The Digital Transformation of Product Formulation

Concepts, Challenges, and Applications for Accelerated Innovation
Herausgeber: Schmidt, Alix; Wallace, Kristin

• Gebundenes Buch

Produktbeschreibung

This book offers practical guidance on how to implement data-driven, accelerated product development through concepts, challenges, and applications. It describes activities related to creating new or improved functional material products by discovering new ingredients or new ingredient combinations resulting in targeted quality properties.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis Ltd (Sales)
• Seitenzahl: 349
• Erscheinungstermin: 14. August 2024
• Englisch
• Abmessung: 234mm x 156mm x 21mm
• Gewicht: 685g
• ISBN-13: 9781032474069
• ISBN-10: 1032474068
• Artikelnr.: 70149878

Autorenporträt

Alix Schmidt is a senior data scientist in Dow's Core R&D Information Research team in Midland, Michigan. Alix earned a BS in chemical engineering at the University of Illinois Urbana-Champaign in 2009 and then joined Dow Corning initially as a process research engineer. Since then, Alix has held a variety of roles at Dow Corning and Dow and completed an MS in data science at Northwestern University. Alix has experience with polymer process research, high-throughput research, machine learning for manufacturing troubleshooting, and data-driven product development. Her interest and experience in materials informatics allow her to lead technical data science strategy at Dow, and she has presented and chaired at the AIChE spring meeting on this topic. Kristin Wallace earned a BS in chemical engineering (2006) and an MS in applied science (optimization focus) (2008) at McMaster University. She has worked on a variety of analytics projects since joining ProSensus Inc. in 2018 as a project engineer in Burlington, Ontario. Her particular interest in product formulation using projection to latent structures (PLS) has led her to be involved with related consulting projects, contributing to the development of FormuSense (commercial software), authoring blogs and magazine articles, as well as presenting and chairing at several AIChE spring meetings. Prior to working at ProSensus, she spent five years designing and troubleshooting non-ferrous electric arc furnaces.

Inhaltsangabe

Section 1: Getting Started. 1. The Digital Transformation of R&D Labs. 2.
Product Formulation Fundamentals. 3. Defining a Successful Predictive
Formulation Project. Section 2: Preparing Your Data. 4. Challenges with
Formulation Datasets. 5. Feature Engineering: Enhancing Your Data with
Descriptors. 6. Machine Learning for Analysis of Structural
Characterization. Section 3: Predictive Modeling. 7. Machine Learning
Techniques for Predicting Properties of Formulations. 8. Modeling of
Product Formulations Using a Latent Variable Approach. 9. Gaining Trust in
Your Model. Section 4: Optimization and Inverse Design. 10. Introduction to
Formulation Optimization. 11. Adaptive Experimental Design. 12. Inverse
Design via PLS Model Inversion. Section 5: Case Studies and Special Topics.
13. Case Studies. 14. Special Topics. 15. Conclusion.