Biosorption of Pb(II) Using Natural and Treated <i>Ardisia compressa</i> K. Leaves: Simulation Framework Extended through the Application of Artificial Neural Network and Genetic Algorithm
This study explored the effects of solution pH, biosorbent dose, contact time, and temperature on the Pb(II) biosorption process of natural and chemically treated leaves of <i>A. compressa</i> K. (Raw-AC and AC-OH, respectively). The results show that the surface characteristics of Raw-A...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/28/17/6387 |
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| author | Alma Y. Vázquez-Sánchez Eder C. Lima Mohamed Abatal Rasikh Tariq Arlette A. Santiago Ismeli Alfonso Claudia Aguilar América R. Vazquez-Olmos |
| author_facet | Alma Y. Vázquez-Sánchez Eder C. Lima Mohamed Abatal Rasikh Tariq Arlette A. Santiago Ismeli Alfonso Claudia Aguilar América R. Vazquez-Olmos |
| author_sort | Alma Y. Vázquez-Sánchez |
| collection | DOAJ |
| description | This study explored the effects of solution pH, biosorbent dose, contact time, and temperature on the Pb(II) biosorption process of natural and chemically treated leaves of <i>A. compressa</i> K. (Raw-AC and AC-OH, respectively). The results show that the surface characteristics of Raw-AC changed following alkali treatment. FT-IR analysis showed the presence of various functional groups on the surface of the biosorbent, which were binding sites for the Pb(II) biosorption. The nonlinear pseudo-second-order kinetic model was found to be the best fitted to the experimental kinetic data. Adsorption equilibrium data at pH = 2–6, biosorbents dose from 5 to 20 mg/L, and temperature from 300.15 to 333.15 K were adjusted to the Langmuir, Freundlich, and Dubinin–Radushkevich (D-R) isotherm models. The results show that the adsorption capacity was enhanced with the increase in the solution pH and diminished with the increase in the temperature and biosorbent dose. It was also found that AC-OH is more effective than Raw-AC in removing Pb(II) from aqueous solutions. This was also confirmed using artificial neural networks and genetic algorithms, where it was demonstrated that the improvement was around 57.7%. The nonlinear Langmuir isotherm model was the best fitted, and the maximum adsorption capacities of Raw-AC and AC-OH were 96 mg/g and 170 mg/g, respectively. The removal efficiency of Pb(II) was maintained approximately after three adsorption and desorption cycles using 0.5 M HCl as an eluent. This research delved into the impact of solution pH, biosorbent characteristics, and operational parameters on Pb(II) biosorption, offering valuable insights for engineering education by illustrating the practical application of fundamental chemical and kinetic principles to enhance the design and optimization of sustainable water treatment systems. |
| first_indexed | 2024-03-10T23:16:06Z |
| format | Article |
| id | doaj.art-2022c7ebdc4e401980f52449c3f3fcfc |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-10T23:16:06Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-2022c7ebdc4e401980f52449c3f3fcfc2023-11-19T08:35:17ZengMDPI AGMolecules1420-30492023-08-012817638710.3390/molecules28176387Biosorption of Pb(II) Using Natural and Treated <i>Ardisia compressa</i> K. Leaves: Simulation Framework Extended through the Application of Artificial Neural Network and Genetic AlgorithmAlma Y. Vázquez-Sánchez0Eder C. Lima1Mohamed Abatal2Rasikh Tariq3Arlette A. Santiago4Ismeli Alfonso5Claudia Aguilar6América R. Vazquez-Olmos7Área Agroindustrial Alimentaria, Universidad Tecnológica de Xicotepec de Juárez, Av. Universidad Tecnológica No. 1000. Col. Tierra Negra Xicotepec de Juárez, Puebla 73080, MexicoInstitute of Chemistry, Federal University of Rio Grande do Sul (UFRGS), Av. Bento Goncalves 9500, P.O. Box 15003, Porto Alegre 91501-970, RS, BrazilFacultad de Ingeniería, Universidad Autónoma del Carmen, Campeche 24115, MexicoInstitute for the Future of Education, Tecnologico de Monterrey, Monterrey 64849, MexicoEscuela Nacional de Estudios Superiores, Unidad Morelia, Universidad Nacional Autónoma de México, Antigua Carretera a Pátzcuaro No. 8701, Col. Ex. Hacienda de San José de la Huerta, Morelia 58190, MexicoInstituto de Investigaciones en Materiales, Unidad Morelia, Universidad Nacional Autónoma de México, Antigua Carretera a Pátzcuaro No. 8701, Col. Ex. Hacienda de San José de la Huerta, Morelia 58190, MexicoFacultad de Química, Universidad Autónoma del Carmen, Calle 56 No. 4 Av. Concordia, Ciudad del Carmen, Campeche 24180, MexicoInstituto de Ciencias aplicadas y Tecnología, UNAM, Circuito Exterior, S/N, Ciudad Universitaria, A.P. 70-186, Delegación Coyoacán, Ciudad de México 04510, MexicoThis study explored the effects of solution pH, biosorbent dose, contact time, and temperature on the Pb(II) biosorption process of natural and chemically treated leaves of <i>A. compressa</i> K. (Raw-AC and AC-OH, respectively). The results show that the surface characteristics of Raw-AC changed following alkali treatment. FT-IR analysis showed the presence of various functional groups on the surface of the biosorbent, which were binding sites for the Pb(II) biosorption. The nonlinear pseudo-second-order kinetic model was found to be the best fitted to the experimental kinetic data. Adsorption equilibrium data at pH = 2–6, biosorbents dose from 5 to 20 mg/L, and temperature from 300.15 to 333.15 K were adjusted to the Langmuir, Freundlich, and Dubinin–Radushkevich (D-R) isotherm models. The results show that the adsorption capacity was enhanced with the increase in the solution pH and diminished with the increase in the temperature and biosorbent dose. It was also found that AC-OH is more effective than Raw-AC in removing Pb(II) from aqueous solutions. This was also confirmed using artificial neural networks and genetic algorithms, where it was demonstrated that the improvement was around 57.7%. The nonlinear Langmuir isotherm model was the best fitted, and the maximum adsorption capacities of Raw-AC and AC-OH were 96 mg/g and 170 mg/g, respectively. The removal efficiency of Pb(II) was maintained approximately after three adsorption and desorption cycles using 0.5 M HCl as an eluent. This research delved into the impact of solution pH, biosorbent characteristics, and operational parameters on Pb(II) biosorption, offering valuable insights for engineering education by illustrating the practical application of fundamental chemical and kinetic principles to enhance the design and optimization of sustainable water treatment systems.https://www.mdpi.com/1420-3049/28/17/6387<i>Ardisia compressa</i> K.biosorptionheavy metalsartificial neural networkeducational Innovation |
| spellingShingle | Alma Y. Vázquez-Sánchez Eder C. Lima Mohamed Abatal Rasikh Tariq Arlette A. Santiago Ismeli Alfonso Claudia Aguilar América R. Vazquez-Olmos Biosorption of Pb(II) Using Natural and Treated <i>Ardisia compressa</i> K. Leaves: Simulation Framework Extended through the Application of Artificial Neural Network and Genetic Algorithm Molecules <i>Ardisia compressa</i> K. biosorption heavy metals artificial neural network educational Innovation |
| title | Biosorption of Pb(II) Using Natural and Treated <i>Ardisia compressa</i> K. Leaves: Simulation Framework Extended through the Application of Artificial Neural Network and Genetic Algorithm |
| title_full | Biosorption of Pb(II) Using Natural and Treated <i>Ardisia compressa</i> K. Leaves: Simulation Framework Extended through the Application of Artificial Neural Network and Genetic Algorithm |
| title_fullStr | Biosorption of Pb(II) Using Natural and Treated <i>Ardisia compressa</i> K. Leaves: Simulation Framework Extended through the Application of Artificial Neural Network and Genetic Algorithm |
| title_full_unstemmed | Biosorption of Pb(II) Using Natural and Treated <i>Ardisia compressa</i> K. Leaves: Simulation Framework Extended through the Application of Artificial Neural Network and Genetic Algorithm |
| title_short | Biosorption of Pb(II) Using Natural and Treated <i>Ardisia compressa</i> K. Leaves: Simulation Framework Extended through the Application of Artificial Neural Network and Genetic Algorithm |
| title_sort | biosorption of pb ii using natural and treated i ardisia compressa i k leaves simulation framework extended through the application of artificial neural network and genetic algorithm |
| topic | <i>Ardisia compressa</i> K. biosorption heavy metals artificial neural network educational Innovation |
| url | https://www.mdpi.com/1420-3049/28/17/6387 |
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