氨基功能化新型生物吸收剂,有效地从水中去除化物:使用人工神经网络和机械洞察力的过程优化
Dipankar Jena1, Anjan Kumar Bej2, Anil Kumar Giri3
1Department of Chemistry, Fakir Mohan University, Vyasa Vihar, Balasore, Odisha, 756089, India. dipankarjena.chem@gmail.com.
概括
这项研究开发了一种从Limonia acidissima L.果中提取的新型吸附剂,用于从水中去除化物. 该材料表现出高脱效率,通过人工神经网络和异热模型进行验证.
科学领域:
- 环境科学 环境科学
- 材料科学 材料科学 材料科学
- 水处理 处理水的方法
背景情况:
- 水中化物污染对饮用水安全构成重大全球威胁.
- 开发具有成本效益和高效的除方法对于公共卫生至关重要.
研究的目的:
- 开发和鉴定Limonia acidissima L. (水果) 的新型吸附剂,用于水性脱.
- 为了研究吸附性能和优化实验条件.
- 通过人工神经网络 (ANN) 建模来验证吸附过程.
主要方法:
- 使用TGA-DTA,ATR-FTIR,SEM-EDS和XRD进行吸附剂表征.
- 批量吸附实验研究pH,剂量和时间的影响.
- 人工神经网络 (ANN) 与反向传播学习算法 (BPNN) 的应用.
- 使用兰格穆尔等温和和伪二次运动模型进行分析.
主要成果:
- 开发的吸附剂的最大化物去除效率为80.8 mg/g.
- 最佳条件是pH值为7,剂量为6g/L,接触时间为30分钟.
- 兰木尔等温模型显示出极好的匹配 (R2 > 0.997),而ANN模型实现了高相关性 (R2 = 0.9964).
- 吸附剂在真实水样和干扰离子上表现出令人满意的性能.
结论:
- 利莫尼亚酸度极高 L. 果是一种有希望的,低成本的吸附剂,用于有效的水性脱化.
- 吸附过程由兰格穆尔等温和和伪二次运动模型描述得很好.
- ANN建模准确验证了实验吸附数据,证实了吸附剂在实际水处理应用中的潜力.
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