implementing this fast response problem-solving approach and the guidelines for a successful problem-solving organization. The purpose of this chapter is as follows:

To illustrate and discuss the importance of fully understanding the problem.

To stress the importance of a theoretical first-principles approach.

To provide examples of real-world inadequate problem solving.

To elaborate on the limitations to successful plant problem solving discussed in

Chapter 2

.

Problem solving is an area that is found throughout all activities of daily life. It tends to take place in two mindset modes. There is the intuitive or instinctive reactionary mode, which has also been called “gut feel.” Then, there is the methodical reasoning approach, which is usually based on theoretical considerations and almost always on calculations.

Either of these approaches has its place in real-world problem-solving activities. The intuitive reactionary person will respond much faster to a problem. Their response is usually based on experience. That is, the problem solver has seen the same thing before or something very similar and remembers what the problem solution was. However, if what is occurring is a new problem or is somewhat different, this approach may well lead to an incorrect problem solution. The methodical reasoning person will not be able to react to problems quickly but will usually obtain the correct problem solution for complicated problems much faster than the intuitive reactionary person who must develop several aborted “gut feel” solutions. In the real world, we are all a mixture of these types of problem-solving personalities. The effective problem solver will know when to choose and which mode to use for any situation that the problem solver confronts.

An example of how two people with these different mindsets will react can be found in the most unlikely places. For example, on a golf course, the cry of “Fore” will illicit different responses. The person responding based on intuition or instinct will immediately cover his head and crouch. This will reduce the probably that the errant golf ball hits a sensitive body part. The person responding based on methodical reasoning will begin to assess where the cry came from, where the ball might be coming from, and reach a conclusion as to where it might land. Obviously, in this case, reacting based on intuition or instinct is a far superior mode of operating. There could be many more examples from the sports world where reacting in an intuitive fashion yields far superior results than reacting in a methodical reasoning manner. However, essentially, all of these examples will be experience-based. People who are reacting successfully in an intuitive mode know what to do because they have experienced the same or very similar situations.

Similar things happen in industrial problem solving. Experienced people (engineers, mechanics, or operators) react instinctively because they have experienced similar events. This instinctive approach serves people well in handling emergency situations or making decisions during a start-up. As a rule, the person who tends to respond based on methodical reasoning and calculations rarely can react fast enough to be of assistance in an emergency or if quick action is required in a start-up situation. The exception to this rule is the engineer who has designed the plant and has gone through calculations to understand what will happen in an emergency or start-up. In effect, he has gained the experience through calculations, as opposed to actual experience.

The experience necessary to conduct problem solving in the real world does not always exist. In addition, while the need for a quick response when solving industrial problems is real, there is not always an emergency or need to take immediate action. Thus, the methodical reasoning approach is often the desirable mode of operating. The three components of this methodical reasoning approach are as follows:

A systematic step-by-step procedure. This includes the three essential problem-solving skills (daily monitoring system, disciplined problem-solving approach, and determining optimum technical depth).

A good understanding of how the equipment involved works.

A good understanding of the specific technology involved.

Before discussing problem solving in industrial facilities, two examples from everyday life are discussed. It often aids learning to discuss things that are outside the scope of the original thrust of the teaching. The two examples from everyday life discussed below will be helpful in understanding the difference between intuitive problem solving and those based on methodical reasoning.

3.2 An Electrical Problem