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用生物炭处理放射性废水中的吸附能力的预测

Zening Qu¹, Wei Wang¹, Yan He¹

¹College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China.

|February 23, 2024

概括

研究人员开发了四种基于生物炭的模型,以预测放射性废水中吸附的情况. 这就是"The Fick"的意思.

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.
计算化学计算化学

背景情况:

福岛废水排放需要有效的除去策略.
生物炭为放射性污染物提供了一个有前途的,环保的吸收剂.
精确预测吸附对于废水处理效率至关重要.

研究的目的:

开发和比较使用生物煤的吸附能力的预测模型.
评估与BP神经网络集成的元启发算法的性能.
确定影响生物炭吸附的关键参数.

主要方法:

将反向传播 (BP) 神经网络与四个元启发式算法集成:粒子群优化 (PSO),差异进化 (DE),猎优化 (CO) 和菲克定律算法 (FLA).
开发了四种预测模型:PSO-BP,DE-BP,CO-BP和FLA-BP.
使用确定性系数 (R2),错误率和CEC测试集对模型准确性的定量评估.
使用XGBoost进行参数重要性分析,以确定影响因素.

主要成果:

与PSO,DE和CO相比,基于Fick的定律算法 (FLA) 的模型显示出更高的搜索能力和融合速度.
最初的度 (C0) 和生物炭的总碳含量 (C) 被确定为影响吸附的最重要的参数.
所有四个开发的模型都显示了在实验环境中研究生物炭对吸附的适用性.

结论:

FLA-BP模型为预测吸附能力提供了更有利的方法.
预测建模可以显著降低在试验废水处理中与相关的辐射风险.
这项研究为利用生物炭材料有效处理被污染的废水提供了宝贵的见解.

关键词:

生物炭是一种生物炭.的元启发式算法.预测预测预测预测吸附的吸附方式是吸附.废水处理是废水处理的方法之一.

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