Optimization Process of Potassium Carbonate Activated Carbon through Jute-Based Core Materials by Using Artificial Neural Network with Response Surface Methodology

Autor: L. Natrayan, V. R. Niveditha, S. Kaliappan, Pravin P. Patil, C. K. Arvinda Pandian, Y. Sesha Rao, P. Murugan
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Adsorption Science & Technology, Vol 2023 (2023)
Druh dokumentu: article
ISSN: 2048-4038
DOI: 10.1155/2023/8674382
Popis: Potassium carbonate was tested as novel information for producing carbonaceous materials from jute cores. Two quadratic models have been developed for both answers to link the preparatory parameters: activating temperatures, molar ratio, and incubation time. The RSM and ANN models were used to improve the processing conditions to maximise the quantities of iodine and methylene blue penetration. The best charcoal was obtained using 900°C activating temperatures, a 1.5 molar ratio, and a 4-hour activating time. This resulted in iodine and methylene blue absorption of 1260.07 mg/g and 369.21 mg/g, respectively. It was discovered that the K2CO3-based pyrolysis process might be anticipated to become a safe yet incredibly efficient process of making activated carbons with a very well-defined and monocultural porous structure. Even though the precise emphasis given to K2CO3 is unknown at the moment, given the creation of K2C3O4 just after evolvement with one additional molarity of CO at approximately 870°C, these same porous and papule responses begun by K2CO3 stimulation might be temporarily posited to be quite comparable to an initiation action needed to make progress by K2C3O4. The influence of control parameters was examined in this study using variance analysis like the ANOVA test. Furthermore, the response surface (RSM) and artificial neural networks (ANN) are employed to improve the output results while optimising the methylene blue and iodine qualities. Consequently, the experimental findings correlate well with the statistics.
Databáze: Directory of Open Access Journals
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