1st Edition
The Digital Transformation of Product Formulation Concepts, Challenges, and Applications for Accelerated Innovation
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.
Biography
Kristin Wallace has a Bachelor’s Degree in Chemical Engineering and a Master’s Degree in Applied Science with an optimization focus, both from McMaster University in Ontario, Canada. Kristin spent 5 years designing and troubleshooting electric arc furnaces at Hatch before joining ProSensus in 2018, where she has since focused on manufacturing analytics. Kristin applies multivariate analysis to industrial datasets to accomplish a variety of goals including new product development and process analysis.
Alix Schmidt is a data scientist at Dow Chemical specializing in application of statistics and machine learning to chemicals, materials, and processes. She has a B.S. in Chemical Engineering from the University of Illinois at Urbana-Champaign and a M.S. in Data Science from Northwestern University. In her 13 years at Dow, Alix has worked in new product scale-up, high throughput research, manufacturing process analytics, and predictive formulation.
