Optimization of activated carbon monolith Co3O4-based catalyst for simultaneous SO2/NOx removal from flue gas using response surface methodology

Coal combustion is a primary source of acid gases such as sulfur dioxide (SO2) and nitrogen dioxide (NOx); meanwhile, their effects are detrimental to man and the environment. In this work, three different adsorbents were developed by loading Co3O4 on monolith using pore volume impregnation, deposit...

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Main Authors: Silas, Kiman, Wan Ab. Karim Ghani, Wan Azlina, Choong, Thomas Shean Yaw, Rashid, Umer
Format: Article
Language:English
Published: Taylor & Francis 2020
Online Access:http://psasir.upm.edu.my/id/eprint/87004/1/Optimization%20of%20activated%20carbon%20monolith.pdf
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author Silas, Kiman
Wan Ab. Karim Ghani, Wan Azlina
Choong, Thomas Shean Yaw
Rashid, Umer
author_facet Silas, Kiman
Wan Ab. Karim Ghani, Wan Azlina
Choong, Thomas Shean Yaw
Rashid, Umer
author_sort Silas, Kiman
collection UPM
description Coal combustion is a primary source of acid gases such as sulfur dioxide (SO2) and nitrogen dioxide (NOx); meanwhile, their effects are detrimental to man and the environment. In this work, three different adsorbents were developed by loading Co3O4 on monolith using pore volume impregnation, deposition precipitation, and hydrothermal methods (HMs) of synthesis. The breakthrough study in the simultaneous SO2 and NOx removal from flue gas revealed that the performance of the adsorbent developed by HM was better than the adsorbents developed by the other methods. Therefore, the characterization analysis and optimization of the variables that affect the adsorption capacity on the best performed adsorbent by response surface methodology were carried out. The model prediction and the experimental results for the adsorption capacity of SO2 are 134.5 and 132.9 mg/g while 152.1 and 151.6 mg/g were obtained for NOx. Furthermore, the optimized independent variables comprising amount of adsorbents, airflow rate, and temperature are 1 mg, 400 mL/min, and 100°C. The regression coefficient of 0.9934 for SO2 and 0.9991 for NOx was obtained which indicates that the interaction between the independent variables and the adsorption capacity of SO2 and NOx is very significant. These results confirmed the suitability of the model for the prediction of the process behavior and the performance of the adsorbent at low temperature with high adsorption capacity emerged as a new finding.
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spelling upm.eprints-870042022-01-10T08:07:12Z http://psasir.upm.edu.my/id/eprint/87004/ Optimization of activated carbon monolith Co3O4-based catalyst for simultaneous SO2/NOx removal from flue gas using response surface methodology Silas, Kiman Wan Ab. Karim Ghani, Wan Azlina Choong, Thomas Shean Yaw Rashid, Umer Coal combustion is a primary source of acid gases such as sulfur dioxide (SO2) and nitrogen dioxide (NOx); meanwhile, their effects are detrimental to man and the environment. In this work, three different adsorbents were developed by loading Co3O4 on monolith using pore volume impregnation, deposition precipitation, and hydrothermal methods (HMs) of synthesis. The breakthrough study in the simultaneous SO2 and NOx removal from flue gas revealed that the performance of the adsorbent developed by HM was better than the adsorbents developed by the other methods. Therefore, the characterization analysis and optimization of the variables that affect the adsorption capacity on the best performed adsorbent by response surface methodology were carried out. The model prediction and the experimental results for the adsorption capacity of SO2 are 134.5 and 132.9 mg/g while 152.1 and 151.6 mg/g were obtained for NOx. Furthermore, the optimized independent variables comprising amount of adsorbents, airflow rate, and temperature are 1 mg, 400 mL/min, and 100°C. The regression coefficient of 0.9934 for SO2 and 0.9991 for NOx was obtained which indicates that the interaction between the independent variables and the adsorption capacity of SO2 and NOx is very significant. These results confirmed the suitability of the model for the prediction of the process behavior and the performance of the adsorbent at low temperature with high adsorption capacity emerged as a new finding. Taylor & Francis 2020-03-26 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/87004/1/Optimization%20of%20activated%20carbon%20monolith.pdf Silas, Kiman and Wan Ab. Karim Ghani, Wan Azlina and Choong, Thomas Shean Yaw and Rashid, Umer (2020) Optimization of activated carbon monolith Co3O4-based catalyst for simultaneous SO2/NOx removal from flue gas using response surface methodology. Combustion Science and Technology, 192 (5). 786 - 803. ISSN 0010-2202; ESSN: 1563-521X https://www.tandfonline.com/doi/abs/10.1080/00102202.2019.1594797?journalCode=gcst20 10.1080/00102202.2019.1594797
spellingShingle Silas, Kiman
Wan Ab. Karim Ghani, Wan Azlina
Choong, Thomas Shean Yaw
Rashid, Umer
Optimization of activated carbon monolith Co3O4-based catalyst for simultaneous SO2/NOx removal from flue gas using response surface methodology
title Optimization of activated carbon monolith Co3O4-based catalyst for simultaneous SO2/NOx removal from flue gas using response surface methodology
title_full Optimization of activated carbon monolith Co3O4-based catalyst for simultaneous SO2/NOx removal from flue gas using response surface methodology
title_fullStr Optimization of activated carbon monolith Co3O4-based catalyst for simultaneous SO2/NOx removal from flue gas using response surface methodology
title_full_unstemmed Optimization of activated carbon monolith Co3O4-based catalyst for simultaneous SO2/NOx removal from flue gas using response surface methodology
title_short Optimization of activated carbon monolith Co3O4-based catalyst for simultaneous SO2/NOx removal from flue gas using response surface methodology
title_sort optimization of activated carbon monolith co3o4 based catalyst for simultaneous so2 nox removal from flue gas using response surface methodology
url http://psasir.upm.edu.my/id/eprint/87004/1/Optimization%20of%20activated%20carbon%20monolith.pdf
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