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- Sprache: Englisch
Chemical Product Formulation Design and Optimization
Explore the cutting-edge in chemical product formulation and design
In Chemical Product Formulation Design and Optimization: Methods, Techniques, and Case Studies, a team of renowned technologists and engineers delivers a practice guide to chemical product design. Offering real-world case studies for disinfectant formulation, the optimization of defined media, and the formulation of biocomposites, the book contains introduction to the current product design process.
In addition to the background of related statistical techniques, readers will find:
- Clear illustrations, figures, and tables that improve understanding and retention of critical topics
- Thorough introductions to the mathematical principles of chemical product design
- A complete examination of intellectual property considerations in the chemical product design process
Ideal for process and chemical engineers, Chemical Product Formulation Design and Optimization: Methods, Techniques, and Case Studies is a must-read resource for professionals in the pharmaceutical and cosmetics industry as well as chemical engineers working in the food, paint, and dye industries who seek a one-stop resource that includes the latest advances in chemical product formulation.
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Seitenzahl: 365
Veröffentlichungsjahr: 2023
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Table of Contents
Cover
Title Page
Copyright
Preface
About the Authors
1 Introduction
1.1 Chemical Product Engineering
1.2 Chemical Product Design
1.3 Product Design and Computer-Aided Product Design
References
2 Some Typical Applications of Chemical Product Design and Intellectual Property
2.1 Natural Fiber Plastic Composites
2.2 Wheat Straw Polypropylene Composites
2.3 Modeling Natural Fiber Polymer Composites
2.4 Graphene Composites
2.5 Corrosion Protection Using Polymer Composites
2.6 Intellectual Property
References
3 Mathematical Principles for Chemical Product Design
3.1 Factorial and Fractional Factorial Design
3.2 Response Surface Methods and Designs
3.3 D-Optimal Designs
3.4 Bayesian Design of Fractional Factorial Experiments
3.5 Regression Analysis
3.6 Artificial Neural Networks
3.7 Mixture Design of Experiments
3.8 Multiway Principal Component Analysis
References
4 Disinfectant Formulation Design
4.1 Introduction
4.2 Disinfectants Characteristics
4.3 Toxicity of Disinfectants
4.4 Experimental Design for Antimicrobial Activity
4.5 Experimental Design for Stability of Hydrogen Peroxide
4.6 Experimental Design for Corrosion
4.7 Final Formulation Optimization
4.8 Conclusion
References
5 Streptomyces Lividans 66 for developing a Minimal Defined Medium for Recombinant Human Interleukin-3
5.1 Introduction
5.2 Materials and Methods
5.3 Results and Discussion
5.4 Conclusion
References
6 Multivariate Modeling of a Chemical Toner Manufacturing Process
6.1 Introduction
6.2 Results and Discussion
6.3 Conclusion
References
7 Wheat Straw Fiber Size Effects on the Mechanical Properties of Polypropylene Composites
7.1 Introduction
7.2 Materials and Methods
7.3 Results and Discussions
7.4 Conclusion
References
8 Framework for Product Design of Wheat Straw Polypropylene Composite
8.1 Introduction
8.2 Product Design Framework for WS-PP Composite
8.3 Response Surface Models
8.4 Case Study
8.5 Conclusion
References
9 Product Design for Gasoline Blends to Control Environmental Impact Using Novel Sustainability Indices: A Case Study
9.1 Introduction
9.2 Methodology
9.3 Results
9.4 Conclusion
References
10 Corrosion Protection of Copper Using Polyetherimide/Graphene Composite Coatings
10.1 Introduction
10.2 Experimental
10.3 Results and Discussion
10.4 Conclusion
References
11 Optimization of Mechanical Properties of Polypropylene Montmorillonite Nanocomposites
11.1 Introduction
11.2 Methodology
11.3 Mathematical Models
11.4 Optimization Mechanism
11.5 Results and Discussion
11.6 Conclusion
References
12 Product Selection and Business Portfolio for Long-Range Financial Stability: Case Study from the Petrochemical Industry
12.1 Introduction
12.2 Manufacturing Strategy and Product Selection Tools
12.3 Model Development
12.4 Illustrative Case Study
12.5 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Automotive manufacturers, model, and components using natural fibe...
Table 2.2 Examples of variables in the formulation and manufacturing of whea...
Table 2.3 Discrepancies between theory and experimental work in natural fibe...
Chapter 3
Table 3.1 Input and output neurons for a network architecture containing inp...
Table 3.2 A 2
2
(two factors – two levels) factorial design of the lemonade e...
Chapter 4
Table 4.1 Historical data for antimicrobial tests against
S. aureus
.
Table 4.2 Fractional factorial design to augment the historical data.
Table 4.3 Analysis of variance.
Table 4.4 Validation data set for antimicrobial tests against staph in coded...
Table 4.5 Historical stability data.
Table 4.6 Bayesian D-optimality design to augment the prior data with 4 tria...
Table 4.7 Actual experiment for Bayesian D-optimality along with the test re...
Table 4.8 Bayesian D-optimality design to augment the prior data with 5 tria...
Table 4.9 Actual experiment for Bayesian D-optimality along with the test re...
Table 4.10 Bayesian D-optimality design to augment the prior data with 6 tri...
Table 4.11 Actual experiment for Bayesian D-optimality along with the test r...
Table 4.12 Analysis of variance.
Table 4.13 Prior data augmentation using 8 trials based on resolution IV.
Table 4.14 Actual design based on the resolution IV fractional factorial des...
Table 4.15 Analysis of variance.
Table 4.16 Fractional factorial design for brass corrosion test.
Table 4.17 Actual performed fractional factorial design.
Table 4.18 Box–Behnken design for corrosion tests.
Table 4.19 Actual experiments for the Box–Behnken design.
Table 4.20 Ten selected trials from Box–Behnken design.
Table 4.21 O1 optimized formulation (using least square regression for LR) a...
Table 4.22 O2 optimized formulation (using ANN for LR) actual test results v...
Table 4.23 Comparison of the optimized formulations with a conventional prod...
Chapter 5
Table 5.1 Test of growth dependence on amino acid combinations.
Table 5.2 Screening mixture experiment design and results (total amino acid ...
Table 5.3 Predicted rank of amino acids combinations and corresponding bioma...
Table 5.4 Experimental design and results for four component mixture experim...
Table 5.5 ANOVA table for the improved model.
Table 5.6 Verification of the regression model (total amino acid concentrati...
Chapter 6
Table 6.1 List of variables measured per batch.
Table 6.2 Fourfold cross-validation of data set.
Table 6.3 Quality measurements of batches 517 and 629.
Table 6.4 Operational boundaries of latent variables.
Table 6.5 Quality measurements of batch 609.
Chapter 7
Table 7.1 The mean values of fiber width, fiber length, and fiber aspect rat...
Table 7.2 TGA analysis summary of fiber fractions.
Table 7.3 Chemical analysis summary of fiber fractions.
Table 7.4 The mean values of fiber width, fiber length, and aspect ratio of ...
Table 7.5 The results of impact strength measurement of composites made of d...
Chapter 8
Table 8.1 Systematic representation of the study of WS-PP composite system....
Table 8.2 The design points of WS-PP/ICP mixture experimental design.
Table 8.3 ANOVA test results summary for WS-PP/ICP property models.
Table 8.4 Composite property models obtained from the designed WS-PP/ICP exp...
Table 8.5 List of product specifications used as the targets of WS-PP/ICP fo...
Table 8.6 Flexural and impact properties comparison matrix between composite...
Table 8.7 The value of parameter estimates
β
for composite property mod...
Table 8.8 Unit price of composite components used in WS-PP/ICP composite for...
Table 8.9 Optimum proportions of WS-PP/ICP composite.
Chapter 9
Table 9.1 Comparison of the mass heat values and prices of chemicals used in...
Table 9.2 Price comparisons of pure gasoline and other chemicals used in thi...
Table 9.3 Comparison of the prices of blends used in this case study.
Table 9.4 The summary of the blends metrics.
Table 9.5 Random index (RI) used in analytical hierarchy process (AHP).
Table 9.6 The result of weighting and ranking of factors.
Table 9.7 The total impacts and price of each blend for cost-KPI analysis.
Chapter 10
Table 10.1 Electrochemical corrosion parameters obtained from potentiodynami...
Table 10.2 Electrochemical corrosion parameters obtained from equivalent cir...
Chapter 11
Table 11.1 The results of the variance between the predicted and desired val...
Table 11.2 The results of the variance between the predicted and desired val...
Table 11.3 Comparison of the cost and the weight fraction between the cost f...
Chapter 12
Table 12.1 Model optimal solution.
Table 12.2 Effect of BCG distribution on model solution.
List of Illustrations
Chapter 1
Figure 1.1 The design process for product design.
Figure 1.2 Chemical product design (CAMD, CAM
b
D) are “reverse” of property p...
Figure 1.3 A simplified framework for computer-aided chemical product design...
Chapter 2
Figure 2.1 Illustration of coupling mechanism of cellulose fiber and maleic ...
Figure 2.2 Schematic presentation of composite system and composite modeling...
Chapter 3
Figure 3.1 The sequential nature of CCD.
Figure 3.2 Face-centered composite design
.
Figure 3.3 Architecture of a hidden layer feed-forward neural network
.
Figure 3.4 Simplex coordinate system for a three-component mixture.
Figure 3.5 Examples of a [
q
,
m
] simplex lattice design.
Figure 3.6 Response surface and contour plot of a response over a three-comp...
Figure 3.7 A process-mixture designs with a Three-component mixture design a...
Figure 3.8 Unfolding batch data from 3D to 2D.
Figure 3.9 NIPALwS flowchart for PLS [26]/with permission of John Wiley and ...
Chapter 4
Figure 4.1 Box–Cox transformation for the microbial data.
Figure 4.2 Normal probability plot of the residuals.
Figure 4.3 Plot of residuals versus observation order.
Figure 4.4 Measured log reduction versus calculated.
Figure 4.5 The predicted log reduction versus the actual log reduction for t...
Figure 4.6 Number of optimal trials.
Figure 4.7 Comparison of measured peroxide loss versus calculated peroxide l...
Figure 4.8 Comparison of measured peroxide loss versus calculated peroxide l...
Figure 4.9 Training and test data sets for ANN.
Chapter 5
Figure 5.1 Outline of the technique used for design and optimization of medi...
Figure 5.2 The effect of total amino acid concentration on biomass productio...
Figure 5.3 Amino acid concentrations in fresh and first supernatant.
Figure 5.4 Percent variability of distance-based multivariate response matri...
Figure 5.5 rHuIL-3 production versus biomass.
Figure 5.6 Contour plot of rHuIL-3 production (mg/L) over simplex region. So...
Chapter 6
Figure 6.1 Process flow diagram.
Figure 6.2 Evolution of reactor weight during five stages of reaction.
Figure 6.3 Evolution of reactor process variables during five stages of reac...
Figure 6.4 Evolution of pre-weighed Tank process variables during five stage...
Figure 6.5 Cumulative R-squared value.
Figure 6.6 Cumulative PRESS value.
Figure 6.7 Projection of the first pair of the latent variables from batch 5...
Figure 6.8 (a) Projection of process variable 1 for batches 517 and 609, (b)...
Chapter 7
Figure 7.1 Results of fiber length and width measurements of fiber factions....
Figure 7.2 The summary of size measurements of fiber fractions. The dots rep...
Figure 7.3 Pictures of fiber fractions.
Figure 7.4 Degradation temperatures of fiber fractions.
Figure 7.5 Cellulose, hemicelluloses, and lignin content of fiber fractions....
Figure 7.6 Mean plot of fiber length and fiber width before and after compou...
Figure 7.7 Mean plot of fiber length and aspect ratio before and after compo...
Figure 7.8 Flexural modulus of WS-PP composite samples made of different siz...
Figure 7.9 Impact test results of composite samples made of different fiber ...
Figure 7.10 The density of composite made of different fiber sizes. The bars...
Figure 7.11 Relative specific modulus of composite samples compared to pure ...
Figure 7.12 The graph plot of specific modulus versus impact strength of WS-...
Chapter 8
Figure 8.1 Illustration of product design framework for wheat straw polyprop...
Figure 8.2 Plot of design space and design points on pseudo-simplex lattice ...
Figure 8.3 Contour plot of standard error of the design of experiment.
Figure 8.4 Contour plot of flexural modulus (MPa) of WS-PP/ICP composite wit...
Figure 8.5 Contour plot of izod impact strength (J/m) of WS-PP/ICP composite...
Figure 8.6 Overlay plot of model simulation with constraints required by spe...
Figure 8.7 Overlay plot of model simulation with constraints required by spe...
Figure 8.8 Overlay plot of model simulation with constraints required by spe...
Figure 8.9 Flexural modulus of WS-PP composite samples made of different siz...
Figure 8.10 Impact test results of composite samples made of different fiber...
Figure 8.11 Flexural modulus and izod impact strength of WS-PP/ICP blend sam...
Figure 8.12 Contour plot of objective function values (in $/m
3
).
Chapter 9
Figure 9.1 Comparing the octane number of gasoline and MeOH, EtOH, and isooc...
Figure 9.2 Comparison of prices among gasoline blends.
Figure 9.3 Modeling of gasoline and methanol blends with HYSYS 2006.
Figure 9.4 The comparison of impacts of blends on the environment.
Figure 9.5 Safety risk index for methanol, ethanol, and isooctane blends.
Figure 9.6 Employing AHP methodology for selection of sustainable gasoline b...
Figure 9.7 Cost–benefit (KPI) analysis of three blends.
Figure 9.8 Cost-KPI analysis of three blends ignoring risk to safety.
Chapter 10
Figure 10.1 Schematic description of the process for the synthesis of PEI/G ...
Figure 10.2 SEM images for bare copper substrate and PEI-coated copper subst...
Figure 10.3 SEM images for PEI/G
0.5
and PEI/G
1
before (a and c) and after (b...
Figure 10.4 SEM images for graphene dispersion in (a and c) PEI/G
0.5
and (b ...
Figure 10.5 TEM images for graphene dispersion in (a and b) PEI/G
0.5
and (c ...
Figure 10.6 SEM images of PEI/G
2
-coated copper.
Figure 10.7 SEM image of post-adhesion test copper substrates coated with PE...
Figure 10.8 SEM image of post-adhesion test copper substrates coated with PE...
Figure 10.9 SEM image of post-adhesion test copper substrates coated with PE...
Figure 10.10 SEM image of post-adhesion test copper substrate coated with PE...
Figure 10.11 Tafel plots for (a) bare copper, (b) PEI, (c) PEI/G
0.5
, and (d)...
Figure 10.12 Equivalent circuit used for modeling electrochemical impedance ...
Figure 10.13 Nyquist plots for (a) bare copper, (b) PEI, (c) PEI/G
0.5
, and (...
Figure 10.14 Bode plots for (a) bare copper, (b) PEI, (c) PEI/G
0.5
, and (d) ...
Figure 10.15 Representation of tortuous paths as corrosive agents pass throu...
Chapter 11
Figure 11.1 Systematic modeling and optimizing product design.
Figure 11.2 Surface plots of tensile modulus and oxygen permeation models of...
Figure 11.3 Plotting the result of the cost versus tensile modulus at differ...
Figure 11.4 Plotting the result of tensile modulus versus (a) weight fractio...
Figure 11.5 The result of the cost versus tensile modulus at different value...
Figure 11.6 Comparison of the variance and cost function versus tensile modu...
Figure 11.7 Plotting the result of tensile modulus versus (a) weight fractio...
Chapter 12
Figure 12.1 The BCG Business Portfolio Matrix.
Figure 12.2 A simplified network of processes in the model.
Figure 12.3 Optimal solution BCG matrix.
Guide
Cover
Table of Contents
Title Page
Copyright
Preface
About the Authors
Begin Reading
Index
End User License Agreement
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Chemical Product Formulation Design and Optimization
Methods, Techniques, and Case Studies
Ali Elkamel
Hesham Alhumade
Navid Omidbakhsh
Keyvan Nowruzi
Thomas Duever
Authors
Prof. Ali Elkamel
University of Waterloo
Department of Chemical Engineering
200 University Avenue West
N2L 3G1 NK
Canada
Prof. Hesham Alhumade
King Abdulaziz University
Chemical and Materials Engineering
21589 Jeddah
Saudi Arabia
Dr. Navid Omidbakhsh
Johnson & Johnson Company
R&D Advanced Sterilization Products
33 Technology Drive
CA
United States
Dr. Keyvan Nowruzi
Johnson & Johnson Company
Associate Research Fellow
33 Technology Drive
CA
United States
Prof. Thomas Duever
Professor of Chemical Engineering and Dean of Engineering
Toronto Metropolitan University
350 Victoria Street
Toronto, ON
M5B 2K3, Canada
Cover Image: Shutterstock
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 Data
A catalogue record for this book is available from the British Library.
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The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at <http://dnb.d-nb.de>.
© 2023 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: 978-3-527-33264-9ePDF ISBN: 978-3-527-68963-7ePub ISBN: 978-3-527-68964-4oBook ISBN: 978-3-527-68962-0
Cover Design ADAM DESIGN, Weinheim, Germany
Preface
Chemical product design is a very important topic in the chemical industry. While commodity chemicals have been the main area for chemical engineering focus in the past several decades, specialty chemicals have been gaining more and more attention in recent years. Therefore, accelerating the development process and optimizing the formulation of chemical products would be of great benefit. With this change already happening in the industry, chemical engineering education and training have not changed enough to train engineers to fill positions in the product design field.
This book aims at providing the reader with a detailed understanding of the product design, related statistical techniques, and optimization, and gives real-life case studies for disinfectant formulations, optimization of defined medium, the formulation of biocomposites, etc. This book can be used as a supplemental textbook for chemical engineering students in a chemical product design course or to R&D product formulation engineers so that they become familiar with the efficient techniques used in developing new formulations. The book contains 11 chapters as follows:
Chapters 1
and
2
: Introduction to the current product design process
Chapter 3
: Background to the related mathematical and statistical techniques
Chapters 4
–
12
: Cases studies
Chapters 1 and 2 introduce the reader to the current methodologies used for designing new products in chemical industries and outlines the disadvantages of the current processes and the need for improvement.
Chapter 3 gives a background about the theories of the methodologies used to accelerate new product development. These methodologies include factorial designs, mixture designs, optimal designs, linear and nonlinear regression analysis, machine learning techniques (i.e. artificial neural networks), and multi-way principal component analysis.
Chapters 4–11 present seven case studies to illustrate the process of product design and its practical implications. The first case study covers optimization of a disinfectant formulation, the second one presents optimization of a defined medium, the third case deals with product improvement in a chemical toner manufacturing process using multivariate modeling, the fourth case presents over two chapters the design of wheat straw polypropylene composites, the fifth case employs simulation to formulate gasoline blends, the sixth case presents the design of a corrosion protection coating using polyetherimide/graphene composites, and finally the seventh case study deals with the optimization of the mechanical properties of polypropylene-organically modified montmorillonite (PP-OMMT) nanocomposites. The book ends with Chapter 12 that illustrates how to proceed in selecting products to invest for business sustainability.
All chapters are equipped with clear illustrations, figures, and tables to help the reader understand the included topics.
Many people contributed directly or indirectly to this book. We wish to pay our gratitude and our respects to the late Professor Park Reily with whom we have collaborated on research articles related to the topics in this book and have learned a great deal from him. Also, this book would not have been possible without the interactions we had with past graduate students. Although we give credit and references in the appropriate chapters, we would like to vouch our words of appreciation to Rois Fatoni, Hossein Ordouei, Youssef Al Herz, and Hassan Khorami. Special thanks go also to the Wiley publishing team (Elke Maase, Katherine Wong, and Lesley Jebaraj) for their professional work and for being patient with us. Last but not least, we extend great appreciation to our friends and families.
About the Authors
Ali Elkamel is Professor of Chemical Engineering at the University of Waterloo. He is also cross-appointed in Systems Design Engineering. Prof. Elkamel holds a BSc in Chemical Engineering and a BSc in Mathematics from Colorado School of Mines, MSc in Chemical Engineering from the University of Colorado-Boulder, and PhD in Chemical Engineering from Purdue University – West Lafayette, Indiana. His specific research interests are in computer-aided modelling, optimization, and simulation with applications to energy production planning, carbon management, sustainable operations, and product design. Prof. Elkamel supervised over 90 graduate students (of which 35 are PhDs) and more than 30 post-doctoral fellows/research associates, and his trainees all obtain good jobs in the chemical process industry and in academia. He has been funded for several research projects from government and industry. Among his accomplishments are the Research Excellence Award, the Excellence in Graduate Supervision Award, the Outstanding Faculty Award, the Best Teacher Award, and the Industrial engineering and Operations Management (IEOM) Outstanding Service and Distinguished Educator Award. He has written more than 370 journal articles, 145 proceedings, and 45 book chapters and has been an invited speaker on numerous occasions at academic institutions throughout the world and at national and international conferences. He is also a co-author of five books; two recent books were published by Wiley and entitled Planning of Refinery and Petrochemical Operations and Environmentally Conscious Fossil Energy Production.
Hesham Alhumade is a skilled engineer with experience in chemical industry and enthusiastic assistant professor of chemical and material engineering with extensive research, teaching, supervision, and administration experience. He is meticulous and methodical in approach to all tasks, guaranteeing high-quality results in line with learning specifications. Dr. Alhumade was recently appointed as the president of the chemical engineering chapter of the Saudi Council of Engineers. His research interests include polymer nanocomposites, renewable energy, catalyst, solar systems, and fuel cell. He is currently working on developing pyrolysis techniques for biomass conversion to biofuel to meet the growing global demand for alternative and green sources of energy in addition to the current industrial demand for adequate waste management process. In oil and gas industry, he has conducted promising research in the field of synthesis and functionalization of catalyst for hydrocarbon conversion and oil upgrading purposes. His research interests include modeling and simulation of fluid dynamics in porous media and synthesis of nanocomposites materials for various electrochemical applications including fuel cells, supercapacitors, batteries, and corrosion mitigation. Dr. Alhumade received the SABIC Distinguished Award in 2006.
Navid Omidbakhsh is Director of Early R&D and Advanced Research for Advanced Sterilization Products (ASP), where he leads the innovation and technical feasibility of new concepts for future products. Prior to joining ASP, Navid was Vice President of Open Innovation and Intellectual Property for Virox Technologies and held a key role in the development of Virox's globally registered products and company's exponential growth. Before Virox, Navid was an R&D engineer for Henkel in surface technology field. Navid has earned his PhD in chemical engineering from the University of Waterloo, Waterloo, Ontario, Canada, where his main research area was on the development of a systematic method to optimize chemical products/formulations. Navid holds several patents and peer-reviewed publications in the area of product design, disinfectants, and sterilization formulations and systems. Navid is also an alumnus of Harvard Business School, where he completed programs on business, management, and innovation. He is also a licensed professional engineer of Ontario, Canada.
Keyvan Nowruzi is a principal scientist at ASP. He has a BS in chemical engineering from Sahand University of Technology, Tabriz, Iran; an MSc in chemical engineering from Tehran Polytechnic University; and a PhD in biochemical engineering from the University of Waterloo, Canada. Prior to joining ASP, he has served as a post-doctoral fellow at the University of Guelph, Canada for four years and a staff scientist for Akkim Kimya San. Ve Tic. A. Ş. for one year. He has been with ASP for six years. Dr. Nowruzi has contributed in few inventions patented worldwide and has several publications in peer-reviewed journals and international conferences.
Thomas Duever is Dean of the Faculty of Engineering and Architectural Science and a professor of chemical engineering at Toronto Metropolitan University (TMU). Prior to his role at TMU, Dr. Duever served as chair in the Department of Chemical Engineering at the University of Waterloo for nine years, navigating the department toward unprecedented growth. He has also taught industrial short course in experimental design and polymer reaction engineering.
Dr. Duever is an accomplished researcher with interests including applied statistics, experimental design, polymer reaction engineering, and product development. He has written more than 100 articles in journals and conference proceedings to his credit and has supervised the research projects of over 35 graduate students.
Dr. Duever is a registered professional engineer in the Province of Ontario, a fellow of the Chemical Institute of Canada, and a fellow of the Canadian Academy of Engineering. He holds PhD, masters, and bachelor degrees in chemical engineering from the University of Waterloo.
1Introduction
1.1 Chemical Product Engineering
Current globalization trends have resulted in a fierce competition between multinational companies for gaining more market share. Startup companies, on the other hand, also try to play in this game by offering differentiated or disruptive products that would potentially change the game and dynamics in each market segment. The main tool for technological companies to compete, however, remains their product offerings, and how they can serve the customers and address their needs. Any profitable market invites new entrants which creates competition. Companies try to accelerate their product development processes to launch more differentiated products to stay ahead of the game, while even reducing their costs. This is of course not a trivial task for scientists and engineers to take on. Furthermore, customers nowadays have been poised to see newer products and can quickly switch to other companies with better product offerings if the “newer” products are not commercialized quick enough, as the life cycle of the current products keeps becoming shorter. Brand loyalty does not exist as it used to be a few decades ago, and customers can quickly switch if they find a product with better features. An obvious example is the smartphone market, and that companies fiercely compete to introduce new products every year. Imagine one of the incumbents misses one product launch by a few months, and how catastrophic financial outcome they can encounter. In many cases, these new products are only simple modifications to existing technologies, but even these “small modifications” should carry enough value proposition to convince buyers among all choices they have. This competition is of course not limited to electronics market and is widespread in all industries, from cosmetics to pharmaceuticals and consumer to agriculture. In all these market segments, research and development teams work closely with their marketing counterparts to identify market needs and trends to stay ahead of the curve. There is no exaggeration to say that in the current market, innovation is like oxygen for the business, and without that any business will soon become irrelevant. Naturally, innovation can only be monetized if it is translated into a new product and capture revenue. This is why freshness index, i.e. the ratio of new products contributing to the revenue of the company over total revenue, is considered as a key success metric for most companies. A faster commercialization cannot be achieved without a lean and agile product development process, and therefore it is very important that companies spend their R&D dollars very wisely and try to avoid less efficient development methodologies.
Product design can have various interpretations, among them is the definition as the entire procedures required to deliver a product with defined properties that serve a specific need in society or industry based on inputs from various segments. For instance, inputs from the industry of how the product may serve and what specifications should be considered during the manufacturing process. Items that can be considered include environmental and regional regulations. An example of environmentally friendly product design is the manufacturing of a greenhouse ventilation system, where the house is designed to attenuate energy consumption and maintain required rate of fresh air exchange. In such a process of product design of a household air exchanger, various elements need to be considered including heat and humidity. In addition, material selection is a significant factor in the manufacturing of such a device to take into consideration environmental impacts such as energy conservation, corrosion, and exhaust gases, if any. The topic of product design has become even more important with the growing changes in industry and regulation to protect the environment. For example, the manufacturing process of synthetic textile fiber has been continuously developing since 1950. Starting wth a global production of less than 10 million mt in the 1950 and undergoing a 10-fold increase by 2017, the effective utilization of fibers in various applications was achieved through product design studies that were caried out on the development of various prototypes utilizing statistical software packages. In general, the process of product design encompasses the following steps: market needs, ideas, material selection, and finally manufacturing and process control and optimization.
Many of the products we touch and feel today have come out of a chemical plant one way or another. These products cannot be missed even in any quick visit to a grocery store. Consumer products (e.g. detergents), cosmetics, health care products (e.g. disinfectants, sanitizers), adhesives, pharmaceuticals, etc., are all examples of chemical products. Therefore, chemical product design (CPD) is a very important market segment and deserves enough attention in improving product development methodologies. Chemical product engineering is the science and art of creating chemical products, a much larger concept encompassing CPD. In other words, chemical product engineering can be seen as the general background of knowledge and practice supporting the concrete task of designing chemical products and their manufacturing processes.
1.2 Chemical Product Design
One of the crucial challenges facing modern corporations and industry is the growing competitive and dynamics market. A successful business requires continuous monitoring of consumers' needs and delivering valuable products at competitive prices and high quality, while addressing environmental regulations. Therefore, researchers from various fields of industry including but not limited to management, marketing, and engineering design always devote attention to development of new products and issues associated with the fabrication of the products such as environmental concerns. When designing a new product, different factors are usually combined such as strategic and technical effort. Here, strategic planning is required to deliver a successful launch of the product, while technical effort focuses on design, manufacturing, control, and process optimization aspects. Therefore, a growing number of researchers from different fields of engineering including chemical engineering have devoted attention to the area of efficient design of new products.
Specialty chemical products include petrochemicals, pharmaceuticals, green chemicals, food products, household care consumables, and cosmetics. In different sectors, chemical products are undergoing continuous changes to meet the expectations of the consumers in addition to continuously stricter environmental requirements. The fabrication of a chemical product is a multistage process starting from synthesis, design, optimization, operation, and control. The successful execution of the previous steps would transform raw materials into valuable products. Furthermore, the design of a chemical product requires deep understanding of the properties of the materials and usage functions. Chemical products can be classified into six categories as follows: specialty chemicals, bioproducts, formulated products, devices, technology-based products, and virtual chemicals, where each category has a special identity. For example, specialty chemicals can be defined as pure compounds that are delivered in small quantities and may serve specific functions. Formulated products such as cosmetics and food represent a large market and can be defined as combined systems where various raw materials are blended together to deliver a multifunctional product with specific appearance and properties. Continued development in health care applications triggers the need to develop bioproducts that include biomaterials, tissue, and metabolic elements. Most of pharmaceutical drugs are now derived from biological sources rather than traditional synthetic chemicals. Moreover, products that cannot be classified as pure compounds, mixture, or fabricated biomaterials may include devices that carry out a physical or chemical transformation.
There have been major changes in the chemical industry during the last two decades. The dominance of commodity chemicals has been eroded by a newer emphasis on products such as specialty chemicals [1]. These chemicals include but are not limited to detergents, cosmetics, pharmaceutical drugs, fertilizers, adhesives, and many more. Today, there are many companies and industries that have focused on developing such products and are in fierce competition with each other for market share. Chemical process industries have always launched successful new products. However, the dynamic and demanding markets require companies to adopt a more systematic approach to bring the new product to the market faster and cheaper to guarantee competitiveness. Chemical Product Design and Engineering is becoming more important as a consequence of this change. While customer needs and product differentiation for competition purposes are significant drivers to faster develop products, global warming and climate change require newer products to have less environmental impact. Increased awareness by both people and governments, and media's increased attention to this important topic, has led governments to impose more stringent environmental regulations which puts even more pressure on companies to try to reduce waste and carbon footprint. It would be obvious for companies to try to optimize processes and product formulations to deliver the same performance using “less” chemicals in a faster time and using less resources. The million-dollar question to ask is how to achieve this, or simply how to do more with less? In this book, we are trying to answer this question partially and our focus will be on chemical and biological product mixtures.
In summary, the dynamic nature of the chemical and biochemical industries, intense competition for market share, and emergence of more strict environmental regulations require deployment of innovative product development methods to address increasing demands for faster, leaner, and optimized products.
1.3 Product Design and Computer-Aided Product Design
CPD can be defined as a systematic procedure or framework of methodologies and tools whose aim is to provide a more efficient and faster design of chemical products able to meet market demands. From the practical standpoint, Cussler and Moggridge [2] simply defined product design as a procedure consisting of four steps: (i) defining the needs, (ii) generating ideas to meet the needs, (iii) selecting the best ideas, and (iv) manufacturing the product. Generating ideas and selecting the best ideas are the most time-consuming steps. These two steps traditionally involved an exhaustive search by trial-and-error methods which often ended up with no significant results. One way to overcome this problem is by using computer-aided techniques to identify very quickly a set of promising candidates and select a subset of likely final products, from which the desired properties can be identified through experiments (Figure 1.1).
The first step in Figure 1.1 is the predesign, or problem formulation step. Steps 2 and 3 represent, respectively, two types of product design problems: molecular design and mixture/blend design. In the molecular design, the objective is to find a chemical product that exhibits certain functional properties. The invention of new fuel additives and solvents in organic synthesis are examples of this type of design. In the mixture/blend design, the objective is to find a recipe of chemical ingredients which give desirable final product properties. Examples of this type of design are the design of fuel blends and polymer blends, including polymer composites and additives. The associated computer-aided designs for the two CPDs are called computer-aided molecular design (CAMD) and computer-aided mixture/blend design (CAMbD).
Chemical products are judged by consumers not from their technical specifications but rather by the functional and performance attributes which are usually described by a set of performance indices. These indices are determined by three factors: (i) the composition and physicochemical properties of materials that constitute the product; (ii) product structure, which is dependent on the manufacturing process; and (iii) product usage conditions. The relationship between performance indices and product composition, product ingredients' properties, and product structure has been mathematically systematized through the concept of property function. In generic terms, the CPD can be defined as: given a set of desired (target) needs, determine a chemical product (molecule or mixture) that satisfies these needs. Based on this definition and the concept of property function, the CPD problem can be described as a “reverse property prediction,” as illustrated in Figure 1.2, where the needs are defined through product properties [3].
Figure 1.1 The design process for product design.
Figure 1.2 Chemical product design (CAMD, CAMbD) are “reverse” of property prediction problems.
A simple framework for CPD is illustrated in Figure 1.3. Different aspects of CPD are represented by methods for CAMD, CAMbD, analysis, and model validation, while different calculation options are represented by tools of process simulation, pure component property estimation, mixture property estimation, and search engines for data retrieval from databases. Although the two-directional arrows in Figure 1.3 show the connection between two adjacent methods or tools, they are meant to indicate that all the tools and methods are connected to each other.
In any CPD problem, property functions and property models play important roles. While the framework is flexible enough to handle a large range of CPD problems, the currently available methods and tools can only solve a relatively small percentage of these problems. This is because the property models that are currently available are unable to predict the needed properties within an acceptable limit of uncertainty.
Figure 1.3 A simplified framework for computer-aided chemical product design.
The framework, however, can give a great contribution to creating property models and database development in a systematic way. This will reduce time and effort in the early stages of the product design process and subsequently bring the product to the market cheaper and faster.
The remainder of this book is organized as follows: Chapter 2 surveys a variety of applications associated with CPD, while Chapter 3 covers tools commonly used to accelerate product development. Chapters 4–12 provide illustrative case studies related to CPD and formulation.
References
1
Lee, N.-J. and Jang, J. (1997). Performance optimisation of glass fibre mat reinforced polypropylene composites using statistical experimental design.
Polym. Test.
16: 497–506.
2
Cussler, E.L. and Moggridge, G.D. (2011).
Chemical Product Design
. Cambridge University Press. ISBN: 9781139035132.
3
Halvarsson, S., Edlund, H., and Norgren, M. (2008). Properties of medium-density fibreboard (MDF) based on wheat straw and melamine modified urea formaldehyde (UMF) resin.
Ind. Crops Prod.
28: 37–46.
2Some Typical Applications of Chemical Product Design and Intellectual Property
2.1 Natural Fiber Plastic Composites
The use of natural fibers as reinforcement in composite materials dates back to 3000 years ago when ancient Egyptians used clay reinforced with wheat straw as materials to build walls of their houses. In the automotive industry, Henry Ford developed the first prototype composite car made from hemp fibers in 1942. Due to economic constraints at that time, however, the car was not commercially produced. Since then, numerous attempts have been made to incorporate natural fibers into automotive components. The pressure to produce fuel-efficient, low-polluting vehicles has become the major driving force for the increasing use of natural fibers in automotive parts. The inclusion of natural fibers will make it possible to reduce the utilization of petroleum-based polymeric materials. It will also increase the fuel efficiency due to car's lighter weight and will result in an easier product end-of-life, i.e. waste management. Today, several car manufacturers are using natural fiber composites in their products. Some examples of the applications are presented in Table 2.1.
Both thermoplastic and thermoset resins were being used in automotive industries. However, since thermoplastic resins are easily recyclable, they exhibit less environmental impact than the thermoset resins. Therefore, industries such as automotive industry is using more thermoplastics than thermosets. For automotive industry, the key advantage of thermoplastics is that they can be reprocessed or recycled, thus reducing the amount of scrap material during manufacturing and allowing easy recovery and recycling of materials at the end-of-life cycle. Due to the lower thermal stability of natural fibers, the number of thermoplastics which can be used to make composite materials is limited to those thermoplastics with processing temperatures that do not exceed the temperature for degradation or burning the plant fibers (typically below 210 °C). Polypropylene (PP) and polyethylene are the most commonly used thermoplastic polymer matrices with plant natural fibers.
There are various natural fibers with broad ranges of sizes and properties available to be used as fibers in composites, such as cotton, jute, flax, hemp, sisal, coir, bamboo, wood, pineapple, ramie, coconut leaves, and so on. The choice of fibers mainly depends on the final composite product specifications and their application. However, flax, hemp, and kenaf fibers are favored, because they have excellent combinations of economic and functional properties [1].
Table 2.1 Automotive manufacturers, model, and components using natural fibers [1].
Manufacturer
Model and application
Audi
A2, A3, A4, A4Avant, A6, A8, Roadstar, Coupe: Seat back, side and back door panel, boot lining, hat rack, spare tire lining
