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人工智能模型用于使用功能化碳纳米管去除甲蓝.

Abd-Alkhaliq Salih Mijwel1, Ali Najah Ahmed2,3, Haitham Abdulmohsin Afan4

  • 1Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Kajang, Selangor, Malaysia.

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概括

人工智能 (AI) 模型有效地预测了功能化碳纳米管 (CNT) 的甲蓝 (MB) 吸附能力. 这项研究优化了CNT合成,并验证了AI.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 环境化学环境化学
  • 计算化学的计算化学

背景情况:

  • 功能化碳纳米管 (CNTs) 是有前途的吸附剂,用于去除污染物.
  • 甲蓝 (MB) 是一种常见的模型污染物,用于评估吸附剂性能.
  • 优化CNT合成对于最大限度地提高吸附效率至关重要.

研究的目的:

  • 评估使用人工智能 (AI) 建模甲蓝 (MB) 在功能化碳纳米管 (CNT) 上吸附的可行性.
  • 确定通过乙二氧化解合成CNT的最佳条件,以增强MB吸附.
  • 评估不同AI模型的性能,包括循环神经网络 (RNN) 和前神经网络 (FFNN),用于预测MB吸附能力.

主要方法:

  • 在550°C的最佳温度,37.3分钟的反应时间和1.0.0的H2/C2H2气体比率下,通过乙烯热解合成CNT.
  • 在CNTs上MB的实验吸附数据与动力 (伪二阶) 和异温 (Langmuir) 模型相匹配.
  • 人工智能模型,特别是RNN和FFNN,使用实验数据开发和训练来预测MB吸附能力.

主要成果:

  • CNT合成产生了MB吸附的最佳特性.
  • 实验MB吸附数据显示,它与伪二次运动模型 (R2=0.998) 和兰慕尔异热模型 (R2=0.989,qm=250.0 mg/g) 非常适合.
  • 人工智能建模显示出高预测精度,FFNN达到R2=0.9658,RNN达到R2=0.9471,这表明预测和实验吸附能力之间存在很强的相关性.

结论:

  • 人工智能建模,特别是FFNN,显示出很大的潜力,可以准确预测CNTs的吸附性能,以消除MB.
  • 在特定的热解条件下合成的优化CNT表现出高的MB吸附能力.
  • 人工智能与实验数据的整合提供了一个强大的方法来提高基于CNT的废水处理解决方案的效率.