Abstract

There is an immense potential for the computer-aided tools in the design of paints and coatings. Significant advances have been made, involving also the use of thermodynamic and in general property models for the study and theoretical formulation of these products. Algorithms and tools based on such models enable the formulation chemist to speed up the design process, by allowing them to focus their experimental efforts on a selected number of reliable constituents for the coating formulation. Even though model-based methods and tools can save resources and time required for the design, service life prediction and formulation of new products, the experimental validation cannot be done away with; as certain interactions in these complex systems can be accounted for only by using practical design procedures. Machine learning algorithms can, however, be used to improve the accuracy of predictive methods, if sufficient data on observed anomalies from physicochemical based theoretical predictions, is available.
Original languageEnglish
JournalCurrent Opinion in Chemical Engineering
Volume27
Pages (from-to)107-120
ISSN2211-3398
DOIs
Publication statusPublished - 2020

Keywords

  • Computer-Aided Tools
  • Thermodynamics
  • Property Models
  • Algorithms
  • Coating Design

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