Carbon dioxide adsorption performance of KOH-activated potato straw hydrochar: RSM and ML optimization, and mechanism insights

Carbon dioxide adsorption performance of KOH-activated potato straw hydrochar: RSM and ML optimization, and mechanism insights | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Carbon dioxide adsorption performance of KOH-activated potato straw hydrochar: RSM and ML optimization, and mechanism insights Zhanpeng Huo, Zifeng Sui, Zifeng Zhang, Qianmei Lyu, Wenjie Hu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6948930/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Nov, 2025 Read the published version in Chemical Papers → Version 1 posted 5 You are reading this latest preprint version Abstract KOH-activated hydrochar (HC) prepared using biomass straw serves as a promising carbon dioxide (CO 2 ) adsorbent. However, numerous factors influence its preparation process, leading to significant variability in the adsorption performance of the resulting products. However, thoroughly understanding and optimizing this process remains challenging. In this study, a response surface method (RSM) and machine learning model were applied to predict the CO 2 adsorption capacity of potato straw active HC and analyze the influence of multiple factors on the CO 2 adsorption capacity. The results showed that the random forest (RF) model had a good predictive effect on the CO 2 adsorption effect of active HC, compared with the RSM model, based on the central composite design experimental dataset and additional experimental data. In addition, the hydrothermal carbonization temperature had the most significant influence on the CO 2 adsorption performance among various preparation factors, based on analysis of the RSM and RF models. Through experimental characterization and analysis, we found that with an increase in hydrothermal carbonization temperature, more biomass components would be decomposed, with the degree of carbonization increasing accordingly, forming a stable aromatic structure. Subsequently, more micropores would be generated following KOH activation, thus increasing the CO 2 adsorption performance of active HC. Response Surface Method Machine Learning Hydrochar KOH Potato Straw Full Text Cite Share Download PDF Status: Published Journal Publication published 01 Nov, 2025 Read the published version in Chemical Papers → Version 1 posted Editorial decision: Minor revisions 09 Sep, 2025 Reviewers agreed at journal 25 Aug, 2025 Reviewers invited by journal 24 Aug, 2025 Editor assigned by journal 24 Jun, 2025 First submitted to journal 22 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6948930","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504885313,"identity":"45bd569d-bd96-425e-a55d-43746378e028","order_by":0,"name":"Zhanpeng Huo","email":"","orcid":"","institution":"Inner Mongolia University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhanpeng","middleName":"","lastName":"Huo","suffix":""},{"id":504885314,"identity":"f654af59-4210-4530-8132-da8eab01ba82","order_by":1,"name":"Zifeng 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However, numerous factors influence its preparation process, leading to significant variability in the adsorption performance of the resulting products. However, thoroughly understanding and optimizing this process remains challenging. In this study, a response surface method (RSM) and machine learning model were applied to predict the CO\u003csub\u003e2\u003c/sub\u003e adsorption capacity of potato straw active HC and analyze the influence of multiple factors on the CO\u003csub\u003e2\u003c/sub\u003e adsorption capacity. The results showed that the random forest (RF) model had a good predictive effect on the CO\u003csub\u003e2\u003c/sub\u003e adsorption effect of active HC, compared with the RSM model, based on the central composite design experimental dataset and additional experimental data. 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