Aqueous Pb(II) Removal Using ZIF-60: Adsorption Studies, Response Surface Methodology and Machine Learning Predictions
Zeolitic imidazolate frameworks (ZIFs) are increasingly gaining attention in many application fields due to their outstanding porosity and thermal stability, among other exceptional characteristics. However, in the domain of water purification via adsorption, scientists have mainly focused on ZIF-8...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Nanomaterials |
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| Online Access: | https://www.mdpi.com/2079-4991/13/8/1402 |
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| author | Usman M. Ismail Sagheer A. Onaizi Muhammad S. Vohra |
| author_facet | Usman M. Ismail Sagheer A. Onaizi Muhammad S. Vohra |
| author_sort | Usman M. Ismail |
| collection | DOAJ |
| description | Zeolitic imidazolate frameworks (ZIFs) are increasingly gaining attention in many application fields due to their outstanding porosity and thermal stability, among other exceptional characteristics. However, in the domain of water purification via adsorption, scientists have mainly focused on ZIF-8 and, to a lesser extent, ZIF-67. The performance of other ZIFs as water decontaminants is yet to be explored. Hence, this study applied ZIF-60 for the removal of lead from aqueous solutions; this is the first time ZIF-60 has been used in any water treatment adsorption study. The synthesized ZIF-60 was subjected to characterization using FTIR, XRD and TGA. A multivariate approach was used to investigate the effect of adsorption parameters on lead removal and the findings revealed that ZIF-60 dose and lead concentration are the most significant factors affecting the response (i.e., lead removal efficiency). Further, response surface methodology-based regression models were generated. To further explore the adsorption performance of ZIF-60 in removing lead from contaminated water samples, adsorption kinetics, isotherm and thermodynamic investigations were conducted. The findings revealed that the obtained data were well-fitted by the Avrami and pseudo-first-order kinetic models, suggesting that the process is complex. The maximum adsorption capacity (<i>q</i><sub>max</sub>) was predicted to be 1905 mg/g. Thermodynamic studies revealed an endothermic and spontaneous adsorption process. Finally, the experimental data were aggregated and used for machine learning predictions using several algorithms. The model generated by the random forest algorithm proved to be the most effective on the basis of its significant correlation coefficient and minimal root mean square error (RMSE). |
| first_indexed | 2024-03-11T04:40:27Z |
| format | Article |
| id | doaj.art-7e3e030469ae45a7a9f15bdaab5f8f61 |
| institution | Directory Open Access Journal |
| issn | 2079-4991 |
| language | English |
| last_indexed | 2024-03-11T04:40:27Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Nanomaterials |
| spelling | doaj.art-7e3e030469ae45a7a9f15bdaab5f8f612023-11-17T20:43:57ZengMDPI AGNanomaterials2079-49912023-04-01138140210.3390/nano13081402Aqueous Pb(II) Removal Using ZIF-60: Adsorption Studies, Response Surface Methodology and Machine Learning PredictionsUsman M. Ismail0Sagheer A. Onaizi1Muhammad S. Vohra2Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi ArabiaChemical Engineering Department, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi ArabiaCivil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi ArabiaZeolitic imidazolate frameworks (ZIFs) are increasingly gaining attention in many application fields due to their outstanding porosity and thermal stability, among other exceptional characteristics. However, in the domain of water purification via adsorption, scientists have mainly focused on ZIF-8 and, to a lesser extent, ZIF-67. The performance of other ZIFs as water decontaminants is yet to be explored. Hence, this study applied ZIF-60 for the removal of lead from aqueous solutions; this is the first time ZIF-60 has been used in any water treatment adsorption study. The synthesized ZIF-60 was subjected to characterization using FTIR, XRD and TGA. A multivariate approach was used to investigate the effect of adsorption parameters on lead removal and the findings revealed that ZIF-60 dose and lead concentration are the most significant factors affecting the response (i.e., lead removal efficiency). Further, response surface methodology-based regression models were generated. To further explore the adsorption performance of ZIF-60 in removing lead from contaminated water samples, adsorption kinetics, isotherm and thermodynamic investigations were conducted. The findings revealed that the obtained data were well-fitted by the Avrami and pseudo-first-order kinetic models, suggesting that the process is complex. The maximum adsorption capacity (<i>q</i><sub>max</sub>) was predicted to be 1905 mg/g. Thermodynamic studies revealed an endothermic and spontaneous adsorption process. Finally, the experimental data were aggregated and used for machine learning predictions using several algorithms. The model generated by the random forest algorithm proved to be the most effective on the basis of its significant correlation coefficient and minimal root mean square error (RMSE).https://www.mdpi.com/2079-4991/13/8/1402lead removaladsorption isotherm and kineticsresponse surface methodology (RSM)machine learningwater treatmentheavy metals |
| spellingShingle | Usman M. Ismail Sagheer A. Onaizi Muhammad S. Vohra Aqueous Pb(II) Removal Using ZIF-60: Adsorption Studies, Response Surface Methodology and Machine Learning Predictions Nanomaterials lead removal adsorption isotherm and kinetics response surface methodology (RSM) machine learning water treatment heavy metals |
| title | Aqueous Pb(II) Removal Using ZIF-60: Adsorption Studies, Response Surface Methodology and Machine Learning Predictions |
| title_full | Aqueous Pb(II) Removal Using ZIF-60: Adsorption Studies, Response Surface Methodology and Machine Learning Predictions |
| title_fullStr | Aqueous Pb(II) Removal Using ZIF-60: Adsorption Studies, Response Surface Methodology and Machine Learning Predictions |
| title_full_unstemmed | Aqueous Pb(II) Removal Using ZIF-60: Adsorption Studies, Response Surface Methodology and Machine Learning Predictions |
| title_short | Aqueous Pb(II) Removal Using ZIF-60: Adsorption Studies, Response Surface Methodology and Machine Learning Predictions |
| title_sort | aqueous pb ii removal using zif 60 adsorption studies response surface methodology and machine learning predictions |
| topic | lead removal adsorption isotherm and kinetics response surface methodology (RSM) machine learning water treatment heavy metals |
| url | https://www.mdpi.com/2079-4991/13/8/1402 |
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