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将基于人工智能的神经网络建模与使用Sargassum fusiforme生物吸收剂Cd(II) 离子吸附的实验性特征集成

P Thamarai¹, R Kamalesh¹, V C Deivayanai¹

¹Department of Biotechnology, Saveetha School of Engineering, SIMATS, Chennai, 602105, India.

Environmental research

|October 13, 2025

概括

状 (Sargassum fusiforme) 有效地从废水中去除 (Cd),达到99.25%的效率. 这种可持续的生物吸收剂显示出对环境修复和公共卫生保护的承诺.

科学领域:

环境科学环境科学
材料科学材料科学材料科学
生物技术是生物技术.

背景情况:

废水中的污染对环境和健康构成重大风险.
有效和可持续的减缓策略对于废水处理至关重要.

研究的目的:

评估 Sargassum fusiforme (SF) 作为从水溶液中去除 (Cd (II)) 离子的生物吸收剂.
描述SF,并阐明Cd的生物吸收机制.
优化生物吸收条件并评估SF的再生潜力.

主要方法:

关于碳水化合物含量的SF的近似分析.
使用SEM,EDX,FTIR和XRD进行表征.
在不同的条件下进行批量生物吸收实验 (pH,剂量,时间,度).
吸附同热,动力和热力学建模.
人工神经网络 (ANN) 用于预测.
通过吸附-脱附循环进行再生研究.

主要成果:

富含多糖的SF (56.8%),表现出有效的Cd (II) 吸附,在吸附后观察到显著的形态和元素变化.
在最佳条件下 (pH6.2 g/L剂量,40分钟,20 mg/L初始度) 产生了99.25%的Cd (II) 清除.
吸附之后是兰迈尔异温 (最大容量144.87 mg/g) 和伪第一阶动力学.
通过ANN模型实现了高预测准确度 (R=0.99).
在8个循环后,SF保持了>60%的效率,表明了良好的可重复使用性.

结论:

藻 (Sargassum fusiforme) 是一种高效且具有成本效益的生物吸收剂,用于从废水中去除.
该研究证实了SF在可持续环境修复应用中的潜力.
ANN建模为预测生物吸收性能提供了可靠的工具.

关键词:

人工神经网络的人工神经网络生物吸收生物吸收是的组成部分.运动学运动学兰木尔等热法兰木尔等热法兰木尔等热法

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