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ASPAD动态模拟和人工神经网络用于GGSWAC包装床列中的阿诺洛尔吸附

Kah Zheng Goh¹, Anis Atikah Ahmad^2,3, Mohd Azmier Ahmad⁴

¹Faculty of Chemical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), 02600, Arau, Perlis, Malaysia.

Environmental science and pollution research international

|December 1, 2023

概括

这项研究评估了阿斯吸附 (ASPAD) 和人工神经网络 (ANN) 模型,用于使用活性炭去除阿提诺洛尔 (ATN). Python ANN 模型准确地预测了吸附行为,性能优于 ASPAD 和 MATLAB 模型.

科学领域:

环境科学环境科学
化学工程是化学工程的重要组成部分.
材料科学材料科学材料科学

背景情况:

废水处理需要有效地去除药物污染物,如阿提诺洛 (ATN).
来自生物质的活性炭,如气化Glyricidia sepium木活性炭 (GGSWAC),显示了吸附的希望.
动态模拟模型对于理解和优化固定床柱中的吸附过程至关重要.

研究的目的:

评估阿斯吸附 (ASPAD) 和人工神经网络 (ANN) 模型在模拟ATN吸附到GGSWAC上的性能.
调查操作参数 (床高度,ATN初始度,流量) 对ATN吸附动态的影响.
为了比较ANN模型 (MATLAB和Python) 与ASPAD对固定床吸附的预测准确度.

主要方法:

使用阿斯吸附 (ASPAD) 软件进行动态模拟.
在 MATLAB 和 Python 中开发和验证人工神经网络 (ANN) 模型.
在固定床柱中使用GGSWAC对ATN吸附的实验研究.
分析影响突破和耗尽时间以及吸附能力的因素.

主要成果:

床的高度增加和ATN初始度增加了吸附能力,而更高的流速保持了它.
ASPAD准确地建模了疲劳点,但在突破时间预测中显示了显著的偏差 (6.52239.53%).
与 MATLAB ANN (R2 = 0.9691) 和 ASPAD.相比,Python ANN 模型 (R2 = 0.985) 显示出更高的预测准确度和适配性能.
最初的ATN度,床高度,影响性流量率和废水时间是影响吸附的关键因素.

结论:

Python ANN 模型提供了一个高度准确和可靠的工具,用于预测固定床列中的 ATN 吸附动态.
操作参数显著影响使用GGSWAC去除ATN的效率.
这项研究为优化基于GGSWAC的废水处理系统的药物清除提供了有价值的见解.

关键词:

人工神经网络的人工神经网络阿斯的吸附方式亚特诺洛尔 (Atenolol) 是一种动态模拟的动态模拟气化格莱里西迪亚 (Glyricidia sepium) 树木片中的木片.

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