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E-raamat: Digital Transformation of Product Formulation: Concepts, Challenges, and Applications for Accelerated Innovation [Taylor & Francis e-raamat]

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  • Formaat: 349 pages, 42 Tables, black and white; 43 Line drawings, black and white; 70 Halftones, black and white; 113 Illustrations, black and white
  • Ilmumisaeg: 14-Aug-2024
  • Kirjastus: CRC Press
  • ISBN-13: 9781003385974
Teised raamatud teemal:
  • Taylor & Francis e-raamat
  • Hind: 170,80 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 244,00 €
  • Säästad 30%
Digital Transformation of Product Formulation: Concepts, Challenges, and Applications for Accelerated InnovationSuurem pilt
  • Formaat: 349 pages, 42 Tables, black and white; 43 Line drawings, black and white; 70 Halftones, black and white; 113 Illustrations, black and white
  • Ilmumisaeg: 14-Aug-2024
  • Kirjastus: CRC Press
  • ISBN-13: 9781003385974
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781003385974

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.



In competitive manufacturing industries, organizations embrace product development as a continuous investment strategy since both market share and profit margin stand to benefit. Formulating new or improved products has traditionally involved lengthy and expensive experimentation in laboratory or pilot plant settings. However, recent advancements in areas from data acquisition to analytics are synergizing to transform workflows and increase the pace of research and innovation. The Digital Transformation of Product Formulation 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 combinations of ingredients that result in targeted quality properties.

• Introduces product development and predictive modeling, details hardware advancements affecting conventional R&D lab workflows, and covers common characteristics of experimental datasets and challenges in using this data for predictive modeling.

• Discusses issues and solutions applicable to a variety of industries including chemicals, polymers, pharmaceuticals, oil and gas, and food and beverages.

• Addresses effective strategies for enhancing a dataset with advanced formulation information and ingredient characterization.

• Covers two distinct approaches to developing predictive models on formulation data: multivariate analysis and machine learning methods.

• Discusses inverse design via optimization and Bayesian optimization as natural extensions to predictive modeling.

• Features several complete datasets among numerous case studies, with the aim of educating readers and encouraging benchmarking of current and future solution approaches.

This book provides students and professionals from engineering and science disciplines with practical-know how in product development in the context of chemical products, across the entire modeling lifecycle.

1. Introduction.
2. The Digital Transformation of R&D.
3. Challenges with Formulation.
4. Advanced Formulation/ingredient Characterization.
5. Challenges in Characterizing Chemical Formulations & Their Ingredients.
6. Product Formulation Predictive Modeling with the Multivariate Analysis Approach.
7. Product Formulation Predictive Modeling with Transfer Learning Approaches.
8. Inverse Design via Optimization.
9. Bayesian Optimization.
10. Established and Emerging Use-Cases.
11. The Future of Product Formulation.
Alix Schmidt is a senior data scientist in Dows Core R&D Information Research team in Midland, Michigan. Alix earned a BS in chemical engineering at the University of Illinois UrbanaChampaign 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.
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