Evaluation of adaptive neuro-fuzzy inference system-genetic algorithm in the prediction and optimization of NOx emission in cement precalcining kiln

Okoji, Anthony I. and Anozie, Ambrose Nwora and Omoleye, James and Taiwo, Abiola E. and Babatunde, Damilola Elizabeth (2023) Evaluation of adaptive neuro-fuzzy inference system-genetic algorithm in the prediction and optimization of NOx emission in cement precalcining kiln. Environment Science Pollution, 30. pp. 54835-54845.

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Abstract

The increasing demand for cement due to urbanization growth in Africa countries may result in an upsurge of pollutants associated with its production. One major air pollutant in cement production is nitrogen oxides (NOx) and reported to cause serious damage to human health and the ecosystem. The operation of a cement rotary kiln NOx emission was studied with plant data using the ASPEN Plus software. It is essential to understand the effects of calciner temperature, tertiary air pressure, fuel gas, raw feed material, and fan damper on NOx emissions from a precalcining kiln. In addition, the performance capability of adaptive neuro-fuzzy inference systems and genetic algorithms (ANFIS-GA) to predict and optimize NOx emissions from a precalcining cement kiln is evaluated. The simulation results were in good agreement with the experimental results, with root mean square error of 2.05, variance account (VAF) of 96.0%, average absolute deviation (AAE) of 0.4097, and correlation coefficient of 0.963. Further, the optimal NOx emission was 273.0 mg/m3, with the parameters as determined by the algorithm were calciner temperature at 845 °C, tertiary air pressure − 4.50 mbar, fuel gas of 8550 m3/h, raw feed material 200 t/h, and damper opening of 60%. Consequently, it is recommended that ANFIS should be combined with GA for effective prediction, and optimization of NOx emission in cement plants.

Item Type:	Article
Uncontrolled Keywords:	Environmental pollution, Nitrogen oxides  Precalcining kiln  Cement production  Adaptive neuro-fuzzy inference systems  Genetic algorithms
Subjects:	T Technology > T Technology (General) T Technology > TP Chemical technology
Divisions:	Faculty of Engineering, Science and Mathematics > School of Engineering Sciences
Depositing User:	Patricia Nwokealisi
Date Deposited:	19 Feb 2025 08:26
Last Modified:	19 Feb 2025 08:26
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/18804

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