Predictive Model for Cement Clinker Quality Parameters

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.

Parametric Study of Matisaa Gray Rock as a Potential Clinker Material

“…The AR value predicates the potential relative content of aluminate and ferrite phases in the resultant clinker [22]. The alumina ratio of Matisaa gray rock (0.85 -4.63) is beyond the acceptable limits (1.5  AR  2.0).…”

“…AR values higher than 2.0 tend to increase the aluminate phase at the expense of the ferrite phase, thereby compromising the clinker quality. This also imparts harder burning which translates to high fuel consumption [22]. This, therefore, necessitates the beneficiation of Matisaa gray rock for clinker production.…”

Online Monitoring of Soft Sensor Performance: A Case Study of Cement Clinker Quality Parameters Soft Sensor

“…The soft sensor whose performance is to be monitored online by the proposed reverse model was reported in [14]. The steps followed in developing the reported soft sensor are summarised in section 3.1.…”

“…Thereafter, the experimental (simulation) data obtained were used to build the soft sensor. Equations (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) is the first principle-based cement rotary kiln model.…”

“…Furthermore, with the aid of Design Expert 7.0 statistical tool, these 154 data points were then used (after outlying data points had been removed based on the method described in [14]) to build the regression model (soft sensor). The soft sensor is of the form:…”