{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T01:56:32Z","timestamp":1790560592159,"version":"4.1.0"},"reference-count":39,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,8,16]],"date-time":"2026-08-16T00:00:00Z","timestamp":1786838400000},"content-version":"vor","delay-in-days":15,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Water Environment Research"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    This study developed an integrated and uncertainty\u2010aware machine learning framework to predict and optimize methylene blue (MB) removal efficiency using biochar adsorbents. A dataset comprising 504 literature\u2010derived adsorption experiments was compiled using five operational parameters, namely, initial dye concentration, contact time, initial pH, adsorbent dosage, and temperature. Four advanced ensemble learning models, namely, XGBoost, CatBoost, LightGBM, and stacking ensemble, were developed and evaluated. The stacked ensemble model achieved the highest predictive performance with\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u20090.7740, RMSE\u2009=\u20097.95, and MAPE\u2009=\u20097.04%, demonstrating the ability to capture complex nonlinear adsorption processes. Explainable artificial intelligence (XAI) analysis using SHAP identified temperature, contact time, and adsorbent dosage as the most influential variables governing MB removal. Sensitivity analysis further confirmed the significant influence of contact time, temperature, and adsorbent dosage on prediction variability. Two Bayesian optimization strategies were investigated, mathematical optimization, performed over the complete range of the literature\u2010derived dataset, and engineering\u2010constrained optimization, performed within practically feasible operating ranges reported in previous adsorption studies, and both cases reported a maximum predicted removal efficiency of 99.99% with varying input variables. Monte Carlo uncertainty analysis indicated a mean predicted efficiency of 75.05% with a 95% confidence interval of 71.85%\u201379.54%. Residual diagnostics and Bland\u2013Altman analysis demonstrated satisfactory agreement between predicted and observed values within the investigated dataset. Overall, the proposed framework integrates prediction, explainability, uncertainty quantification, and optimization into a unified workflow, providing a promising decision\u2010support tool for biochar\u2010based wastewater treatment.\n                  <\/jats:p>","DOI":"10.1002\/wer.70527","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T02:05:38Z","timestamp":1786932338000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction, Optimization, and Uncertainty Quantification of Methylene Blue Removal by Biochar Adsorbents Using Ensemble Machine Learning"],"prefix":"10.1002","volume":"98","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1874-0408","authenticated-orcid":false,"given":"Gokulan","family":"Ravindiran","sequence":"first","affiliation":[{"name":"Department of Civil Engineering Dayananda Sagar College of Engineering  Bengaluru Karnataka India"},{"name":"Institute of Energy Infrastructure Universiti Tenaga Nasional (UNITEN)  Kajang Selangor Darul Ehsan Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gorti","family":"Janardhan","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering GMR Institute of Technology (GMRIT) Deemed to Be University  Rajam India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marlinda","family":"Abdul Malek","sequence":"additional","affiliation":[{"name":"Environmental Engineering Department, College of Engineering Imam Abdulrahman Bin Faisal University  Dammam Eastern Province Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2702-9271","authenticated-orcid":false,"given":"Mary Subaja","family":"Christo","sequence":"additional","affiliation":[{"name":"Department of Networking and Communications, School of Computing SRM Institute of Science and Technology  Kattankulathur India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5627-8050","authenticated-orcid":false,"given":"Deepshikha","family":"Datta","sequence":"additional","affiliation":[{"name":"Department of Basic Science and Humanities, Institute of Engineering and Management (IEM) University of Engineering and Management (UEM)  Kolkata West Bengal India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2061-5032","authenticated-orcid":false,"given":"Ali. 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