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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
A Real-Time Approach to Distillation Process Control A practical and hands-on discussion of modern distillation control In A Real-time Approach to Distillation Process Control, a team of distinguished researchers and industrial practitioners delivers a practical text combining hands-on and active learning using process simulation with discussions of the fundamental knowledge and tools required to apply modern distillation control principles. The book offers a balanced, real-time approach integrated with practical insights. It includes many exercises designed to be simulator agnostic that can be performed on the process simulator locally available to the reader. Readers will discover explorations of topics including distillation control hardware, distillation composition control, refinery versus chemical plant distillation control, distillation control tuning, advanced regulatory control, and more. They'll also find: * A thorough introduction to distillation fundamentals, as well as basic and advanced modern controls from a practical point of view * Comprehensive explorations of known base controls combined with modern control practices * Practical discussions of hands-on modelling and simulation exercises, allowing the reader to design and tune controls on a distillation column * Fulsome treatments of control structure design integrated with controller tuning using a real-time approach Perfect for senior undergraduate and graduate students studying general process control or distillation process control, A Real-time Approach to Distillation Process Control will also benefit plant managers, production supervisors, startup supervisors, operations engineers, production engineers, and chemical engineers working in industry.
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Veröffentlichungsjahr: 2023
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Table of Contents
Cover
Title Page
Copyright Page
Dedication Page
Preface
About the Companion Website
1 Introduction
1.1 The Purpose of Process Control
1.2 Introduction to Distillation
1.3 Distillation Process Control
1.4 A Real‐Time Approach to Distillation Process Control Education
Tutorial and Self Study Questions
References
2 Fundamentals of Distillation Control
2.1 Mass and Energy Balance: The Only Means to Affect Distillation Tower’s Behavior
2.2 Control Design Procedure
2.3 Degrees of Freedom
2.4 Pairing
2.5 Gain Analysis
2.6 Common Control Configuration
2.7 Screening Control Strategies via Steady‐State Simulation
Tutorial and Self‐Study Questions
References
3 Control Hardware
3.1 Introduction
3.2 Control Hardware Overview
3.3 Sensors
3.4 Final Control Elements
3.5 Controllers/CPU
3.6 Modern Trends
Tutorial and Self‐Study Questions
References
4 Inventory Control
4.1 Pressure Control
4.2 Level Control
Tutorial and Self‐Study Questions
References
5 Distillation Composition Control
5.1 Temperature Control
5.2 Actual Composition Control
5.3 More Complex Control Configurations
5.4 Distillation Control Scheme Design Using Steady‐State Models
5.5 Performance Analysis Using Steady‐State Data for an Existing Distillation Tower
5.6 Distillation Control Scheme Design Using Dynamic Models
Tutorial and Self‐Study Questions
References
6 Refinery Versus Chemical Plant Distillation Operations
6.1 New Generation of Refinery Controls
6.2 Improving Thermodynamic Efficiency Through Control
6.3 Blending and Its Implications on Control
Tutorial and Self‐Study Questions
References
7 Distillation Controller Tuning
7.1 Model Identification: Step Testing
7.2 Typical Process Responses
7.3 Engineering Units Versus Percent‐of‐Scale
7.4 Basics in PID Tuning
7.5 Tuning in Distillation Control
7.6 The Role of Tuning in a “Value Engineering” Era
Tutorial and Self‐Study Questions
References
8 Fine and Specialty Chemicals Distillation Control
8.1 Key Features
8.2 Measurement and Control Challenges
8.3 Nuances of Fine Chemicals Distillation
8.4 Side‐Draw Distillation
8.5 Composition Control in High‐Purity Side‐Draw Distillation
8.6 Advanced Distillation Column Configurations
8.7 Petlyuk and Divided Wall Columns
8.8 Optimal Design Versus Optimal Operations
8.9 Conclusions
Tutorial and Self‐Study Questions
References
9 Advanced Regulatory Control
9.1 Introduction
9.2 Cascade Control
9.3 Ratio Control
9.4 Feedforward Control
9.5 Constraint/Override Control
9.6 Decoupling
Tutorial and Self‐Study Questions
References
10 Model Predictive Control
10.1 Introduction to MPC
10.2 To MPC or not to MPC
10.3 MPC Fundamentals
10.4 Dynamic Matrix Control
10.5 Setting Up a MPC in Distillation
10.6 Digitalization and MPC
Tutorial and Self‐Study Questions
References
11 Plant‐Wide Control in Distillation
11.1 Distillation Column Trains
11.2 Heat Integration (Energy Recycle)
11.3 Materials Recycling
Tutorial and Self‐Study Questions
References
Workshop 1 Hands‐on Learning By Doing
Course Philosophy: “
Learning By Doing
” or “
Hands‐on Learning”
Key Learning Objectives
Book Coverage
Prerequisites
Study Material
Organization
The Simulation Tool
Overall Learning Objectives
Tasks
Tutorial and Self‐Study Questions
Workshop 2 Fundamental Distillation Column Control
Introduction
Key Learning Objectives
Workshop 3 Distillation Column Model Predictive Control
Introduction
Key Learning Objectives
Workshop 4 Distillation Column Control in a Plant‐Wide Setting
Introduction
Key Learning Objectives
Reference
Appendix A: P&ID Symbols
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Possible pairings available for dual composition control.
Chapter 5
Table 5.1 Typical equipment hold‐up times.
Chapter 8
Table 8.1 Example specifications for a methanol distillation column with a ...
Chapter 10
Table 10.1 Example step response and step response model coefficients.
Table 10.2 MPC process model requirements for MPC example.
Workshop 2
Table W2.1 Control configurations for Workshop 2.
Workshop 3
Table W3.1 Debutanizer MPC model parameters.
Workshop 4
Table W4.1 Plant‐wide control workshop control objectives.
Table W4.2 Pairing of manipulated and controlled variables.
List of Illustrations
Chapter 1
Figure 1.1 Typical definition of the purpose of process control.
Figure 1.2 Process control bodies of knowledge.
Chapter 2
Figure 2.1 Distillation theoretical stage liquid and vapor flows.
Figure 2.2 Changes to temperature/composition profile using mass balance.
Figure 2.3 Changes to temperature/composition profile using energy balance....
Figure 2.4 Distillation column schematic with five manipulated variables (th...
Figure 2.5 Distillation column as a “black box.”
Figure 2.6 Plot of distillate composition versus distillate flow.
Figure 2.7 Plot of distillate composition versus distillate flow.
Figure 2.8 Column basic control scheme.
Figure 2.9 Loop interactions for a 2 × 2 system.
Chapter 3
Figure 3.1 Orifice plate and differential pressure‐based flow rate calculati...
Figure 3.2 Schematic representation of how a level measurement can be derive...
Figure 3.3 Schematic representation of how a level can be derived from radar...
Figure 3.4 Hierarchical representation of a distributed control system.
Chapter 4
Figure 4.1 Basic total condenser‐based pressure control.
Figure 4.2 Flooded condenser arrangement.
Figure 4.3 Diagram illustrating how an elevated flooded condenser operates....
Figure 4.4 Hot‐vapor bypass design on flooded condensers.
Figure 4.5 Pressure control with no or only trace non‐condensable gases.
Figure 4.6 Pressure control with significant levels non‐condensable gases.
Figure 4.7 Different designs of accumulator systems.
Figure 4.8 Open‐loop stable versus integrating process step test response.
Figure 4.9 Truncated circle parameters.
Figure 4.10 Graph of Eq. (4.18).
Figure 4.11 Graph of Eq. (4.20) (Eq. (4.19) derivative).
Figure 4.12 Typical binary distillation column.
Figure 4.13 Level controller response as process gain decreases.
Chapter 5
Figure 5.1 Generic distillation column configuration.
Figure 5.2 Single temperature‐based composition control for the distillate....
Figure 5.3 LPG splitter temperature profile.
Figure 5.4 Composition to temperature cascade control for distillate composi...
Figure 5.5 Ryskamp’s scheme for distillate composition control.
Figure 5.6 Distillation tower temperature profile plot.
Figure 5.7 Distillation tower overhead composition v. stage temperature.
Figure 5.8 Distillation tower with double‐ended composition controls.
Chapter 6
Figure 6.1 Typical refinery distillation column controls evolution.
Figure 6.2 Pump around controls.
Figure 6.3 Side stripper controls.
Chapter 7
Figure 7.1 First‐order plus dead‐time process response.
Figure 7.2 First order plus dead time fit to actual plant data.
Figure 7.3 Integrating versus open‐loop stable process step responses.
Figure 7.4 PID controller responses for integrating versus process open‐loop...
Figure 7.5 Controller gain versus process gain.
Figure 7.6 Good v. poor MV–CV pairing.
Figure 7.7 Interactive versus ideal PID form block diagrams. (a) Interactive...
Figure 7.8 QDR controller response.
Chapter 8
Figure 8.1 Product impurity specification versus distillation flow control. ...
Figure 8.2 Temperature profile along the column with and without accumulatio...
Figure 8.3 Ratio control configuration between feed flow and distillate flow...
Figure 8.4 A ratio control structure reset by online product stream composit...
Figure 8.5 Side‐draw flow controller arrangement for a column configuration ...
Figure 8.6 Level control arrangement using the side‐draw controller for a co...
Figure 8.7 An intensified column design consisting of a pre‐fractionation un...
Figure 8.8 Potential control handles that can be used to control a pre‐fract...
Figure 8.9 A divided wall column configuration.
Chapter 9
Figure 9.1 Process control architecture layers.
Figure 9.2 Cascade control general information flow and components.
Figure 9.3 An example of cascade control in a distillation column.
Figure 9.4 An inferential cascade control example.
Figure 9.5 Ratio control general information flow and components.
Figure 9.6 An example of ratio control in a distillation column.
Figure 9.7 An example of double ratio control in distillation.
Figure 9.8 Feedforward control general information flow and components.
Figure 9.9 An example of feedforward ratio control.
Figure 9.10 Override control to prevent tower flooding.
Figure 9.11 Flash distillation schematic.
Chapter 10
Figure 10.1 The basic elements of model predictive control.
Figure 10.2 Side‐draw column example.
Chapter 11
Figure 11.1 Schematic of multiple distillation columns operated in series.
Figure 11.2 Averaging flow control on the bottoms flow to reduce the variati...
Figure 11.3 Waste heat reboiler.
Figure 11.4 Distillation tower feed preheater designs.
Figure 11.5 Feed preheating.
Figure 11.6 High‐pressure/low‐pressure columns.
Figure 11.7 Mechanical vapor recompression.
Figure 11.8 Materials recycle loop.
Figure 11.9 A simplified process flow diagram for the production of benzene ...
Workshop 2
Figure W2.1 Process overview.
Figure W2.2 Simulator P&ID diagram (LV‐1 control strategy, indirect feed).
Workshop 3
Figure W3.1 Simulator P&ID diagram (LV‐1 control strategy, indirect feed).
Workshop 4
Figure W4.1 Simulator P&ID diagram of the isomerization process.
Guide
Cover Page
Title Page
Copyright Page
Dedication Page
Preface
About the Companion Website
Table of Contents
Begin Reading
Workshop 1 Hands‐on Learning By Doing
Workshop 2 Fundamental Distillation Column Control
Workshop 3 Distillation Column Model Predictive Control
Workshop 4 Distillation Column Control in a Plant‐Wide Setting
Appendix A P&ID Symbols
Index
WILEY END USER LICENSE AGREEMENT
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A Real‐time Approach to Distillation Process Control
Brent R. Young
University of Auckland
New Zealand
Michael A. Taube
S&D Consulting, Inc
Houston, Texas, USA
Isuru A. Udugama
The University of Waikato
New Zealand
This edition first published 2023.© 2023 John Wiley & Sons, Inc.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of Brent R. Young, Michael A. Taube, and Isuru A. Udugama to be identified as the authors of this work has been asserted in accordance with law.
Registered OfficeJohn Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA
For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
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Limit of Liability/Disclaimer of WarrantyIn view of ongoing research, equipment modifications, changes in governmental regulations, and the constant flow of information relating to the use of experimental reagents, equipment, and devices, the reader is urged to review and evaluate the information provided in the package insert or instructions for each chemical, piece of equipment, reagent, or device for, among other things, any changes in the instructions or indication of usage and for added warnings and precautions. While the publisher and authors have used their best efforts in preparing this work, they 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 any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
Library of Congress Cataloging‐in‐Publication DataNames: Young, Brent R., author. | Taube, Michael A., author. | Udugama, Isuru A., author.Title: A real‐time approach to distillation process control / Brent R. Young, University of Auckland New Zealand, Michael A. Taube, S&D Consulting, Inc, Isuru A. Udugama, The University of Waikato, New Zealand.Description: Hoboken, NJ, USA : Wiley, [2023] | Includes bibliographical references and index.Identifiers: LCCN 2022051966 (print) | LCCN 2022051967 (ebook) | ISBN 9781119669210 (cloth) | ISBN 9781119669241 (adobe pdf) | ISBN 9781119669272 (epub)Subjects: LCSH: Distillation. | Chemical process control.Classification: LCC TP156.D5 Y58 2023 (print) | LCC TP156.D5 (ebook) | DDC 663/.506–dc23/eng/20230111LC record available at https://lccn.loc.gov/2022051966LC ebook record available at https://lccn.loc.gov/2022051967
Cover Image: WileyCover Design by © 06photo/Shutterstock
Dedicated to those from whom we (and you, the reader) have benefited:
William Y. Svrcek, PhD
Robert V. Bartman, PhD
Robert D. Kirkpatrick, PhD
Preface
There is a gap between academic study and practice of the subject of distillation process control: Students are currently unable to apply the theory as it is taught in the traditional way to the real world as they find it.
Despite the development of digital simulation tools, the subject of control theory has largely continued to be taught as it was in the 1960s or even earlier using transfer functions, frequency‐domain analysis, and Laplace transforms. For linear single‐loop systems such as electromechanical devices, this approach is well suited. As an approach to the control of distillation processes, which are characterized by nonlinearity, having multivariable influences, and exhibiting “slow” dynamic behavior (e.g. lag time and dead time), classical control techniques have significant limitations.
In today’s rich digital environment, hardware and software are generally available in universities and workplaces to implement a “hands‐on” approach to distillation process controls analysis, design, and operations using process simulators. Students and engineers are now able to experiment with digital twins or virtual plants developed within these process simulators that capture the important nonlinearities, dynamics, and multivariable nature of the real distillation units and are able to test even the most complex of control structures without struggling with oversimplified and nonintuitive mathematics or placing real plants at risk.
Lastly, while synthesis reactions are an essential element of every process plant and provide the feedstock to distillation units, due to the vast array and the specific characteristics of the many types of chemical synthesis processes (including biochemical, oil and gas processing, petrochemical, and specialty chemical), including “control” of the reaction elements is beyond the scope of this book. The common unit operation in all these processes is of course, distillation. Furthermore, distillation comprises the most flexible and manipulative handle to affect the economics of all profess units. Hence, the “reason why” for this book.
The basis of this text is therefore to provide a practical, hands‐on introduction to the topic of distillation process control by using only time‐based representations of the process and the associated instrumentation and controls. This book adopts the approach pioneered in A Real‐time Approach to Control (Svrcek et al. 2000), which was the first to treat the topic of process control generally without relying at all upon classical, frequency‐domain techniques such as Laplace transforms. In summary, how our book stands out is as follows:
Comprehensive treatment of distillation fundamentals and basic to modern advanced controls from a practical point of view.
A systematic build up from known base controls to more modern control practices with reasons explained and practical insights given.
A break from the traditional Laplace transform approach to a time‐based approach with a good mix of fundamentals and practical insights.
A teaching text with up‐to‐date process simulation exercises.
Employs an active learning, hands‐on, real‐time approach to facilitate student learning via process simulation workshops.
Process simulation exercises are designed to be simulator agnostic so that they can be performed on the process simulator locally available.
Combines control structure design and controller tuning in one handy book enabled through the unique “real‐time approach.”
The hands‐on exercises allow a student who follows the book to design and tune controls on a distillation column.
This textbook is designed as an introductory course on distillation process control for senior undergraduate or graduate university students in the chemical engineering curriculum. It is also a useful supplementary text to a general process control course, a primary text for a semester‐long, specific distillation process control course, and as a supplementary text for a plant design course.
It is also intended that professionals, including engineers, industrial scientists, and technicians, will benefit immensely from the book, especially but not only those new to the important field of distillation control. It is expected to also form the basis of an excellent three‐ or four‐day short course.
We believe the era of real‐time, simulation‐based instruction of distillation process control has finally arrived. We wish you every success as you begin to learn more about this mature and yet ever‐changing and exciting field. Your comments on and suggestions for improving this textbook and workshops are solicited and are most welcome!
Reference
Svrcek, W.Y., Mahoney, D.P., and Young, B.R. (2000).
A Real‐time Approach to Process Control
, 1e. Wiley.
About the Companion Website
This book is accompanied by a companion website.
www.wiley.com/go/Young/DistillationProcessControl
This website includes:
Selected Answers to Tutorial and Self‐Study Questions for Instructors
Figures from the book in PowerPoint
1Introduction
1.1 The Purpose of Process Control
In both academic and industrial treatises on process control, the stated purpose for the design of control schemes is to reduce process variation, as illustrated in Figure 1.1.
While this is a true statement, it falls well short of giving a full and complete purpose that is worthy of study and mastery. In addition to the purely mathematical treatment given by most academic (as well as industrial) control courses, the would‐be process control engineer has little, if any, guidance on what specific outcomes should result from proper controls design. Thus, we submit the following definition for the purpose of process control:
To stably, robustly, and predictably maintain Product Qualities in the face of Measured AND Unmeasured Disturbances with the LEAST total and incremental energy input (i.e. minimal movement) to the Process.
Based on this definition, one begins to understand that there are several elements or attributes that must be considered regarding the proper design and implementation of control schemes. Figure 1.2 illustrates the four bodies of knowledge, which, in the authors’ experience, are required for one to be a fully qualified and competent process control engineer.
Entire tomes can be (and have been) written on each of these topical areas. This book, however, while not providing a comprehensive coverage, addresses the first two and portions of the third elements, defined below, to give the engineering student and practicing engineer sufficient insights such that the real‐time performance of the actual distillation plant achieves (and even excels) the intended objectives. The working definitions for each of these bodies of knowledge are as follows:
Figure 1.1 Typical definition of the purpose of process control.
Figure 1.2 Process control bodies of knowledge.
Process Understanding: By far, the singularly most important area, it encompasses all of the fundamental aspects of fluid dynamics, heat and material transfer, thermodynamics, and reaction kinetics, that is basic process engineering design and operations principles.
Control Structures and Instrumentation: The former element being the major focus of this book. The latter is also a vital aspect, as the choice of measurement and final control devices play a significant role in the real‐time behavior of all the control schemes and these topics are introduced in the text.
Process Dynamics and Tuning: This body of knowledge is what separates the professional from the novice, in that, while the process engineer’s focus is on the steady‐state aspects of a process unit’s design and operations, only by knowing how a process behaves in real time, that is how measured and unmeasured disturbances propagate through process over time, and then understanding how this dynamic behavior affects regulatory controls such as PID controls (as well as model predictive) controls, the process engineer can ensure that the plant achieves the intended objectives. To be clear, the “black art” of proportional‐integral‐derivative (PID) and model predictive control (MPC) tuning is
not
addressed herein: due to the variations, nuances and inconsistencies of how control algorithms are implemented across the many control system platforms; that is a topic requiring its own treatment. Detailed tuning is also often not the domain of the process engineer. However, pointing out the potential effects of disturbances and how they should be addressed is covered in this text.
Control Systems and Process Automation: The proliferation of computer‐based control systems has resulted in a wide array of software products and systems’ capabilities and features for addressing important aspects of real‐time process operations, including a dizzying array of function blocks, high‐performance human–machine interfaces (HMI) (i.e. control system operating graphic displays), alarm rationalization and management and control systems design, implementation and maintenance, in addition to cybersecurity measures. Each of these aspects entail significant effort and training. While outside the scope of this book they are, nonetheless, important and vital considerations for robust, resilient, reliable, and secure process operations, and the general aspects of these topics are introduced to the reader.
While the underlying details of each of the preceding four bodies of knowledge can be expanded upon as technology improvements and enhancements are developed, the authors provide the following Eight Rules for successful controls implementation to assist the student and practicing engineer with guidance on what elements require focused attention:
Know how the process is operated and how it behaves in real time. While seemingly self‐explanatory, this rule implies that one must have an intimate understanding of how a specific process unit behaves across its entire operating envelope, including startup and shutdown conditions and knowing equipment design effects (and their limitations) in various operating scenarios (i.e. maximum throughput versus turndown, product selection/optimization, and seasonal influences).
Understand all of the primary, secondary, and tertiary operating objectives. Again, seemingly self‐explanatory but is necessary to ensure that the controls’ design supports (and does not conflict with) these objectives to ensure real‐time achievement.
Know the control system’s capabilities (and limitations) and use it to its fullest; avoid “rolling your own” algorithms(s), unless absolutely necessary.
Document the intent of the controls and the reason(s)
why
particular functions or features are utilized. This is both for your own reference but, more importantly, for the person who takes over from you in the on‐going maintenance and development of the controls you design.
Make the controls design reliable, resilient, robust, and maintainable. The effort required to accomplish this exceeds the typical “safe and operable” criteria applied by many engineering and operations management organizations. But it is required to ensure both the immediate and long‐term achievement of the unit’s objectives. The essence of this rule is that the controls should behave the same (even predictably) regardless of the conditions (throughput, weather, feed composition, etc.) to which it is subjected.
Make a good design into a great implementation with tuning. This is a significant element that separates the professional from the novice and, as described above, detailed tuning is substantially outside the scope of this book. Nevertheless, this is, more often than not, the reason for poor process performance, in spite of good controls structural design.
Provide an intuitive interface for the operator. If/when, in the event the operators must intervene, they should be provided with a readily available (e.g. intuitive) means to “take control” of the process without having to “drill down” through control system detail displays to alter some specific parameter(s) in one, or more, of the control scheme’s function blocks.
Identify possible or necessary follow‐up enhancements for the controls. This includes addressing nonlinear behavior (a significant issue that is detrimental to all controls – both PID and model‐predictive technologies) – as well as equipment changes, such as instrumentation selection and process piping and mechanical alterations.
These rules are summarized in what the authors propose should be the process control engineer’s Prime Directive:
Prevent the controls from doing anything unexpected!
1.2 Introduction to Distillation
Distillation is the process of the physical separation of components in a liquid mixture by heating and then cooling. This is accomplished by utilizing the differences of relative volatility between the mixture components. Distillation may be used to almost completely separate components into nearly pure component products or to partially separate components such that it selectively concentrates specific components into the products.
Distillation is a unit operation of significant industrial importance. For example, in 2019, there were 132 operating refineries in the United States with a crude distillation of 18.7 million US barrels per day (US Energy Information Administration 2019). The energy use of industrial distillation also represents a significant fraction of energy usage in the chemical and process industries. White (2012) reports distillation amounts to 40% of the total energy used to operate plants in the refining and bulk chemical industries. Thus, improving the control and energy efficiency of this unit operation is important to achieving overall energy savings.
Distillation has many applications. Throughout the Hydrocarbon Process Industry (HPI), distillation is used both in upstream and downstream processing. Crude oil is stabilized by partial distillation for safe storage and transport, and, at the refinery, fractional distillation is used to separate it into fuel products and chemical feedstocks. In addition to refining, distillation is used industrially in many other applications. Distillation is used in the chemical industry to separate and purify chemical reaction products to produce sales streams. The distillation of the products of fermentation and other bio‐industry processes also produces many products of commercial value, including distilled beverages of high alcohol content. Distillation is also used in cryogenic processes to separate air into its constituent components for use in industrial and medical grade gases, as well as for liquified natural gas (LNG) obtained from the associated gas out of oil and gas wells. And distillation continues to be used as a desalination treatment solution. It is a small wonder that it has been estimated that there are more than 40 000 distillation columns in North America (White 2012).
Distillation has a long history across most, if not all, ancient cultures. Early evidence of distillation has been found on Mesopotamian Babylonian tablets from around 1200 BCE that described distillation for perfumery operations (Levey 1959). Distillation may have been practiced in China as early as the first century of the Common Era (CE) (Haw 2012). Evidence has also been found in Roman (Forbes 1970) and Byzantine (Bunch and Hellemans 2004) Egypt in the first and third centuries CE. Distilled water has been produced since at least 200 CE (Taylor 1945). Distillation was also used to make weak liquor in ancient India (modern Pakistan) in the early centuries of the CE (e.g. Husain 1993).
Medieval Arabic chemists worked on the distillation of various substances from the eighth century of the CE (al‐Hassan 2009). By the twelfth century, fractional distillation (Burnett 2001) and the production of ethanol by the distillation of wine with salt (Multhauf 1966) had become known to Western European chemists.
As human history moved from the agricultural era to the first industrial revolution in the nineteenth century, the basis of modern distillation processing was developed – continuous processing, reflux, trayed columns, and preheating (Othmer 1982).
Chemical engineering’s genesis as a separate discipline at the end of the nineteenth century provided a scientific foundation to the development of distillation, simultaneously with the second industrial revolution and the developing petroleum industry with the development of design methods, including the Fenske equation (1932), the McCabe–Thiele method (1925), and the unit operations approach (Hougen 1977) in general.
Subsequent to these developments, the advent of heat integration, pinch technology, and process intensification for energy efficiency since the 1970s resulted in more complex and alternative distillation column design proposals, which, in some cases, have been implemented in industry, such as divided wall, reactive distillation, and Petlyuk columns (e.g. Doherty and Malone 2001).
A few words about how calculations are performed before talking about distillation process control are appropriate for a book advocating a real‐time (or simulation‐based) approach. Before the 1950s, calculations were done manually (e.g. using a slide rule with pencil and paper). As computer technology became more accessible, these manual calculations were implemented using simple programming languages, such as FORTRAN and, later, BASIC as the personal computer (PC) revolution came into being during the third industrial revolution in the 1970s and 1980s. Finally, today’s fourth industrial revolution (Industry 4.0) has ushered in a plethora of process simulation software for the fast, accurate steady‐state, as well as dynamic, simulation (e.g. as described in detail in Svrcek et al. 2014), and development of “digital twins” of distillation columns and entire process facilities – such tools as Aspen HYSYS, Schlumberger’s Symmetry, and Siemens PSE’s gPROMS. So, while manual methods still have their place for initial conceptualization and sanity checks, digital modeling tools enable fast, accurate, and precise modeling of complex processes and unit operations.
1.3 Distillation Process Control
The traditional approach to general control loop analysis and design for all processes, including distillation, was based on mechanical and electrical engineering methods very often derived from the frequency domain, such as transfer functions, Laplace transforms, Bode plots, and Nyquist diagrams. While perhaps somewhat helpful for developing a deep understanding of dynamics, these methods are essentially abstract mathematical formulations and were primarily pen and paper techniques for solving linear ordinary differential equations for single‐loop systems when the computational resources were unavailable. By knowing or identifying the Laplace transfer function of the process, a student or engineer could then use a range of controller tuning resources to create an ideal controller equation. However, this approach had major drawbacks that included:
The mathematical concept was often too abstract for students to grasp and apply practically.
An apparent disjoint between Laplace with the “time‐domain” often results in a lack of intuition for users.
They use linearized systems equations that oversimplify the complexities of distillation dynamics.
They are difficult to apply to real, multiple input and output processes, such as when attempting to develop multiloop controls.
Educationalists and industrialists alike have realized these issues and limitations and subsequently have taken a different approach. Present‐day distillation control texts tend to either be aimed at being references for practitioners with minimal or no treatment of process calculations or simulations, or be comprehensive academic texts that are similarly lacking in calculations and simulations, and sometimes only treat the fundamentals very lightly.
Notable examples of the former industry‐focused texts include (i) Shinskey (1977), which was a classic in the field for its time but was not a teaching text and included no advanced control or simulation; (ii) Luyben (1992), which is an edited volume of leading industry practitioners and academics and very good for its time but not a teaching text with no examples, exercises, or simulations; (iii) Luyben (2013), which includes design and control but whose primary focus is how to use a specific simulator rather than distillation and simulation fundamentals per se; (iv) Nag (2015), which is a practical reference intended for processing engineers and not a teaching text with minimal calculations and simulations.
Academic/teaching texts addressing distillation process control include (i) Robbins (2011), which is geared toward control structure using a lesson style but has very light coverage with little dynamics considered; (ii) Smith (2012) is a comprehensive distillation control book covering lots of basic and advanced control techniques, but no time‐varying behavior (dynamics), nor mention of simulation or exercises; (iii) Kiss (2013) that focused on advanced distillation arrangements and is comprehensive in that regard, but has cursory coverage of fundamentals, basic distillation, controls, and advanced process control; (iv) Gorak and Schoenmaker (2014) who present a comprehensive, well‐written coverage of the topic, but without sections on advanced process control, light coverage of simulation, and no explicit coverage of energy usage.
1.4 A Real‐Time Approach to Distillation Process Control Education
With the ever‐improving computational capabilities, high‐fidelity distillation modeling (and control) emerged in the late 1980s. These tools explicitly modeled the mass transfer, transport phenomena, thermodynamics, and the vapor–liquid equilibrium (VLE) of a given distillation column. By the early 2000s industrial process simulators were able to accurately and explicitly model the time‐dependent behavior of a distillation column (i.e. dynamics). The simulation environment is graphical in nature, somewhat mimicking a distributed control system’s (DCS) graphical screen, enabling even modestly experienced users to set up a distillation process with relative ease. The implications for education are that the users could focus on learning the development of controls rather than how to represent a distillation operation using abstract mathematics such as Laplace Transforms; refer to the book A Real‐Time Approach to Process Control (Svrcek et al. 2014) that includes a step‐by‐step guide on how this was achieved. The main benefits of applying a simulation‐based approach to teaching and learning distillation control for the user/students are as follows:
Users/students experience realistic nonlinear, coupled process responses that are expected from a distillation column, as opposed to idealized linear, decoupled mathematical functions, as there is a one‐to‐one correspondence between the dynamic distillation models and the actual plant.
Users/students are able to learn how to set up sensors, controllers, and valves to achieve both an overall as well as specific control objective(s).
Through interaction with the dynamic process simulator, users/students can develop an intuitive understanding of the dynamics of distillation control and relate learnings from other areas of process engineering to the observations.
Users/students are able to compare candidate control structures and assess the propagation of disturbances through a distillation plant, enabling the evaluation of advanced process control and plant‐wide control schemes.
Before summarizing the content of the following chapters, it is appropriate to mention one thing that this book consciously does not cover in detail – instrumentation and control hardware. The hardware of course makes software happen and is therefore very important for control scheme execution. In Svrcek et al. (2014), a broad introduction is given. However, this topic is worth a book in itself, and indeed, there is an excellent text that provides a very comprehensive cover (McMillan and Vegas 2019).
This text is organized into a framework that provides relevant theory and industrial experience, along with a series of hands‐on workshops that employ computer simulations that test and explore the theory. This chapter provided conceptual practical basis and a historical overview of the field of real‐time distillation process control, including simulation, and the pros (mainly) and cons of a real‐time approach. Chapter 2 introduces the fundamentals of distillation control, covering the basic principles of distillation and relevant control information, including pressure and inventory control. Chapter 3 introduces the reader to the fundamentals of hardware that the “software” described in this book runs on. In Chapter 4, we look at distillation inventory control, a key set of issues for stability, in greater detail. Then, in Chapter 5
