Optmisation of cadmium (II) removal onto biodegradable composite using artificial neural networks and response surface methodology: quantum chemical performance
A multifunctional grafted cellulose nanocrystals derivative adsorbent (composites) with carboxyl, amide, and secondary amino groups was successfully developed for Cd2+ removal. The characteristics of CNCs, chitosan, and nanocomposites were determined using FTIR, TGA, SEM, and BET. The approaches of...
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| Format: | Article |
| Language: | English |
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University of Bologna
2024-02-01
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| Series: | EQA |
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| Online Access: | https://eqa.unibo.it/article/view/18602 |
| _version_ | 1797299310676672512 |
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| author | Jean Claude Banza Maurice Stephane Onyango |
| author_facet | Jean Claude Banza Maurice Stephane Onyango |
| author_sort | Jean Claude Banza |
| collection | DOAJ |
| description | A multifunctional grafted cellulose nanocrystals derivative adsorbent (composites) with carboxyl, amide, and secondary amino groups was successfully developed for Cd2+ removal. The characteristics of CNCs, chitosan, and nanocomposites were determined using FTIR, TGA, SEM, and BET. The approaches of artificial intelligence and Response Surface Methodology modeling were employed, as well as how well they predicted response (adsorption capacity). The adsorption isotherm and kinetic models were applied to comprehend the process further. Statistical results demonstrated that The response surface model approach performed better than the artificial neural network model approach. The adsorption capacity was 440.01 mg/g with a starting pH of 5.65, a duration of contact of 315 minutes, a starting concentration of 333 mg/L, and an adsorbent dose of 16.93 mg. The FTIR examination revealed that the functional groups of the nanocomposites were equivalent to those of CNCs and chitosan; however, the nanocomposites were more thermally stable than CNCs and chitosan. The nanocomposites' SEM pictures revealed a porous structure, thin particle size, and needle-like shape. The Langmuir model explains the spontaneous nature of the adsorption process, and chemisorption served as the primary control. According to the Dubinin-Radushkevich Model, to adsorb Cd2+, the energy required is larger than 8 kJ mol-1, suggesting that the chemisorption mechanism was involved. The adsorption kinetics were established using the pseudo-second-order rate model. HOMO−LUMO energy binding differences were used to find the best locations for adsorption. |
| first_indexed | 2024-03-07T22:47:50Z |
| format | Article |
| id | doaj.art-a3db81fc963041cbb90f9d4b84b598af |
| institution | Directory Open Access Journal |
| issn | 2039-9898 2281-4485 |
| language | English |
| last_indexed | 2024-03-07T22:47:50Z |
| publishDate | 2024-02-01 |
| publisher | University of Bologna |
| record_format | Article |
| series | EQA |
| spelling | doaj.art-a3db81fc963041cbb90f9d4b84b598af2024-02-23T14:58:51ZengUniversity of BolognaEQA2039-98982281-44852024-02-016011710.6092/issn.2281-4485/1860216961Optmisation of cadmium (II) removal onto biodegradable composite using artificial neural networks and response surface methodology: quantum chemical performanceJean Claude Banza0Maurice Stephane Onyango1Department of Chemical, Metallurgical and Materials Engineering, Tshwane University of Technology, PretoriaDepartment of Chemical, Metallurgical and Materials Engineering, Tshwane University of Technology, PretoriaA multifunctional grafted cellulose nanocrystals derivative adsorbent (composites) with carboxyl, amide, and secondary amino groups was successfully developed for Cd2+ removal. The characteristics of CNCs, chitosan, and nanocomposites were determined using FTIR, TGA, SEM, and BET. The approaches of artificial intelligence and Response Surface Methodology modeling were employed, as well as how well they predicted response (adsorption capacity). The adsorption isotherm and kinetic models were applied to comprehend the process further. Statistical results demonstrated that The response surface model approach performed better than the artificial neural network model approach. The adsorption capacity was 440.01 mg/g with a starting pH of 5.65, a duration of contact of 315 minutes, a starting concentration of 333 mg/L, and an adsorbent dose of 16.93 mg. The FTIR examination revealed that the functional groups of the nanocomposites were equivalent to those of CNCs and chitosan; however, the nanocomposites were more thermally stable than CNCs and chitosan. The nanocomposites' SEM pictures revealed a porous structure, thin particle size, and needle-like shape. The Langmuir model explains the spontaneous nature of the adsorption process, and chemisorption served as the primary control. According to the Dubinin-Radushkevich Model, to adsorb Cd2+, the energy required is larger than 8 kJ mol-1, suggesting that the chemisorption mechanism was involved. The adsorption kinetics were established using the pseudo-second-order rate model. HOMO−LUMO energy binding differences were used to find the best locations for adsorption.https://eqa.unibo.it/article/view/18602cellulose nanocrystalschitosanquantum chemical simulationcentral composite designartificial neural networkresponse surface method |
| spellingShingle | Jean Claude Banza Maurice Stephane Onyango Optmisation of cadmium (II) removal onto biodegradable composite using artificial neural networks and response surface methodology: quantum chemical performance EQA cellulose nanocrystals chitosan quantum chemical simulation central composite design artificial neural network response surface method |
| title | Optmisation of cadmium (II) removal onto biodegradable composite using artificial neural networks and response surface methodology: quantum chemical performance |
| title_full | Optmisation of cadmium (II) removal onto biodegradable composite using artificial neural networks and response surface methodology: quantum chemical performance |
| title_fullStr | Optmisation of cadmium (II) removal onto biodegradable composite using artificial neural networks and response surface methodology: quantum chemical performance |
| title_full_unstemmed | Optmisation of cadmium (II) removal onto biodegradable composite using artificial neural networks and response surface methodology: quantum chemical performance |
| title_short | Optmisation of cadmium (II) removal onto biodegradable composite using artificial neural networks and response surface methodology: quantum chemical performance |
| title_sort | optmisation of cadmium ii removal onto biodegradable composite using artificial neural networks and response surface methodology quantum chemical performance |
| topic | cellulose nanocrystals chitosan quantum chemical simulation central composite design artificial neural network response surface method |
| url | https://eqa.unibo.it/article/view/18602 |
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