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Sustainable biopolymer synthesis via superstructure and multiobjective optimization
Author(s) -
del RioChaa Ehecatl Antonio,
Zhang Dongda,
Shah Nilay
Publication year - 2018
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.15877
Subject(s) - biopolymer , biochemical engineering , petrochemical , environmentally friendly , industrialisation , process (computing) , multi objective optimization , biomass (ecology) , raw material , process engineering , computer science , polymer , engineering , materials science , waste management , chemistry , economics , ecology , machine learning , market economy , biology , operating system , composite material , organic chemistry
Sustainable polymers derived from biomass have great potential to replace petrochemical based polymers and fulfill the ever‐increasing market demand. To facilitate their industrialization, in this research, a comprehensive superstructure reaction network comprising a large number of reaction pathways from biomass to both commercialized and newly proposed polymers is constructed. To consider economic performance and environmental impact simultaneously, both process profit and green chemistry metrics are embedded into the multiobjective optimization framework, and MINLP is used to enable the effective selection of promising biopolymer candidates. Through this proposed approach, this study identifies the best biopolymer candidates and their most profitable and environmentally friendly synthesis routes under different scenarios. Moreover, the stability of optimization results regarding the price of raw materials and polymers and the effect of process scale on the investment cost are discussed in detail. These results, therefore, pave the way for future research on the production of sustainable biopolymers. © 2017 American Institute of Chemical Engineers AIChE J , 63: 91–103, 2018