A Regression Model Based on Artificial Neural Networks for a Stone Conical Crusher

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dc.contributor.affiliationBernal-Fernández A.P., Universidad Nacional de Colombia, Medellin, Colombia
dc.contributor.affiliationRojas-Reyes N.R., Universidad Nacional de Colombia, Medellin, Colombia
dc.contributor.affiliationChica-Osorio L.M., Universidad de Medellín, Medellin, Colombia
dc.contributor.affiliationBolaños-Martínez F., Universidad Nacional de Colombia, Medellin, Colombia
dc.contributor.affiliationFlorez H., Universidad Distrital Francisco Jose de Caldas, Bogota, Colombia
dc.contributor.authorBernal-Fernández A.P.
dc.contributor.authorRojas-Reyes N.R.
dc.contributor.authorChica-Osorio L.M.
dc.contributor.authorBolaños-Martínez F.
dc.contributor.authorFlorez H.
dc.date.accessioned2025-09-08T14:23:44Z
dc.date.available2025-09-08T14:23:44Z
dc.date.issued2025
dc.descriptionModeling for design and simulation has gained significance in both academic and industrial domains, primarily due to the availability of specialized software. In the domain of modeling mineral beneficiation crushers, the focus is on controlling variables related to equipment mechanics and material characteristics. In this study, the comminution process of a conical crusher in a stone aggregates production plant was specifically modeled using artificial neural networks. A Feedforward artificial neural network topology was employed to simulate the comminution process. Different network configurations were tested through 50 laboratory experiments, where size data were collected at the crusher’s entrance and exit. The aim was to select the configuration that best matched the actual data by optimizing mean square error, error dispersion, and linear fit. After thorough evaluation, the network configuration with hidden layers of 70 and 40 neurons emerged as the most effective. This configuration yielded a mean square error of 6.24, an error dispersion of 35.05, and a robust linear fit of 0.99. Subsequently, this model was applied to new size distribution data from the crusher’s entrance and exit, resulting in an R parameter of 0.999. The study highlights the effectiveness of the trained artificial neural network model in accurately predicting particle size distribution at the exit of a conical crusher. This has significant implications for optimizing the beneficiation process in stone aggregate production. © The Author(s) 2025.
dc.identifier.doi10.1007/s42979-025-03949-8
dc.identifier.instnameinstname:Universidad de Medellínspa
dc.identifier.issn2662995X
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellínspa
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.urihttp://hdl.handle.net/11407/9096
dc.language.isoeng
dc.publisher.facultyFacultad de Ingenieríasspa
dc.publisher.programIngeniería Civilspa
dc.relation.citationissue5
dc.relation.citationvolume6
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-105005093701&doi=10.1007%2fs42979-025-03949-8&partnerID=40&md5=c7988b5e4a663e593690c3ccc15d853d
dc.relation.referencesAlquraish M., Modeling and simulation of manufacturing processes and systems: Overview of tools, challenges, and future opportunities, Eng Technol Appl Sci Res, 12, 6, pp. 9779-9786, (2022)
dc.relation.referencesGrznar P., Gregor M., Krajcovic M., Mozol &#X.0160;., Schickerle M., Vavrik V., Durica L., Marschall M., Bielik T., Modeling and simulation of processes in a factory of the future, Applied sciences, 10, 13, (2020)
dc.relation.referencesNiu Y., Ying L., Yang J., Bao M., Sivaparthipan C., Organizational business intelligence and decision making using big data analytics, Inform Process Manage, 58, 6, (2021)
dc.relation.referencesYamashita A.S., Thivierge A., Euzebio T.A., A review of modeling and control strategies for cone crushers in the mineral processing and quarrying industries, Min Eng, 170, (2021)
dc.relation.referencesFang H., Ji X., Yang J., Yang Y., Ji T., Wei, C (2024) Adaptive control method and experimental study of cone crusher based on aggregate online detection, Physicochem Problems Min Process, 60, 3, (2024)
dc.relation.referencesItavuo P., Vilkko M., Size reduction control in cone crushers, Min Eng, 173, (2021)
dc.relation.referencesFang H.Y., Yang J.H., Chen Q., Particle size distribution and energy consumption during impact crushing of single granite particles, J Southern African Inst Min Metallurgy, 118, pp. 555-561, (2018)
dc.relation.referencesPerez J.R.H., Velazquez A.L.C., Brawn R.L., Aguado J.M.M., Aplicación de los modelos de nikolov en la trituración por impacto del mineral zeolita del yacimiento san andrés, holguín, cuba, Minería y Geología, 24, 4, pp. 1-17, (2008)
dc.relation.referencesMykhailenko O., Modeling of cone crusher steady-state operation modes, IOP Conf. Ser, 1415, 1, (2024)
dc.relation.referencesMoncada M.M., Toledo M.P., Betancourt C.F., Rodriguez C.G., Predictive modeling of crushing power in cone crushers with the discrete element method, Powder Technology, 447, (2024)
dc.relation.referencesJiang C., Gou D., Li C., Wu G., An X., Wang J., Guo P., Crushing characteristics and performance evaluation of iron ore in a cone crusher: a numerical study, Min Eng, 204, (2023)
dc.relation.referencesAbuhasel K.A., Machine learning approach to handle data-driven model for simulation and forecasting of the cone crusher output in the stone crushing plant, Computational Intelligence, 37, 3, pp. 1098-1110, (2021)
dc.relation.referencesDundar H., Benzer H., Aydogan N., Application of population balance model to hpgr crushing, Min Eng, 50, pp. 114-120, (2013)
dc.relation.referencesLindqvist M., Li H., A cone crusher model with a coupled energy- and breakage dependency, Min Eng, 172, (2021)
dc.relation.referencesCleary P.W., Sinnott M.D., Simulation of particle flows and breakage in crushers using dem: Part 1-compression crushers, Min Eng, 74, pp. 178-197, (2015)
dc.relation.referencesSegura-Salazar J., Barrios G.P., Rodriguez V., Tavares L.M., Mathematical modeling of a vertical shaft impact crusher using the whiten model, Min Eng, 111, pp. 222-228, (2017)
dc.relation.referencesDi Franco G., Santurro M., Machine learning, artificial neural networks and social research, Q Quantity, 55, 3, pp. 1007-1025, (2021)
dc.relation.referencesMeireles M.R., Almeida P.E., Simoes M.G., A comprehensive review for industrial applicability of artificial neural networks, IEEE Trans Ind Electron, 50, 3, pp. 585-601, (2003)
dc.relation.referencesMoncada M.M., Toledo M.P., Betancourt C.F., Rodriguez C.G., Predictive modeling of crushing power in cone crushers with the discrete element method, Powder Technology, 447, (2024)
dc.relation.referencesBishop C.M., Neural networks and their applications, Rev Sci Instruments, 65, 6, pp. 1803-1832, (1994)
dc.relation.referencesMontesinos Lopez O.A., Montesinos Lopez A., Crossa J., Fundamentals of artificial neural networks and deep learning, Multivariate Statistical Machine Learning Methods for Genomic Prediction, pp. 379-425, (2022)
dc.relation.referencesPaturi U.M.R., Cheruku S., Geereddy S.R., Process modeling and parameter optimization of surface coatings using artificial neural networks (anns): state-of-the-art review, Mater Today, 38, pp. 2764-2774, (2021)
dc.relation.referencesDuarte R.A., Yamashita A.S., Silva M.T., Cota L.P., Euzebio T.A.M., Calibration and validation of a cone crusher model with industrial data, Minerals, 11, 11, (2021)
dc.relation.referencesHernandez J., Daza K., Florez H., Spiking neural network approach based on caenorhabditis elegans worm for classification, IAENG Int J Comput Sci, 49, 4, (2022)
dc.relation.referencesNina Yucra S.M., Modelo predictivo de producción de minerales de plata mediante redes neuronales artificiales. Revista PGI, Investigación, Ciencia y Tecnología en Informática, 8, pp. 176-179, (2020)
dc.relation.referencesHaykin S., Neural Networks and Learning Machines, 3/E, (2009)
dc.relation.referencesDe Giorgi M.G., Campilongo S., Ficarella A., Congedo P.M., Comparison between wind power prediction models based on wavelet decomposition with least-squares support vector machine (ls-svm) and artificial neural network (ann), Energies, 7, 8, pp. 5251-5272, (2014)
dc.relation.referencesAbuhasel K.A., Machine learning approach to handle data-driven model for simulation and forecasting of the cone crusher output in the stone crushing plant, Computational Intelligence, 37, 3, pp. 1098-1110, (2021)
dc.relation.referencesKoken E., Modeling the throughput of horizontal shaft impact crushers using regression analyses, artificial neural networks and multivariate adaptive regression spline, Afyon Kocatepe Univ J Sci Eng, 22, pp. 1193-1203, (2022)
dc.relation.referencesGharaei R.S., Gholami A., Khoshdast H., B H., A multi-method machine learning framework for simulating power dynamics in an iron ore cone crusher plant, Mineral Processing and Extractive Metallurgy Review, pp. 1-16, (2024)
dc.rights.accesoRestricted access
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.sourceSN Computer Science
dc.sourceSN COMPUT. SCI.
dc.sourceScopus
dc.subjectArtificial neural networks
dc.subjectConical crusher
dc.subjectControl
dc.subjectFeedforward topology
dc.subjectOptimization
dc.subjectStone aggregate
dc.titleA Regression Model Based on Artificial Neural Networks for a Stone Conical Crusher
dc.typeArticle
dc.type.localArtículospa
dc.type.versioninfo:eu-repo/semantics/publishedVersion

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