Evaluation of optimization techniques for predicting exergy efficiency of the cement raw meal production process

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, R-2 > 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.

Automated summary

The study compared the predictive accuracy of different parameters in predicting exergy efficiency of raw meal production, using ANFIS, MLR, and RSM models. It found that ANFIS and RSM consistently outperformed MLR in predicting accurately, with ANFIS slightly better than RSM.