A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks
The application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a wide spectrum of adsorbents and adsorbates in t...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publications
2022-01-01
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| Series: | Adsorption Science & Technology |
| Online Access: | http://dx.doi.org/10.1155/2022/9384871 |
| _version_ | 1797288400620879872 |
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| author | Hilda Elizabeth Reynel-Ávila Ismael Alejandro Aguayo-Villarreal Lizbeth Liliana Diaz-Muñoz Jaime Moreno-Pérez Francisco Javier Sánchez-Ruiz Cintia Karina Rojas-Mayorga Didilia Ileana Mendoza-Castillo Adrián Bonilla-Petriciolet |
| author_facet | Hilda Elizabeth Reynel-Ávila Ismael Alejandro Aguayo-Villarreal Lizbeth Liliana Diaz-Muñoz Jaime Moreno-Pérez Francisco Javier Sánchez-Ruiz Cintia Karina Rojas-Mayorga Didilia Ileana Mendoza-Castillo Adrián Bonilla-Petriciolet |
| author_sort | Hilda Elizabeth Reynel-Ávila |
| collection | DOAJ |
| description | The application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a wide spectrum of adsorbents and adsorbates in the context of water purification. Artificial neural networks allow to overcome some drawbacks of traditional adsorption models especially in terms of providing better predictions at different operating conditions. However, these surrogate models have been applied mainly in adsorption systems with only one pollutant thus indicating the importance of extending their application for the prediction and simulation of adsorption systems with several adsorbates (i.e., multicomponent adsorption). This review analyzes and describes the data modeling of adsorption of organic and inorganic pollutants from water with artificial neural networks. The main developments and contributions on this topic have been discussed considering the results of a detailed search and interpretation of more than 250 papers published on Web of Science ® database. Therefore, a general overview of the training methods, input and output data, and numerical performance of artificial neural networks and related models utilized for adsorption data simulation is provided in this document. Some remarks for the reliable application and implementation of artificial neural networks on the adsorption modeling are also discussed. Overall, the studies on adsorption modeling with artificial neural networks have focused mainly on the analysis of batch processes (87%) in comparison to dynamic systems (13%) like packed bed columns. Multicomponent adsorption has not been extensively analyzed with artificial neural network models where this literature review indicated that 87% of references published on this topic covered adsorption systems with only one adsorbate. Results reported in several studies indicated that this artificial intelligence tool has a significant potential to develop reliable models for multicomponent adsorption systems where antagonistic, synergistic, and noninteraction adsorption behaviors can occur simultaneously. The development of reliable artificial neural networks for the modeling of multicomponent adsorption in batch and dynamic systems is fundamental to improve the process engineering in water treatment and purification. |
| first_indexed | 2024-03-07T18:48:48Z |
| format | Article |
| id | doaj.art-eba294615ece46419f4023e6ee63a124 |
| institution | Directory Open Access Journal |
| issn | 2048-4038 |
| language | English |
| last_indexed | 2024-03-07T18:48:48Z |
| publishDate | 2022-01-01 |
| publisher | SAGE Publications |
| record_format | Article |
| series | Adsorption Science & Technology |
| spelling | doaj.art-eba294615ece46419f4023e6ee63a1242024-03-02T02:12:45ZengSAGE PublicationsAdsorption Science & Technology2048-40382022-01-01202210.1155/2022/9384871A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural NetworksHilda Elizabeth Reynel-Ávila0Ismael Alejandro Aguayo-Villarreal1Lizbeth Liliana Diaz-Muñoz2Jaime Moreno-Pérez3Francisco Javier Sánchez-Ruiz4Cintia Karina Rojas-Mayorga5Didilia Ileana Mendoza-Castillo6Adrián Bonilla-Petriciolet7Instituto Tecnológico de AguascalientesUniversidad de ColimaInstituto Tecnológico de AguascalientesInstituto Tecnológico de AguascalientesUniversidad Popular Autónoma del Estado de PueblaUniversidad de ColimaInstituto Tecnológico de AguascalientesInstituto Tecnológico de AguascalientesThe application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a wide spectrum of adsorbents and adsorbates in the context of water purification. Artificial neural networks allow to overcome some drawbacks of traditional adsorption models especially in terms of providing better predictions at different operating conditions. However, these surrogate models have been applied mainly in adsorption systems with only one pollutant thus indicating the importance of extending their application for the prediction and simulation of adsorption systems with several adsorbates (i.e., multicomponent adsorption). This review analyzes and describes the data modeling of adsorption of organic and inorganic pollutants from water with artificial neural networks. The main developments and contributions on this topic have been discussed considering the results of a detailed search and interpretation of more than 250 papers published on Web of Science ® database. Therefore, a general overview of the training methods, input and output data, and numerical performance of artificial neural networks and related models utilized for adsorption data simulation is provided in this document. Some remarks for the reliable application and implementation of artificial neural networks on the adsorption modeling are also discussed. Overall, the studies on adsorption modeling with artificial neural networks have focused mainly on the analysis of batch processes (87%) in comparison to dynamic systems (13%) like packed bed columns. Multicomponent adsorption has not been extensively analyzed with artificial neural network models where this literature review indicated that 87% of references published on this topic covered adsorption systems with only one adsorbate. Results reported in several studies indicated that this artificial intelligence tool has a significant potential to develop reliable models for multicomponent adsorption systems where antagonistic, synergistic, and noninteraction adsorption behaviors can occur simultaneously. The development of reliable artificial neural networks for the modeling of multicomponent adsorption in batch and dynamic systems is fundamental to improve the process engineering in water treatment and purification.http://dx.doi.org/10.1155/2022/9384871 |
| spellingShingle | Hilda Elizabeth Reynel-Ávila Ismael Alejandro Aguayo-Villarreal Lizbeth Liliana Diaz-Muñoz Jaime Moreno-Pérez Francisco Javier Sánchez-Ruiz Cintia Karina Rojas-Mayorga Didilia Ileana Mendoza-Castillo Adrián Bonilla-Petriciolet A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks Adsorption Science & Technology |
| title | A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks |
| title_full | A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks |
| title_fullStr | A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks |
| title_full_unstemmed | A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks |
| title_short | A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks |
| title_sort | review of the modeling of adsorption of organic and inorganic pollutants from water using artificial neural networks |
| url | http://dx.doi.org/10.1155/2022/9384871 |
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