基于响应面法的人工神经网络优化黄麻基芯材制备碳酸钾活性炭工艺

L. Natrayan, V. Niveditha, S. Kaliappan, Pravin P. Patil, C. Pandian, Y. Rao, P. Murugan
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引用次数: 0

摘要

研究了碳酸钾作为黄麻芯制备含碳材料的新材料。两个二次模型已经为这两个答案建立了联系准备参数:活化温度,摩尔比和孵育时间。RSM和ANN模型用于改善加工条件,以最大限度地提高碘和亚甲基蓝的渗透率。在活化温度为900℃、摩尔比为1.5、活化时间为4小时的条件下,得到了最佳的活性炭。这导致碘和亚甲蓝的吸收率分别为1260.07 mg/g和369.21 mg/g。研究发现,以k2co3为基础的热解工艺有望成为一种安全而高效的工艺,可以生产具有良好定义和单一文化多孔结构的活性炭。尽管目前对K2CO3的确切强调尚不清楚,但考虑到K2C3O4在大约870°C的温度下随着CO的摩尔浓度增加而产生,这些由K2CO3刺激开始的相同的多孔和丘泡反应可能暂时被认为与K2C3O4产生进展所需的起始作用相当。本研究采用方差分析如ANOVA检验来检验控制参数的影响。此外,采用响应面(RSM)和人工神经网络(ANN)对输出结果进行了改进,同时优化了亚甲基蓝和碘的质量。因此,实验结果与统计数据相吻合。
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Optimization Process of Potassium Carbonate Activated Carbon through Jute-Based Core Materials by Using Artificial Neural Network with Response Surface Methodology
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.
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