[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122849-en":3,"doc-seo-122849-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122849,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning for surrogate process models of bioproduction pathways - Research article","Technoeconomic analysis and life-cycle assessment guide and prioritize bench-scale experiments and evaluate economic and environmental performance for scaled biofuel and biochemical production. Traditional commercial process simulations are detailed but can be costly and computationally intensive, limiting sharing and reproducibility. This study assesses an automated machine-learning approach to build surrogate models from conventional process simulation models. Results show accurate approximations of cost, mass, and energy-balance outputs with far lower computational expense, enabling broader, faster pathway assessment.","UC Berkeley  \nUC Berkeley Previously Published Works  \nTitle  \nMachine learning for surrogate process models of bioproduction pathways  \nPermalink  \n[https://escholarship.org/uc/item/7wc0h0qq](https://escholarship.org/uc/item/7wc0h0qq)  \nAuthors  \nHuntington, Tyler  \nBaral, Nawa Raj Yang, Minlianget al.  \nPublication Date  \n2023-02-01  \nDOI  \n10.1016/j.biortech.2022.128528  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nBioresource Technology 370 (2023) 128528  \nContents lists available at ScienceDirect  \nBioresource Technology  \njournal [homepage: www.elsevier.com/locate/biortech](homepage: www.elsevier.com/locate/biortech)  \n| Machine learning for surrogate process models of bioproduction pathways   Tyler Huntington a, b, Nawa Raj Barala, b, Minliang Yanga, b, Eric Sundstrom b, c,\u003Cbr>Corinne D. Scown a, b, d, e, *\u003Cbr>a Life-cycle, Economics, and Agronomy Division, Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA b Biosciences Area, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA\u003Cbr>c Advanced Biofuels and Bioproducts Process Development Unit, 5885 Hollis Street, Emeryville, CA 94608, USA d Energy Technologies Area, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA e Energy & Biosciences Institute, University of California, Berkeley, 282 Koshland Hall, Berkeley, CA 94720, USA |  |\n| --- | --- |\n| H I G H L I G H T S\u003Cbr>• Machine learning can be used to develop surrogate models from process simulations.\u003Cbr>• Surrogate models make technoeconomic models more accessible and fast to run.\u003Cbr>• Surrogate models are most useful when further design changes will not be made.\u003Cbr>• Automated design strategies can be complementary to machine learning approaches.\u003Cbr>• Advanced sampling strategies may yield further performance improvements. A R T I C L E I N F O\u003Cbr>Keywords: Biofuels\u003Cbr>Bioproducts Technoeconomic analysis Life-cycle assessment TPOT | G R A P H I C A L A B S T R A C T\u003Cbr>A B S T R A C T\u003Cbr>Technoeconomic analysis and life-cycle assessment are critical to guiding and prioritizing bench-scale experiments and to evaluating economic and environmental performance of biofuel or biochemical production processes at scale. Traditionally, commercial process simulation tools have been used to develop detailed models for these purposes. However, developing and running such models can be costly and computationally intensive, which limits the degree to which they can be shared and reproduced in the broader research community. This study evaluates the potential of an automated machine learning approach to develop surrogate models based on conventional process simulation models. The analysis focuses on several high-value biofuels and bioproducts for which pathways of production from biomass feedstocks have been well-established. The results demonstrate that surrogate models can be an accurate and effective tool for approximating the cost, mass and energy balance outputs of more complex process simulations at a fraction of the computational expense. |\n\n1. Introduction  \nTechnoeconomic analysis and life-cycle assessment are powerful  \nanalytical tools for evaluating novel bioproduction processes, identifying key cost bottlenecks, and drivers of greenhouse gas emissions and other environmental impacts (Mahmud et al., 2021; Scown et al., 2021).  \n* Corresponding author at: Life-cycle, Economics, and Agronomy Division, Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA. E-mail address: [cdscown@lbl.gov](cdscown@lbl.gov) (C.D. Scown).  \n[https://doi.org/10.1016/j.biortech.2022.128528](https://doi.org/10.1016/j.biortech.2022.128528)  \nReceived 29 ","cbCaiexgRAJXatzz","https://ap.wps.com/l/cbCaiexgRAJXatzz","pdf",3954363,1,10,"English","en",105,"# Introduction\n## Technoeconomic and life-cycle assessment in bioproduction\n## Limits of detailed process simulations\n## Rationale for automated surrogate modeling","[{\"question\":\"Why are technoeconomic analysis and life-cycle assessment important for bioproduction?\",\"answer\":\"They help evaluate and prioritize experiments and measure economic and environmental performance for scaled biofuel or biochemical production processes.\"},{\"question\":\"What problem do conventional commercial process simulations create?\",\"answer\":\"They require specialized tools and domain expertise and are costly and computationally intensive, which restricts how widely analyses can be shared and reproduced.\"},{\"question\":\"How does the study use machine learning in this work?\",\"answer\":\"It applies automated machine learning to develop surrogate models based on conventional process simulation models, aiming to approximate key outputs more efficiently.\"}]","Machine learning for surrogate process models of bioproduction pathways - 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