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Adv. Comput. Intell. Intell. Inform."],"published-print":{"date-parts":[[2026,3,20]]},"abstract":"<jats:p>\n                    Grinding in mineral processing critically depends on real-time control of the circulating load ratio (CLR) to optimize efficiency and reduce energy consumption. \t\tWhile investigating the impact of the CLR on the grinding process from a mechanistic perspective can optimize production, it fails to achieve real-time perception of its dynamic variations during operation. This limitation hinders timely adjustments to operational parameters. Focusing on an actual semi-autogenous grinding (SAG) process, this paper presents a hybrid fuzzy\n                    <jats:italic>C<\/jats:italic>\n                    -means (FCM) and Bayesian-optimized random forest (BO-RF) framework that explicitly addresses nonlinearity and operational variability in the SAG process. Key parameters influencing CLR are first identified through mechanistic analysis. Operating conditions are clustered via FCM, followed by BO-RF submodel construction for each cluster. A nearest-neighbor criterion dynamically activates submodels for real-time prediction. Validated with industrial data from an iron concentrate plant, the method achieves more than 90% prediction accuracy. This approach establishes a generalizable framework for complex industrial processes with multivariate dynamics.\n                  <\/jats:p>","DOI":"10.20965\/jaciii.2026.p0496","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T15:02:06Z","timestamp":1773932526000},"page":"496-508","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of Circulating Load Ratio for Semi-Autogenous Grinding Process Using Fuzzy\n                    <i>C<\/i>\n                    -Means and Bayesian-Optimized Random Forest"],"prefix":"10.20965","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4119-4764","authenticated-orcid":true,"given":"Zhenhong","family":"Liao","sequence":"first","affiliation":[{"name":"School of Automation, China University of Geosciences, No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China"},{"name":"Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, No.300 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China"},{"name":"Department of Mineral Resources Development and Utilization, Changsha Research Institute of Mining and Metallurgy Co., Ltd., No.966 South Lushan Road, Yuelu District, Changsha, Hunan 410012, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3165-5045","authenticated-orcid":true,"given":"Jinhua","family":"She","sequence":"additional","affiliation":[{"name":"School of Automation, China University of Geosciences, No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China"},{"name":"School of Engineering, Tokyo University of Technology, 1404-1 Katakuramachi, Hachioji, Tokyo 192-0982, Japan"}]},{"given":"Yanglong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Mineral Resources Development and Utilization, Changsha Research Institute of Mining and Metallurgy Co., Ltd., No.966 South Lushan Road, Yuelu District, Changsha, Hunan 410012, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4489-811X","authenticated-orcid":true,"given":"Wen","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Mineral Resources Development and Utilization, Changsha Research Institute of Mining and Metallurgy Co., Ltd., No.966 South Lushan Road, Yuelu District, Changsha, Hunan 410012, China"}]}],"member":"8550","published-online":{"date-parts":[[2026,3,20]]},"reference":[{"key":"key-10.20965\/jaciii.2026.p0496-1","doi-asserted-by":"crossref","unstructured":"K. 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