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Kemija u industriji : Časopis kemičara i kemijskih inženjera Hrvatske, Vol. 70 No. 1-2, 2021.

Izvorni znanstveni članak

https://doi.org/10.15255/KUI.2020.011

Artificial Neural Network Modelling of Multi-system Dynamic Adsorption of Organic Pollutants on Activated Carbon

Yamin Mesellem ; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University Yahia Fares of Médéa, Algeria
Abdallah El Hadj Abdallah ; Faculty of Science, University Saad Dahleb of Blida (USDB-1-), Somaa, Blida, Algeria
Maamar Laidi orcid id orcid.org/0000-0002-8977-9895 ; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University Yahia Fares of Médéa, Algeria
Salah Hanini ; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University Yahia Fares of Médéa, Algeria
Mohamed Hentabli orcid id orcid.org/0000-0002-6693-0708 ; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University Yahia Fares of Médéa, Algeria

Puni tekst: engleski pdf 2.122 Kb

str. 1-12

preuzimanja: 323

citiraj

Sažetak

The aim of this work was to model multi-system dynamic adsorption using an artificial intelligence technique. A set of data points, collected from scientific papers containing the dynamic adsorption kinetics on activated carbon, was used to build the artificial neural network (ANN). The studied parameters were molar mass, initial concentration, flow rate, bed height, particle diameter, BET surface area, average pore diameter, time, and concentration of dimensionless effluents. Results showed that the optimized ANN was obtained with a high correlation coefficient, R = 0.997, a root mean square error of RMSE = 0.029, and a mean absolute deviation of AAD (%) = 1.810 during the generalisation phase. Furthermore, a sensitivity analysis was also conducted using the inverse artificial neural network method to study the effect of all the inputs on the dynamic adsorption. Also in this work, the traceability of the estimated results was conducted by developing a graphical user interface.




This work is licensed under a Creative Commons Attribution 4.0 International License.

Ključne riječi

artificial neural network; dynamic adsorption; organic pollutants; activated carbon

Hrčak ID:

250593

URI

https://hrcak.srce.hr/250593

Datum izdavanja:

24.1.2021.

Podaci na drugim jezicima: hrvatski

Posjeta: 1.017 *