Machine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater
To maintain human health and purity of drinking water, it is crucial to eliminate harmful chemicals such as nitrophenols and azo dyes, considering their natural presence in the surroundings. In this particular research study, the application of machine learning techniques was employed in order to ma...
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my.uniten.dspace-362892025-03-03T15:41:49Z Machine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater Khan M.S.J. Sidek L.M. Kumar P. Alkhadher S.A.A. Basri H. Zawawi M.H. El-Shafie A. Ahmed A.N. 57214778682 35070506500 57206939156 56405495700 57065823300 39162217600 16068189400 57214837520 Algorithms Azo Compounds Catalysis Coloring Agents Machine Learning Nitrophenols Silver Wastewater Water Pollutants, Chemical Water Purification Wastewater treatment azo dye nitrophenol 4-nitrophenol azo compound coloring agent silver % reductions 4-Nitrophenol Aquatic waste Azo-dyes Catalytic performance Dye reduction Machine-learning Nitrophenols ]+ catalyst adaptive moment estimation Article catalysis catalyst electromagnetic radiation electron energy resource learning algorithm long short term memory network machine learning mean absolute error pollutant recurrent neural network time series analysis waste water management wastewater water pollutant algorithm catalysis chemistry isolation and purification procedures water management water pollutant Azo dyes To maintain human health and purity of drinking water, it is crucial to eliminate harmful chemicals such as nitrophenols and azo dyes, considering their natural presence in the surroundings. In this particular research study, the application of machine learning techniques was employed in order to make an estimation of the performance of reduction catalysis in the context of ecologically detrimental nitrophenols and azo dyes contaminants. The catalyst utilized in the experiment was Ag@CMC, which proved to be highly effective in eliminating various contaminants found in water, like 4-nitrophenol (4-NP). The experiments were carefully conducted at various time intervals, and the machine learning procedures used in this study were all employed to forecast catalytic performance. The evaluation of the performance of such algorithms were done by means of Mean Absolute Error. The noteworthy findings of this research indicated that the ADAM and LSTM algorithm exhibited the most favourable performance in the case of toxic compounds i.e. 4-NP. Moreover, the Ag@CMC catalyst demonstrated an impressive reduction efficiency of 98 % against nitrophenol in just 8 min. Thus, based on these compelling results, it can be concluded that Ag@CMC works as a highly effective catalyst for practical applications in real-world scenarios. ? 2024 Elsevier B.V. Final 2025-03-03T07:41:49Z 2025-03-03T07:41:49Z 2024 Article 10.1016/j.ijbiomac.2024.134701 2-s2.0-85201860374 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201860374&doi=10.1016%2fj.ijbiomac.2024.134701&partnerID=40&md5=cd04ecc2d1a2aa249f9063016a7b785c https://irepository.uniten.edu.my/handle/123456789/36289 278 134701 Elsevier B.V. Scopus |
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Algorithms Azo Compounds Catalysis Coloring Agents Machine Learning Nitrophenols Silver Wastewater Water Pollutants, Chemical Water Purification Wastewater treatment azo dye nitrophenol 4-nitrophenol azo compound coloring agent silver % reductions 4-Nitrophenol Aquatic waste Azo-dyes Catalytic performance Dye reduction Machine-learning Nitrophenols ]+ catalyst adaptive moment estimation Article catalysis catalyst electromagnetic radiation electron energy resource learning algorithm long short term memory network machine learning mean absolute error pollutant recurrent neural network time series analysis waste water management wastewater water pollutant algorithm catalysis chemistry isolation and purification procedures water management water pollutant Azo dyes |
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Algorithms Azo Compounds Catalysis Coloring Agents Machine Learning Nitrophenols Silver Wastewater Water Pollutants, Chemical Water Purification Wastewater treatment azo dye nitrophenol 4-nitrophenol azo compound coloring agent silver % reductions 4-Nitrophenol Aquatic waste Azo-dyes Catalytic performance Dye reduction Machine-learning Nitrophenols ]+ catalyst adaptive moment estimation Article catalysis catalyst electromagnetic radiation electron energy resource learning algorithm long short term memory network machine learning mean absolute error pollutant recurrent neural network time series analysis waste water management wastewater water pollutant algorithm catalysis chemistry isolation and purification procedures water management water pollutant Azo dyes Khan M.S.J. Sidek L.M. Kumar P. Alkhadher S.A.A. Basri H. Zawawi M.H. El-Shafie A. Ahmed A.N. Machine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater |
| description |
To maintain human health and purity of drinking water, it is crucial to eliminate harmful chemicals such as nitrophenols and azo dyes, considering their natural presence in the surroundings. In this particular research study, the application of machine learning techniques was employed in order to make an estimation of the performance of reduction catalysis in the context of ecologically detrimental nitrophenols and azo dyes contaminants. The catalyst utilized in the experiment was Ag@CMC, which proved to be highly effective in eliminating various contaminants found in water, like 4-nitrophenol (4-NP). The experiments were carefully conducted at various time intervals, and the machine learning procedures used in this study were all employed to forecast catalytic performance. The evaluation of the performance of such algorithms were done by means of Mean Absolute Error. The noteworthy findings of this research indicated that the ADAM and LSTM algorithm exhibited the most favourable performance in the case of toxic compounds i.e. 4-NP. Moreover, the Ag@CMC catalyst demonstrated an impressive reduction efficiency of 98 % against nitrophenol in just 8 min. Thus, based on these compelling results, it can be concluded that Ag@CMC works as a highly effective catalyst for practical applications in real-world scenarios. ? 2024 Elsevier B.V. |
| author2 |
57214778682 |
| author_facet |
57214778682 Khan M.S.J. Sidek L.M. Kumar P. Alkhadher S.A.A. Basri H. Zawawi M.H. El-Shafie A. Ahmed A.N. |
| format |
Article |
| author |
Khan M.S.J. Sidek L.M. Kumar P. Alkhadher S.A.A. Basri H. Zawawi M.H. El-Shafie A. Ahmed A.N. |
| author_sort |
Khan M.S.J. |
| title |
Machine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater |
| title_short |
Machine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater |
| title_full |
Machine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater |
| title_fullStr |
Machine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater |
| title_full_unstemmed |
Machine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater |
| title_sort |
machine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater |
| publisher |
Elsevier B.V. |
| publishDate |
2025 |
| _version_ |
1825816101570740224 |
| score |
13.244413 |