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使用PSO-ANN模型预测Cu (II) 修改的波梅罗皮的吸附能力

Mengqing Jiao¹, Johan Jacquemin², Ruixue Zhang¹

¹Hebei Key Laboratory of Green Development of Rock and Mineral Materials, Hebei GEO University, Shijiazhuang 050031, China.

Molecules (Basel, Switzerland)

|October 14, 2023

概括

一个新的粒子群优化人工神经网络 (PSO-ANN) 模型使用修改的果皮改进了Cu(II) 吸附能力的预测. 与传统的人工神经网络 (ANN) 相比,这种方法提高了准确性和概括性.

科学领域:

环境科学环境科学
计算化学计算化学
材料科学材料科学材料科学

背景情况:

传统的人工神经网络 (ANN) 在参数优化过程中经常与局部优化作斗争.
准确预测重金属吸附对于环境修复至关重要.
农业废物材料显示出作为具有成本效益的生物吸收剂的潜力.

研究的目的:

开发一个粒子群集优化人工神经网络 (PSO-ANN) 模型,以更好地预测Cu (II) 吸附能力.
提高吸附能力预测的准确性和概括能力.
为优化使用生物吸收剂进行重金属吸附预测提供一种替代方法.

主要方法:

通过使用改性果皮 (MPP) 对Cu(II) 吸附的实验数据创建了一个数据库.
通过优化传统ANN的权重和偏差,开发了一个PSO-ANN模型.
将PSO-ANN模型的性能与标准ANN模型进行了比较.

主要成果:

与传统的ANN相比,PSO-ANN模型显示出更高的概括能力.
该PSO-ANN模型在预测Cu (II) 吸附能力方面取得了更高的准确性.
混合PSO-ANN方法有效地解决了ANN中的局部最佳问题.

结论:

该PSO-ANN模型提供了一个更准确,更可靠的方法来预测重金属吸附能力.
经过修改的葡萄皮显示出作为Cu (II) 除去的生物吸收剂的前景.
这种混合建模方法为优化农业废弃物在环境应用中的使用提供了有价值的工具.

关键词:

格森方程是指一个方程.皮尔森相关性皮尔森相关性吸附能力吸附能力吸附能力人工神经网络的人工神经网络协弦振幅方法的方法粒子群集优化粒子群集优化

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