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机器学习和p-nitrophenol修复使用可持续活性碳的机制研究

Ashalakshmi Kodandoor¹, Gokulakrishnan Murugesan², Thivaharan Varadavenkatesan¹

¹Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.

Scientific reports

|March 4, 2026

概括

这项研究开发了一种可持续的方法,使用Pistacia vera贝中的活性炭去除p-nitrophenol (pNP). 这种环保吸附剂显示出高效率和可用于废水处理的可重复使用性.

科学领域:

环境化学环境化学
材料科学材料科学材料科学
绿色化学绿色化学

背景情况:

酸 (p-nitrophenol,简称pNP) 是一种在工业废水中发现的有毒污染物.
有效和可持续的PNP去除方法对于环境保护至关重要.
农业废物的利用,比如Pistacia vera贝,为吸附材料提供了一个可持续的来源.

研究的目的:

从Pistacia vera贝中合成活性碳,以有效地去除p-nitrophenol (pNP).
描述合成的活性炭并评估其吸附性能.
开发吸附行为预测模型,并评估吸附剂的实际应用.

主要方法:

使用H3PO4激活,从Pistacia vera贝中合成活性碳.
活性炭表面积,孔隙结构和功能组的表征.
吸附实验以确定动力学 (伪二阶) 和同热量 (Langmuir).
热力学分析和人工神经网络 (ANN) 和自适应神经模糊推理系统 (ANFIS) 模型的开发.
在真实水矩阵中进行再生研究和测试.

主要成果:

合成的活性炭表现出高特异性表面积 (670.25 m2 g-1) 和功能化的表面.
最大吸附能力达到了142.93毫克g-1,遵循兰格穆尔等温和伪二次动力学.
吸附过程是自发的和外热的 (-13.43 kJ mol-1).
ANFIS模型显示出优异的预测性能 (R2 = 0.9935),接触时间和初始PNP度是关键因素.
吸附剂在5个循环中证明了可重复使用性和在真实水样中有效的PNP去除.

结论:

来自Pistacia vera贝的废物衍生活性炭是一种高效和可持续的吸附剂,用于pNP去除.
合成材料为处理pNP污染水提供了低成本,环保的解决方案.
该研究强调了农业废物利用在开发环境修复先进材料方面的潜力.

关键词:

皮斯塔西亚真 (Pistacia vera) 是一种植物.这是一种p-nitrophenol.活性炭是一种活性炭.机器学习是机器学习.污水处理污水处理污水处理

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