A comparative study of black-box models for cement quality prediction using input-output measurements of a closed circuit grinding

Minchala Ávila, Luis IsmaelSanchez, CYungaicela, M2018-01-112018-01-112016-04-189781467395182https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979279823&doi=10.1109%2fSYSCON.2016.7490538&partnerID=40&md5=f53a51d7025bdeb048c214c70c991b0ahttp://dspace.ucuenca.edu.ec/handle/123456789/29168This paper presents the methodology of design of three different modeling techniques for predicting cement quality using input-output measurements of the closed circuit grinding in a cement plant. The modeling approaches used are: statistical, artificial neural networks (ANN), and adaptive neuro-fuzzy inference systems (ANFIS). The data set for generating the predictive models are obtained from a database of the operation of the cement plant, UCEM-Guapan. An OPC (OLE for process control) network configuration in the SCADA system allows online validations of the proposed models in order to select the best approach for real-time prediction of cement quality.en-USAdaptive Neuro-Fuzzy Inference SystemArtificial Neural NetworksBlack-Box ModelFineness Of The CementA comparative study of black-box models for cement quality prediction using input-output measurements of a closed circuit grindingArticle10.1109/SYSCON.2016.7490538