TY - JOUR
T1 - A review of computer-aided design of paints and coatings
AU - Jhamb, Spardha
AU - Enekvist, Markus
AU - Liang, Xiaodong
AU - Zhang, Xiangping
AU - Dam-Johansen, Kim
AU - Kontogeorgis, Georgios M
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Computer-Aided Tools
KW - Thermodynamics
KW - Property Models
KW - Algorithms
KW - Coating Design
U2 - 10.1016/j.coche.2019.12.005
DO - 10.1016/j.coche.2019.12.005
M3 - Journal article
SN - 2211-3398
VL - 27
SP - 107
EP - 120
JO - Current Opinion in Chemical Engineering
JF - Current Opinion in Chemical Engineering
ER -