Bayesian Optimization in Bioprocess Engineering -Where do we stand today?

Bayesian Optimization in Bioprocess Engineering -Where do we stand today? | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 August 2024 V1 Latest version Share on Bayesian Optimization in Bioprocess Engineering -Where do we stand today? Authors : Florian Gisperg 0009-0000-2002-0728 , Robert Klausser , Mohamed Elshazly , Julian Kopp , Eva Přáda Brichtová , and Oliver Spadiut Authors Info & Affiliations https://doi.org/10.22541/au.172322854.40642545/v1 Published Biotechnology and Bioengineering Version of record Peer review timeline 726 views 301 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Bayesian optimization is a stochastic, global black-box optimization algorithm. By combining Machine Learning with decision-making, the algorithm can optimally utilize information gained during experimentation to plan further experiments-while balancing exploration and exploitation. Although Design of Experiments has traditionally been the preferred method for optimizing bioprocesses, AI-driven tools have recently drawn increasing attention to Bayesian optimization within bioprocess engineering. This review presents the principles and methodologies of Bayesian optimization and focuses on its application to various stages of bioprocess engineering in upstream and downstream processing. Supplementary Material File (bayesian_optimization_review.pdf) Download 1.07 MB Information & Authors Information Version history V1 Version 1 09 August 2024 Peer review timeline Published Biotechnology and Bioengineering Version of Record 5 Mar 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords active learning bayesian optimization bioprocess engineering machine learning model-based optimization Authors Affiliations Florian Gisperg 0009-0000-2002-0728 Christian Doppler Laboratory for Inclusion Body Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien View all articles by this author Robert Klausser Christian Doppler Laboratory for Inclusion Body Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien View all articles by this author Mohamed Elshazly Christian Doppler Laboratory for Inclusion Body Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien View all articles by this author Julian Kopp Christian Doppler Laboratory for Inclusion Body Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien View all articles by this author Eva Přáda Brichtová Christian Doppler Laboratory for Inclusion Body Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien View all articles by this author Oliver Spadiut Christian Doppler Laboratory for Inclusion Body Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien View all articles by this author Funding Information Christian Doppler Forschungsgesellschaft Oliver Spadiut Metrics & Citations Metrics Article Usage 726 views 301 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Florian Gisperg, Robert Klausser, Mohamed Elshazly, et al. Bayesian Optimization in Bioprocess Engineering -Where do we stand today?. Authorea . 09 August 2024. DOI: https://doi.org/10.22541/au.172322854.40642545/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); Cited by Eşref Boğar, Damla Nur Kaya, Deniz Kaynar, Büşra Braho, Melek Harmancı, Fırında pişirilen patates kızartmalarında akrilamid oluşumunun optimize edilmiş gauss süreç regresyonu ile modellenmesi, Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 14 , 3, (1088-1099), (2025). https://doi.org/10.28948/ngumuh.1696315 Crossref Loading... View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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