Synergistic optimization framework for the process synthesis and design of biorefineries

作     者：Nikolaus IVollmer Resul Al Krist V.Gernaey Gürkan Sin Nikolaus I.Vollmer;Resul Al;Krist V.Gernaey;Gürkan Sin

作者机构：Process and Systems Engineering(PROSYS)Research CenterDepartment of Chemical and Biochemical EngineeringTechnical University of Denmark2800 Kgs.LyngbyDenmark Novo Nordisk A/S2880 BagsværdDenmark 

出 版 物：《Frontiers of Chemical Science and Engineering》 (化学科学与工程前沿（英文版）)

年 卷 期：2022年第16卷第2期

页      面：251-273页

核心收录：

学科分类：081702[工学-化学工艺] 08[工学] 0817[工学-化学工程与技术] 

基　　金：The authors would like to express their gratitude to the Novo Nordisk Foundation(Grant No.NNF17SA0031362)for funding the Fermentation-Based Biomanufacturing Initiative of which this project is a part 

主　　题：biotechnology surrogate modelling superstructure optimization simulation-based optimization process design 

摘      要：The conceptual process design of novel bioprocesses in biorefinery setups is an important task,which remains yet challenging due to several *** propose a novel framework incorporating superstructure optimization and simulation-based optimization *** this context,several approaches for superstructure optimization based on different surrogate models can be *** means of a case study,the framework is introduced and validated,and the different superstructure optimization approaches are *** results indicate that even though surrogate-based optimization approaches alleviate the underlying computational issues,there remains a potential issue regarding their *** development of appropriate surrogate models,comprising the selection of surrogate type,sampling type,and size for training and cross-validation sets,are essential *** this aspect,satisfactory validation metrics do not ensure a successful outcome from its embedded use in an optimization ***,the framework’s synergistic effects by sequentially performing superstructure optimization to determine candidate process topologies and simulationbased optimization to consolidate the process design under uncertainty offer an alternative and promising *** findings invite for a critical assessment of surrogatebased optimization approaches and point out the necessity of benchmarking to ensure consistency and quality of optimized solutions.