Full Scale Plant Optimization in Chemical Engineering E-Book

Zivorad Lazic

• Herausgeber: Wiley-VCH GmbH
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 235

Veröffentlichungsjahr: 2022

Beschreibung

This new book is to compare the primary methods in full scale plant optimization applications and focuses on a simple but powerful technique which provides the experimental tools for full scale plant optimization, Evolutionary Optimization or EVOP.

Leseprobe

Table of Contents

Cover

Title Page

Copyright

Preface

Biography

1 The Basic Ideas

1.1 Introduction

Notes

2 Design of Experiments – DOE

2.1 The 2

2

Factorial Designs

2.2 Effects for 2

2

Factorial Designs

2.3 Interactions Between Factors

2.4 Standard Error for the Effects

2.5 2

3

Factorial Design

2.6 Effects for the 2

3

Factorial Designs

2.7 Standard Errors of Effects for Two‐ and Three‐Level Factorial Designs

3 Neural Network Modeling – Data Mining

3.1 Data Preprocessing

3.2 Building, Training, and Verifying Model

3.3 Model Analyzing

3.4 What‐Ifs Optimization

3.5 DOE Experiment Using Neural Networks Model

Note

4 Evolutionary Operation – EVOP

4.1 Small‐Scale and Plant‐Scale Investigation

4.2 Scale‐up

4.3 Static and Evolutionary Operation

4.4 Analysis of Information Board

4.5 Three‐Factor Scheme

4.6 Current Best‐Known Conditions

4.7 Change in Mean for a 2

2

Factorial Design with Center Point

4.8 Standard Errors for the Effects

4.9 The Effects and Their Standard Errors for a 2

2

Design with Center Point

4.10 Analysis of Information Board for Three Responses Using Factorial Effects

4.11 2

3

Factorial Design Effects, Interpretation, and Information Board

4.12 Dividing the 2

3

Factorial Design Into Two Blocks

4.13 2

3

Design with Two Center Points Run in Two Blocks

5 Different Techniques of EVOP

5.1 Box EVOP – BEVOP

5.2 Calculation Procedure for Two‐Factor EVOP

5.3 Calculation Procedure for Three‐Factor EVOP

5.4 BEVOP in Plant‐Scale Experiments

5.5 BEVOP Applications

5.6 BEVOP Advantages and Disadvantages

5.7 BEVOP Simulation

5.8 Rotating Square Evolutionary Operation – ROVOP

5.9 Random Evolutionary Operation – REVOP

5.10 Quick‐Start EVOP – QSEVOP

5.11 QSEVOP Simulation

5.12 Simplex Evolutionary Operation – SEVOP

5.13 Some Practical Advice About Using EVOP

6 EVOP Software

Appendix A: The Approximate Method of Estimating the Standard Deviation in EVOP

Appendix B: 2

2

‐ and 2

3

‐Factor Box EVOP Calculations with Center Point

B.1 2

3

Three‐Factor Box EVOP Calculations with Center Point

Appendix C: Short Table of Random Normal Deviates

Appendix D: How Many Cycles Are Necessary to Detect Effects of Reasonable Size

Appendix E: Multiple Responses: The Desirability Approach

References

Index

End User License Agreement

List of Tables

Chapter 2

Table 2.1 2

2

Factorial design.

Table 2.2 2

2

Factorial design in coded values.

Table 2.3 Factors settings.

Table 2.4 The eight sets of conditions of a 2

3

factorial design.

Table 2.5 2

3

Factorial design in coded values.

Table 2.6 Standard Errors (S.E.) for effects.

Table 2.7 Response surface design (

n

 = 2;

b

 = 4;

N

 = 20).

Table 2.8 D‐Optimal design (

n

 = 3;

N

 = 19).

Chapter 3

Table 3.1 Five years, process data and fiber strength.

Table 3.2 Sensitivity report.

Table 3.3 Set points and What‐Ifs optimization tool.

Table 3.4 The central composite rotatable design.

Table 3.5 The central composite rotatable design matrix.

Table 3.6 The factor's levels.

Chapter 4

Table 4.1 The effects and standard errors.

Table 4.2 Information board for by‐product yields after three cycles.

Table 4.3 By‐product data for three repetitions/cycles of a 2

3

design.

Table 4.4 Values of

f

k,n

.

Table 4.5 Calculation for

S

2

and

S

3

.

Chapter 5

Table 5.1 Individual batch yields.

Table 5.2 Information board.

Table 5.3 Individual batch yields.

Table 5.4 Information board.

Table 5.5 2

2

EVOP information board.

Table 5.6 Initial experimental conditions.

Table 5.7 Information board.

Table 5.8 BEVOP calculation.

Table 5.9 Information board.

Table 5.10 BEVOP calculation.

Table 5.11 Information board.

Table 5.12 BEVOP calculation.

Table 5.13 Information board.

Table 5.14 BEVOP calculation.

Table 5.15 Information board.

Table 5.16 Initial experimental conditions.

Table 5.17 Information board.

Table 5.18 BEVOP calculation.

Table 5.19 Information board.

Table 5.20 BEVOP calculation.

Table 5.21 Information board.

Table 5.22 BEVOP calculation.

Table 5.23 Information board.

Table 5.24 BEVOP calculation.

Table 5.25 Information board.

Table 5.26 2

2

BEVOP simulation.

Table 5.27 2

2

BEVOP information board.

Table 5.28 2

2

BEVOP simulation.

Table 5.29 2

2

BEVOP information board.

Table 5.30 2

2

BEVOP simulation.

Table 5.31 2

2

BEVOP information board.

Table 5.32 2

2

BEVOP simulation.

Table 5.33 2

2

BEVOP information board.

Table 5.34 2

2

BEVOP simulation.

Table 5.35 2

2

BEVOP information board.

Table 5.36 2

2

BEVOP simulation.

Table 5.37 2

2

BEVOP information board.

Table 5.38 2

2

BEVOP simulation.

Table 5.39 2

2

BEVOP information board.

Table 5.40 2

2

BEVOP simulation.

Table 5.41 2

2

BEVOP information board.

Table 5.42 2

2

BEVOP simulation.

Table 5.43 2

2

BEVOP information board.

Table 5.44 2

2

BEVOP simulation.

Table 5.45 2

2

BEVOP information board.

Table 5.46 2

3

BEVOP simulation.

Table 5.47 2

3

BEVOP information board.

Table 5.48 2

3

BEVOP simulation.

Table 5.49 2

3

BEVOP information board.

Table 5.50 2

3

BEVOP simulation.

Table 5.51 2

3

BEVOP information board.

Table 5.52 2

3

BEVOP simulation.

Table 5.53 2

3

BEVOP information board.

Table 5.54 2

2

ROVOP simulation.

Table 5.55 2

2

ROVOP information board.

Table 5.56 2

2

ROVOP simulation.

Table 5.57 2

2

ROVOP information board.

Table 5.58 2

2

ROVOP simulation.

Table 5.59 2

2

ROVOP information board.

Table 5.60 2

2

ROVOP simulation.

Table 5.61 2

2

ROVOP information board.

Table 5.62 2

2

ROVOP simulation.

Table 5.63 2

2

ROVOP information board.

Table 5.64 2

2

ROVOP simulation.

Table 5.65 2

2

ROVOP information board.

Table 5.66 2

2

ROVOP simulation.

Table 5.67 2

2

ROVOP information board.

Table 5.68 2

2

ROVOP simulation.

Table 5.69 2

2

ROVOP information board.

Table 5.70 2

2

ROVOP simulation.

Table 5.71 2

2

ROVOP information board.

Table 5.72 2

2

BEVOP simulation.

Table 5.73 2

2

BEVOP information board.

Table 5.74 2

2

BEVOP simulation.

Table 5.75 2

2

BEVOP information board.

Table 5.76 2

2

BEVOP simulation.

Table 5.77 2

2

BEVOP information board.

Table 5.78 2

2

BEVOP simulation.

Table 5.79 2

2

BEVOP information board.

Table 5.80 2

3

ROVOP simulation.

Table 5.81 2

3

ROVOP information board.

Table 5.82 2

3

ROVOP simulation.

Table 5.83 2

3

ROVOP information board.

Table 5.84 2

3

ROVOP simulation.

Table 5.85 2

3

ROVOP information board.

Table 5.86 2

3

ROVOP simulation.

Table 5.87 2

3

ROVOP information board.

Table 5.88 2

3

BEVOP simulation.

Table 5.89 2

3

BEVOP information board.

Table 5.90 2

3

BEVOP simulation.

Table 5.91 2

3

BEVOP information board.

Table 5.92 2

3

BEVOP simulation.

Table 5.93 2

3

BEVOP information board.

Table 5.94 2

3

BEVOP simulation.

Table 5.95 2

3

BEVOP information board.

Table 5.96 REVOP work sheet.

Table 5.97 REVOP simulation.

Table 5.98 REVOP simulation.

Table 5.99 REVOP simulation.

Table 5.100 REVOP simulation.

Table 5.101 REVOP simulation.

Table 5.102 QSEVOP simulation.

Table 5.103 QSEVOP simulation.

Table 5.104 QSEVOP simulation.

Table 5.105 QSEVOP simulation.

Table 5.106 QSEVOP simulation.

Table 5.107 Coordinates of simplex vertices.

Table 5.108 Simplex matrix.

Table 5.109 Factors and variation intervals.

Table 5.110 Initial simplex.

Table 5.111 Initial simplex.

Table 5.112 Simplex optimization.

Table 5.113 Initial simplex.

Table 5.114 Simplex optimization.

Table 5.115 SEVOP simulation, Phase I.

Table 5.116 SEVOP simulation, Phase XI.

Table 5.117 SEVOP simulation, Phase XII.

Table 5.118 Summary of simplex movement.

Table 5.119 SEVOP simulation, Phase I.

Table 5.120 SEVOP simulation, Phase I.

Table 5.121 Summary of SEVOP movement.

List of Illustrations

Chapter 2

Figure 2.1 Fluidity averages after four cycles.

Figure 2.2 2

3

factorial design with average by‐product yields after three cy...

Figure 2.3 Ozone column with bleaching.

Figure 2.4 Ozone product GEB response surface.

Figure 2.5 Contour diagram for ozone product GEB response surface.

Figure 2.6 Bleach product GEB response surface.

Figure 2.7 Contour diagram for bleach product GEB response surface.

Chapter 3

Figure 3.1 The Relative Error

R

2

and the strip chart with original and predi...

Figure 3.2 The Predicted vs. Actual‐Scatter plot.

Figure 3.3 Sensitivity vs. Rank analysis plots.

Figure 3.4 Output vs. Percent Plots of the input–output relationships.

Figure 3.5 Sensitivity vs. Percent analysis plots.

Figure 3.6 Response surfaces.

Chapter 4

Figure 4.1 Effects and the “noise level”.

Figure 4.2 Information board after four cycles.

Figure 4.3 Three‐factor EVOP scheme.

Figure 4.4 Interpretation of the change in mean effect.

Figure 4.5 Information board at the end of four cycles.

Figure 4.6 Approximate contours for (a) cost, (b) impurity, and (c) fluidity...

Figure 4.7 The blocks of the 2

3

design.

Chapter 5

Figure 5.1 2

2

EVOP with center point.

Figure 5.2 Work sheet Cycle No. 1.

Figure 5.3 Work sheet Cycle No. 2.

Figure 5.4 Work sheet Cycle No. 3.

Figure 5.5 Work sheet Cycle No. 4.

Figure 5.6 2

3

Design with center points.

Figure 5.7 Work sheet Cycle No. 1.

Figure 5.8 Work sheet Cycle No. 2.

Figure 5.9 Work sheet Cycle No. 3.

Figure 5.10 2

2

Design for EVOP.

Figure 5.11 Work sheet cycle No. 1.

Figure 5.12 Work sheet cycle No. 2.

Figure 5.13 Work sheet cycle No. 1.

Figure 5.14 Work sheet cycle No. 2.

Figure 5.15 Work sheet cycle No. 1.

Figure 5.16 Work sheet cycle No. 2.

Figure 5.17 Work sheet cycle No. 1.

Figure 5.18 Work sheet cycle No. 2.

Figure 5.19 Work sheet cycle No. 1.

Figure 5.20 Work sheet cycle No. 2.

Figure 5.21 Work sheet cycle No. 1.

Figure 5.22 Work sheet cycle No. 2.

Figure 5.23 Work sheet cycle No. 1.

Figure 5.24 Work sheet cycle No. 2.

Figure 5.25 Work sheet cycle No. 1.

Figure 5.26 Work sheet cycle No. 2.

Figure 5.27 Work sheet cycle No. 1.

Figure 5.28 Work sheet cycle No. 2.

Figure 5.29 Work sheet cycle No. 1.

Figure 5.30 Work sheet cycle No. 2.

Figure 5.31 Work sheet cycle No. 1.

Figure 5.32 Work sheet cycle No. 2.

Figure 5.33 Work sheet cycle No. 1.

Figure 5.34 Work sheet cycle No. 3.

Figure 5.35 Work sheet cycle No. 10.

Figure 5.36 Work sheet cycle No. 1.

Figure 5.37 Work sheet cycle No. 3.

Figure 5.38 Work sheet cycle No. 10.

Figure 5.39 Work sheet cycle No. 1.

Figure 5.40 Work sheet cycle No. 3.

Figure 5.41 Work sheet cycle No. 10.

Figure 5.42 Work sheet cycle No. 1.

Figure 5.43 Work sheet cycle No. 3.

Figure 5.44 Work sheet cycle No. 10.

Figure 5.45 Work sheet cycle No. 1.

Figure 5.46 Work sheet cycle No. 3.

Figure 5.47 Work sheet cycle No. 10.

Figure 5.48 Response surface.

Figure 5.49 Contour diagram.

Figure 5.50 Work sheet cycle No. 1.

Figure 5.51 Work sheet cycle No. 3.

Figure 5.52 Work sheet cycle No. 10.

Figure 5.53 Work sheet cycle No. 1.

Figure 5.54 Work sheet cycle No. 3.

Figure 5.55 Work sheet cycle No. 10.

Figure 5.56 Work sheet cycle No. 1.

Figure 5.57 Work sheet cycle No. 3.

Figure 5.58 Work sheet cycle No. 10.

Figure 5.59 Response surface.

Figure 5.60 Contour diagram.

Figure 5.61 Work sheet cycle No. 1.

Figure 5.62 Work sheet cycle No. 3.

Figure 5.63 Work sheet cycle No. 10.

Figure 5.64 Work sheet cycle No. 1.

Figure 5.65 Work sheet cycle No. 3.

Figure 5.66 Work sheet cycle No. 10.

Figure 5.67 Response surface.

Figure 5.68 Contour diagram.

Figure 5.69 Work sheet cycle No. 1.

Figure 5.70 Work sheet cycle No. 3.

Figure 5.71 Work sheet cycle No. 10.

Figure 5.72 Work sheet cycle No. 1.

Figure 5.73 Work sheet cycle No. 3.

Figure 5.74 Work sheet cycle No. 10.

Figure 5.75 Work sheet cycle No. 1.

Figure 5.76 Work sheet cycle No. 3.

Figure 5.77 Work sheet cycle No. 10.

Figure 5.78 Work sheet cycle No. 1.

Figure 5.79 Work sheet cycle No. 3.

Figure 5.80 Work sheet cycle No. 10.

Figure 5.81 Response surface.

Figure 5.82 Contour diagram.

Figure 5.83 Five successive 2

2

ROVOP patterns.

Figure 5.84 Moving center of the ROVOP without reduction of interval.

Figure 5.85 Moving center of the ROVOP with reduction of interval.

Figure 5.86 Work sheet replica No. 10.

Figure 5.87 Work sheet replica No. 10.

Figure 5.88 Work sheet replica No. 10.

Figure 5.89 Work sheet replica No. 10.

Figure 5.90 Work sheet replica No. 10.

Figure 5.91 Work sheet replica No. 10.

Figure 5.92 Work sheet replica No. 10.

Figure 5.93 Work sheet replica No. 10.

Figure 5.94 Work sheet replica No. 10.

Figure 5.95 Work sheet cycle No. 10.

Figure 5.96 Work sheet cycle No. 10.

Figure 5.97 Work sheet cycle No. 10.

Figure 5.98 Work sheet cycle No. 10.

Figure 5.99 Graphical interpretation of ROVOP moving toward optimum.

Figure 5.100 Two successive 2

3

ROVOP patterns.

Figure 5.101 Simplex movement to optimum.

Figure 5.102 Simplex in coordinate system.

Figure 5.103 Tetrahedron in coordinate system.

Figure 5.104 Coordinates of simplex vertices.

Figure 5.105 Simplex movement on response surface.

Figure 5.106 Initial simplex.

Figure 5.107 Simplex movement.

Figure 5.108 Simplex movement on response surface.

Figure 5.109 The simplex movement on the response surface.

Appendix E

Figure E.1 Desirability functions and optimal solution for example problem....

Figure E.2 Overall desirability function for example problem.

Guide

Cover

Table of Contents

Title Page

Copyright

Preface

Biography

Begin Reading

Appendix A The Approximate Method of Estimating the Standard Deviation in EVOP

Appendix B 22‐ and 23‐Factor Box EVOP Calculations with Center Point

Appendix C Short Table of Random Normal Deviates

Appendix D How Many Cycles Are Necessary to Detect Effects of Reasonable Size

Appendix E Multiple Responses: The Desirability Approach

References

Index

End User License Agreement

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Full Scale Plant Optimization in Chemical Engineering

A Practical Guide

Živorad R. Laziċ

Author

Dr. Živorad R. LaziċMilutina Milankovica 74/1411070 Novi BeogradSerbia

All books published by WILEY‐VCH are carefully produced. Nevertheless, authors, editors, and publisher do not warrant the information contained in these books, including this book, to be free of errors. Readers are advised to keep in mind that statements, data, illustrations, procedural details or other items may inadvertently be inaccurate.

Library of Congress Card No.: applied for

British Library Cataloguing‐in‐Publication DataA catalogue record for this book is available from the British Library.

Bibliographic information published by the Deutsche NationalbibliothekThe Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at <http://dnb.d-nb.de>.

© 2022 WILEY‐VCH GmbH, Boschstr. 12, 69469 Weinheim, Germany

All rights reserved (including those of translation into other languages). No part of this book may be reproduced in any form – by photoprinting, microfilm, or any other means – nor transmitted or translated into a machine language without written permission from the publishers. Registered names, trademarks, etc. used in this book, even when not specifically marked as such, are not to be considered unprotected by law.

Print ISBN: 978‐3‐527‐35038‐4ePDF ISBN: 978‐3‐527‐83729‐8ePub ISBN: 978‐3‐527‐83728‐1oBook ISBN: 978‐3‐527‐83727‐4

Cover Design: SCHULZ Grafik‐Design

Preface

                                               “Let your systems learn the wisdom of age and experience”

It is well known that chemical engineers are involved in each step of the process development (lab‐scale and pilot‐scale) and later in the scale‐up and full‐scale plant process improvement. So far, there are three powerful methodologies/tools for chemical engineers with wide application in industry:

Design of Experiments – DOE

Evolutionary Operation – EVOP

Data Mining using Neural Networks – DM

The objective of this new book is to compare all three methods in full‐scale plant optimization applications and to focus on a simple but powerful technique which provides the experimental tools for full‐scale plant optimization. It is called Evolutionary Operation or EVOP.

Chapter I provides the basic principles necessary for a simple understanding of the difference between these three methodologies.

Chapter II covers only two‐ and three‐factor full factorial designs from the Design of Experiments because of the subject of this book. DOE experiments are very efficient in the lab or pilot plant but NOT in the full‐scale process. There are two main reasons for that:

An owner of the large‐scale process would hardly accept DOE experiment. To detect significant factor's effect in industrial process, a large change in control factors is required, and that on the other hand will produce off‐spec product.

Also replications required by DOE to reduce noise will produce even more off‐spec products.

Chapter III describes theory and application of Data Mining using the Neural Networks modeling tool. In the last twenty or more years, with more data historians installed in process industry, Data Mining, using neural network software, is widely used for industrial process optimization. The main problem with this approach without experiment is static operation of the plant, which means running the plant with almost no change in process factors. Standard Operating Procedures keep factors constant for a very long time. A greater change in process factors is not allowed. If it happens, it will produce off‐spec product. This means, all collected data are in very narrow experimental space range, and usually local or global optimum of the process is out of that range.

An analysis of historical data typically has only 10% to 20% chance of success, despite the fact that hundreds or thousands of data points may be available.

Chapter IV, DOE and Data Mining disadvantages, strongly suggests reusing Evolutionary Operation – EVOP experimental technique. Box G.E.P proposed a special Operation as a technique for improving industrial productivity. Its basic philosophy is that it is nearly always inefficient to run an industrial process to produce a product alone; a process should be run so as to generate a product plus information on how to improve that product. The procedure consists of carefully planned cycle of minor variants on the standard work process. The routine procedure consists of running each of the variants continually repeating the cycle. Usually the effects of these deliberate changes in the factors‐variables are masked by the large errors inherent in large‐scale production units. However, since production will continue anyway, a cycle of variants, which do not affect production significantly, can be run almost indefinitely. Because of constant repetition the effect of small changes can be detected. The wisdom of the age and long experience suggests that EVOP tools help chemical engineers dealing with two different objectives:

Scale‐up from lab and pilot‐plant to full‐scale operation and “fine tuning” of large‐scale production.

Once the optimum is achieved at certain point of time, it is going to be a moving target and it is very hard or impossible to use tools like DOE and DM due to variability of raw materials quality, energy supply fluctuation, tooling wear, gradual equipment misalignment, shifts in ambient temperature and humidity, corrosion, decreasing heat transfer, aging equipment and instrumentation, aging catalyst and scale build‐up, etc.

Chapter V describes theory and applications of five different EVOP methods including Box EVOP, with numerous examples and simulations to compare efficiency:

Box Evolutionary Operation – BEVOP

Rotating Square Evolutionary Operation – ROVOP

Random Evolutionary Operation – REVOP

Simplex Evolutionary Operation – SEVOP

Quick Start EVOP – QSEVOP.

These simulators are the perfect tools for training operators in different EVOP techniques. Also they could be used to run full‐scale plant optimization. In meantime, a special software “EVOP Engine” is under construction and will be released after the book is published.

Research and development scientists and engineers may find the book helpful in optimizing various processes to be more efficient, less energy intensive, less time consuming, more reliable, and less wasteful of raw materials.

This book has come into being a product of many years of research activities at Military Technical Institute, Belgrade, and long experience in process and product development with large‐scale production, including Lenzing Fibers Corporation, Austria; MacDermid, TN, US; and BASF Catalysts LLC., GA, US. The author is especially pleased to offer his gratitude to Helena Smuckler and Anica Lazic for editing of the manuscript. I express my special gratitude to Milica Pojiċ. for helping in a search for the literature.

Petrovac, MontenegroJune 2021

Živorad R. Laziċ

Biography

Živorad R. Laziċ is the author of Design of Experiments in Chemical Engineering: A Practical Guide, published by J. Wiley in January 2004. He has produced a unique, “how to do it,” a practical guide for the statistical design of experiments. It is the ideal book for the industrial scientist or engineer who wants to take advantage of DOE techniques without becoming a statistician. Basic statistical ideas are presented clearly and simply with numerous examples. This is one of the few books that are practically suited for self‐study by a busy technologist, engineers, and scientists. He is a Certified Six‐Sigma Black Belt professional with interests in advanced statistical tools, Design of Experiments (DOE), Statistical Process Control (SPC), Evolutionary Operation (EVOP), and process modeling via application of neural networks.

Živorad received his B.S., M.S., and Ph.D. in chemical engineering from the University of Belgrade, in Belgrade, Serbia. He began his career with Viscosa‐Loznica Corporation (1975–1979). After that he worked for Military Technical Institute (VTI), Belgrade, where he took a position as Head of R&D Department for composite rocket propellant. He was trained in Hercules, McGregor, TX, in 1982 and 1986. Lazić moved abroad in 1994 due to the war which took place in former Yugoslavia.

He spent more than six years as Vice President for process and product development in P.T. South Pacific Viscose, Indonesia, subsidiary of Lenzing Group, Austria. After he moved to USA in 2001, he worked as Quality Assurance manager at Lenzing Fibers Corporation, Lowland, TN, and Black Belt Six Sigma engineer at MacDermid Graphics Solutions Morristown, TN.

From 2008 Živorad worked as a Senior Research Scientist at BASF Catalysts LLC., Gordon, GA., USA. He retired from this position in 2014.

1The Basic Ideas

1.1 Introduction

A typical industrial process passes through many stages of development. Thus, in the evolution of a chemical process to produce a certain product, first comes the idea for a promising manufacturing step, followed by long laboratory work to approve concept. The laboratory results provide a preliminary estimate of feasibility and may lead to the possible industrial process. Those results may then be used to build a pilot plant. The pilot plant is a next step between lab and full scale and will have sufficient flexibility to test more extreme conditions. Engineers will use data from pilot plant to design a full‐scale plant. Assuming that the plant is built then, ideally, plant will run with operating conditions generated from lab and pilot plant. The small‐scale work will have provided estimates of process parameters like concentrations, temperatures, pressures, and flow rates. These estimates of process conditions represent only good first guesses in a continuing process adjustment. This is the reason for special attention to plant startup. A special technical team is usually assigned during the startup, since it is realized that major adjustments may be necessary before the process can be made to perform reasonably well (or sometimes to perform at all).