Clean technology for sequestering Rhodamine B dye on modified mango pod using artificial intelligence techniques
In this study, an acid-modified mango pod (AMMP) was prepared as adsorbent for removal of Rhodamine dye and the adsorption uptake was also determined using machine learning algorithms namely, Artificial Neural network (ANN) and Adaptive neuro-fuzzy inference systems (ANFIS) respectively. The prepare...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Current Research in Green and Sustainable Chemistry |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666086522000170 |
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| author | Kayode Adesina Adegoke Oluwatobi Adeleke Morenike Oluwabunmi Adesina Rhoda Oyeladun Adegoke Olugbenga Solomon Bello |
| author_facet | Kayode Adesina Adegoke Oluwatobi Adeleke Morenike Oluwabunmi Adesina Rhoda Oyeladun Adegoke Olugbenga Solomon Bello |
| author_sort | Kayode Adesina Adegoke |
| collection | DOAJ |
| description | In this study, an acid-modified mango pod (AMMP) was prepared as adsorbent for removal of Rhodamine dye and the adsorption uptake was also determined using machine learning algorithms namely, Artificial Neural network (ANN) and Adaptive neuro-fuzzy inference systems (ANFIS) respectively. The prepared adsorbent was characterized with SEM, FTIR, EDX, and PXRD techniques. Surface chemistry revealed the presence of O–H stretching of free hydroxyls and alcohols and phenols, C–H stretch of alkanes, C≡C stretch of alkynes, CO stretch of ketones, lactones, and carboxylic anhydrides, –CC- stretching of alkenes, C–N stretch of aliphatic amines and O–H bend of carboxylic acids. Surface morphologies of the AMMP revealed well-developed and open porous surfaces needed for an efficient adsorption of the dye molecule. EDX analysis showed an increase in the carbon contents from 79.94% (raw) to 80.12% (AMMP) by weight and 86.89% (raw) to 89.11% (AMMP) by atom. The crystallinity structure revealed the new and intense peak formations and the presence of ordered (organized) crystalline structures on AMMP. Optimal ANN architectures, activation functions and training algorithms were selected after several stimulations with different network parameters while the ANFIS model was tested with three clustering approaches namely, Grid partitioning, fuzzy c-means, and subtractive clustering to carry out the extensive studies to optimally predict the adsorption efficiency/capacity of Rhodamine dye onto AMMP. The performance of the developed models was evaluated using the following statistical metrics; the optimal ANN model gave RMSE = 10.422, MAD = 3.673, MAPE = 5.409, R2 = 0.977 while with optimal ANFIS model gave a RMSE = 9.246, MAD = 4.938, MAPE = 4.672, R2 = 0.984 at the testing phase. The lower value of the statistical parameters indicates better performance in both models. Batch adsorption studies gave qmax of 456.67 mg/g, 389.51 mg/g, and 410.56 mg/g for the experimental data, ANN and ANFIS models respectively thus, suggesting their good correlations. Technoeconomic aspects of the study present that AMMP is approximately 8 times cheaper, translating to saving cost of 225.2 USD/kg when compared with 259.5 USD/kg for commercial activated carbon. |
| first_indexed | 2024-04-11T06:04:46Z |
| format | Article |
| id | doaj.art-ea81197c6aa84d438499dd3119f2c1e2 |
| institution | Directory Open Access Journal |
| issn | 2666-0865 |
| language | English |
| last_indexed | 2024-04-11T06:04:46Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
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| series | Current Research in Green and Sustainable Chemistry |
| spelling | doaj.art-ea81197c6aa84d438499dd3119f2c1e22022-12-22T04:41:32ZengElsevierCurrent Research in Green and Sustainable Chemistry2666-08652022-01-015100275Clean technology for sequestering Rhodamine B dye on modified mango pod using artificial intelligence techniquesKayode Adesina Adegoke0Oluwatobi Adeleke1Morenike Oluwabunmi Adesina2Rhoda Oyeladun Adegoke3Olugbenga Solomon Bello4Department of Chemical Sciences, University of Johannesburg, Doornfontein 2028, South Africa; Corresponding author.Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South Africa; Corresponding author.Department of Chemical Sciences, Lead City University, NigeriaDepartment of Pure and Applied Chemistry, Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Oyo State, NigeriaDepartment of Pure and Applied Chemistry, Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Oyo State, Nigeria; Corresponding author.In this study, an acid-modified mango pod (AMMP) was prepared as adsorbent for removal of Rhodamine dye and the adsorption uptake was also determined using machine learning algorithms namely, Artificial Neural network (ANN) and Adaptive neuro-fuzzy inference systems (ANFIS) respectively. The prepared adsorbent was characterized with SEM, FTIR, EDX, and PXRD techniques. Surface chemistry revealed the presence of O–H stretching of free hydroxyls and alcohols and phenols, C–H stretch of alkanes, C≡C stretch of alkynes, CO stretch of ketones, lactones, and carboxylic anhydrides, –CC- stretching of alkenes, C–N stretch of aliphatic amines and O–H bend of carboxylic acids. Surface morphologies of the AMMP revealed well-developed and open porous surfaces needed for an efficient adsorption of the dye molecule. EDX analysis showed an increase in the carbon contents from 79.94% (raw) to 80.12% (AMMP) by weight and 86.89% (raw) to 89.11% (AMMP) by atom. The crystallinity structure revealed the new and intense peak formations and the presence of ordered (organized) crystalline structures on AMMP. Optimal ANN architectures, activation functions and training algorithms were selected after several stimulations with different network parameters while the ANFIS model was tested with three clustering approaches namely, Grid partitioning, fuzzy c-means, and subtractive clustering to carry out the extensive studies to optimally predict the adsorption efficiency/capacity of Rhodamine dye onto AMMP. The performance of the developed models was evaluated using the following statistical metrics; the optimal ANN model gave RMSE = 10.422, MAD = 3.673, MAPE = 5.409, R2 = 0.977 while with optimal ANFIS model gave a RMSE = 9.246, MAD = 4.938, MAPE = 4.672, R2 = 0.984 at the testing phase. The lower value of the statistical parameters indicates better performance in both models. Batch adsorption studies gave qmax of 456.67 mg/g, 389.51 mg/g, and 410.56 mg/g for the experimental data, ANN and ANFIS models respectively thus, suggesting their good correlations. Technoeconomic aspects of the study present that AMMP is approximately 8 times cheaper, translating to saving cost of 225.2 USD/kg when compared with 259.5 USD/kg for commercial activated carbon.http://www.sciencedirect.com/science/article/pii/S2666086522000170Acid-modified mango podRhodamine B dyeAdsorptionArtificial neural networkAdaptive neuro-fuzzy inference systemsEconomic feasibility |
| spellingShingle | Kayode Adesina Adegoke Oluwatobi Adeleke Morenike Oluwabunmi Adesina Rhoda Oyeladun Adegoke Olugbenga Solomon Bello Clean technology for sequestering Rhodamine B dye on modified mango pod using artificial intelligence techniques Current Research in Green and Sustainable Chemistry Acid-modified mango pod Rhodamine B dye Adsorption Artificial neural network Adaptive neuro-fuzzy inference systems Economic feasibility |
| title | Clean technology for sequestering Rhodamine B dye on modified mango pod using artificial intelligence techniques |
| title_full | Clean technology for sequestering Rhodamine B dye on modified mango pod using artificial intelligence techniques |
| title_fullStr | Clean technology for sequestering Rhodamine B dye on modified mango pod using artificial intelligence techniques |
| title_full_unstemmed | Clean technology for sequestering Rhodamine B dye on modified mango pod using artificial intelligence techniques |
| title_short | Clean technology for sequestering Rhodamine B dye on modified mango pod using artificial intelligence techniques |
| title_sort | clean technology for sequestering rhodamine b dye on modified mango pod using artificial intelligence techniques |
| topic | Acid-modified mango pod Rhodamine B dye Adsorption Artificial neural network Adaptive neuro-fuzzy inference systems Economic feasibility |
| url | http://www.sciencedirect.com/science/article/pii/S2666086522000170 |
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