{"paper_id":"02b45b7f-8417-4e0a-ac2d-14d00f5cd463","body_text":"KNN and Lévy Flight-Assisted Optimization of Biological Chrome Recovery from Leather Waste via Pseudomonas aeruginosa | 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 KNN and Lévy Flight-Assisted Optimization of Biological Chrome Recovery from Leather Waste via Pseudomonas aeruginosa Hadjar BELKACEMI, Amel Benhadji, Mourad Taleb Ahmed, Reguia Boudraa, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7547175/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The leather tanning industry is one of the largest consumers of chromium, generating substantial quantities of chromium-laden waste, which poses severe environmental risks. This study explores the biorecovery of chromium from leather waste using the bacterium Pseudomonas aeruginosa through an optimized bioleaching process. Key operational parameters, including initial pH, aeration rate, stirring speed, solid-to-liquid ratio, and glucose dosage, were systematically varied to maximize chromium recovery. Optimal conditions—pH 5, stirring speed of 400 rpm, aeration rate of 3 L/min, solid/liquid ratio of 1/100 g/mL, and a glucose concentration of 2 g—yielded a maximum chromium recovery of 68.65%. Additionally, to model and predict bioleaching outcomes, a machine learning approach was employed, using a K-Nearest Neighbors (KNN) model coupled with the Lévy Flight Distribution (LFD) algorithm. This hybrid model achieved a near-perfect correlation (R = 0.9999) between predicted and actual chromium recovery rates, with a prediction accuracy of 69.1% compared to the experimental value of 68.65%. This study demonstrates not only the viability of eco-friendly bioleaching for chromium waste management but also highlights the potential of machine learning tools in optimizing complex bioremediation processes. Bioleaching Chromium recovery Leather waste Pseudomonas aeruginosa Machine learning K-Nearest Neighbors Lévy Flight Distribution Algorithm Full Text Cite Share Download PDF Status: Posted Version 1 posted 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-7547175\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":513635088,\"identity\":\"c3beaeb2-f2c9-4b10-a214-250513d32d2b\",\"order_by\":0,\"name\":\"Hadjar 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