Kishor Palle, Sambhani Naga Gayatri, Ramesh Kola, Ch Sandhya Rani, P. Ramesh Babu, L. Vijayalakshmi, Seong Jin Kwon, Md. Mustaq Ali
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引用次数: 0
Abstract
In this study, the effects of artificial neural networks on CO2 adsorption on several types of rice husk activated carbon samples are investigated. Using conventional approach, the eight activated carbon samples are examined for carbon dioxide adsorption at 298 K and up to 1 bar pressure. The influence of altered training/validating ratios, various data initiation points, various training algorithms and number of neurons necessary for an artificial neural network model were investigated using ANN modelling. The work can give useful information on the effects of each of the investigated factors, which are crucial in ANN modelling and training techniques. The results may be used to create an optimum activated carbon, improved applications of gas and oil purification that plan to use artificial intelligence modelling in their evaluations.
摘要 本研究探讨了人工神经网络对几种稻壳活性炭样品吸附二氧化碳的影响。采用传统方法，在 298 K 和高达 1 bar 的压力下检测了 8 种活性炭样品对二氧化碳的吸附情况。使用人工神经网络模型研究了改变训练/验证比例、各种数据起始点、各种训练算法和人工神经网络模型所需的神经元数量的影响。这项工作可以提供有关每个调查因素的影响的有用信息，这些因素对人工神经网络建模和训练技术至关重要。研究结果可用于创建最佳活性炭，改进计划在评估中使用人工智能建模的天然气和石油净化应用。
期刊介绍：
Russian Journal of Applied Chemistry (Zhurnal prikladnoi khimii) was founded in 1928. It covers all application problems of modern chemistry, including the structure of inorganic and organic compounds, kinetics and mechanisms of chemical reactions, problems of chemical processes and apparatus, borderline problems of chemistry, and applied research.
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