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has been cited by the following article:

• TITLE: Predictive Model for Cement Clinker Quality Parameters

  AUTHORS: Nsidibe-Obong Ekpe Moses, Sunday Boladale Alabi

  KEYWORDS: Clinker Quality Parameters, Online Estimation, Cement Rotary Kiln, Model, Soft Sensor

  JOURNAL NAME: Journal of Materials Science and Chemical Engineering, Vol.4 No.7, July 26, 2016

  ABSTRACT: Managers of cement plants are gradually becoming aware of the need for soft sensors in product quality assessment. Cement clinker quality parameters are mostly measured by offline laboratory analysis or by the use of online analyzers. The measurement delay and cost, associated with these methods, are a concern in the cement industry. In this study, a regression-based model was developed to predict the clinker quality parameters as a function of the raw meal quality and the kiln operating variables. This model has mean squared error, coefficient of determination, worst case relative error and variance account for (in external data) given as 8.96 × 10–7, 0.9999, 2.17% and above 97%, respectively. Thus, it is concluded that the developed model can provide real time estimates of the clinker quality parameters and capture wider ranges of real plant operating conditions from first principle-based cement rotary kiln models. Also, the model developed can be utilized online as soft sensor since they contain only variables that are easily measured online.