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 produ...

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Bibliographic Details
Published in:Cogent engineering 2021-01, Vol.8 (1)
Main Authors: Okoji, Anthony I., Anozie, Ambrose N., Omoleye, James A.
Format: Article
Language:English
Subjects:
adaptive neuro-fuzzy inference systems
Adaptive systems
Artificial neural networks
cement production
Comparative studies
Efficiency
Energy consumption
Exergy
Exergy efficiency
Fuzzy logic
Fuzzy systems
Gas flow
Mathematical models
Moisture content
multiple linear regression
Optimization
Optimization techniques
Performance prediction
Prediction models
Process parameters
Raw materials
raw meal production
Response surface methodology
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Summary: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.
ISSN:2331-1916
2331-1916
DOI:10.1080/23311916.2021.1930493