Evaluation of optimization techniques for predicting exergy efficiency of the cement raw meal production process
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Tailor & Francis
Abstract
Description
In cement production, raw meal preparation and energy consumption
are extremely important for cost reduction. However, few studies have examined
the relationship between operational process parameters and exergy efficiency. For
this comparative study on predicting exergy efficiency of raw meal production,
adaptive neuro-fuzzy inference systems (ANFIS), multiple linear regression (MLR),
and response surface methodology (RSM) were used for a comparison of the predictive
accuracy of these parameters. The study also suggests a routine for selecting
the best predictive model, which includes considering raw materials, primary air,
moisture content, and kiln hot gas flow. The established model was tested against
different indicators of predictive performance and found to be consistent. The
developed ANFIS, MLR, and RSM models accurately described the process (coefficient
of determination, R2 > 0.9000), and in each case, the absolute relative errors
(AARE) are 0.000692, 0.00422, and 0.00135. The current study has found that both
ANFIS and RSM predicted correctly and consistently better than MLR, but while
ANFIS and RSM produced similar results, ANFIS performed slightly better than RSM.
Keywords
TP Chemical technology