Polymer Science Series D, Journal Year: 2024, Volume and Issue: 17(4), P. 982 - 989
Published: Dec. 1, 2024
Language: Английский
Polymer Science Series D, Journal Year: 2024, Volume and Issue: 17(4), P. 982 - 989
Published: Dec. 1, 2024
Language: Английский
Polymers, Journal Year: 2024, Volume and Issue: 16(23), P. 3368 - 3368
Published: Nov. 29, 2024
The integration of machine learning (ML) into material manufacturing has driven advancements in optimizing biopolymer production processes. ML techniques, applied across various stages production, enable the analysis complex data generated throughout identifying patterns and insights not easily observed through traditional methods. As sustainable alternatives to petrochemical-based plastics, biopolymers present unique challenges due their reliance on variable bio-based feedstocks processing conditions. This review systematically summarizes current applications techniques aiming provide a comprehensive reference for future research while highlighting potential enhance efficiency, reduce costs, improve product quality. also shows role algorithms, including supervised, unsupervised, deep
Language: Английский
Citations
4Polymer Science Series D, Journal Year: 2024, Volume and Issue: 17(4), P. 982 - 989
Published: Dec. 1, 2024
Language: Английский
Citations
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