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Multi‐scale modeling for sustainable chemical production
Author(s) -
Zhuang Kai,
Bakshi Bhavik R.,
Herrgård Markus J.
Publication year - 2013
Publication title -
biotechnology journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.144
H-Index - 84
eISSN - 1860-7314
pISSN - 1860-6768
DOI - 10.1002/biot.201200272
Subject(s) - biochemical engineering , scale (ratio) , production (economics) , process (computing) , computer science , industrial ecology , portfolio , petrochemical , sustainability , risk analysis (engineering) , environmental science , ecology , engineering , business , economics , environmental engineering , macroeconomics , operating system , physics , finance , quantum mechanics , biology
With recent advances in metabolic engineering, it is now technically possible to produce a wide portfolio of existing petrochemical products from biomass feedstock. In recent years, a number of modeling approaches have been developed to support the engineering and decision‐making processes associated with the development and implementation of a sustainable biochemical industry. The temporal and spatial scales of modeling approaches for sustainable chemical production vary greatly, ranging from metabolic models that aid the design of fermentative microbial strains to material and monetary flow models that explore the ecological impacts of all economic activities. Research efforts that attempt to connect the models at different scales have been limited. Here, we review a number of existing modeling approaches and their applications at the scales of metabolism, bioreactor, overall process, chemical industry, economy, and ecosystem. In addition, we propose a multi‐scale approach for integrating the existing models into a cohesive framework. The major benefit of this proposed framework is that the design and decision‐making at each scale can be informed, guided, and constrained by simulations and predictions at every other scale. In addition, the development of this multi‐scale framework would promote cohesive collaborations across multiple traditionally disconnected modeling disciplines to achieve sustainable chemical production.

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