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使用土耳其本托尼特和人工神经网络 (ANN) 建模从废水中去除

Ezel Uraz¹, Tugba Hayri-Senel¹, Nalan Erdol-Aydin¹

¹Istanbul Technical University, Chemical and Metallurgical Faculty, Chemical Engineering Department, 34469, Maslak, Istanbul, Turkey.

|December 6, 2024

概括

奥尔杜-乌尼耶托尼特有效地从水中去除. 原始托尼特实现了95%的去除,性能优于酸改性版本,人工神经网络优化了该过程.

科学领域:

环境科学环境科学
材料科学材料科学材料科学
化学工程是化学工程的重要组成部分.

背景情况:

水溶液中的污染对环境和健康构成风险.
班托尼特是一种具有成本效益的吸附剂,用于去除重金属.
优化吸附过程对于有效的废水处理至关重要.

研究的目的:

评估奥尔杜-乌尼耶托尼特作为去除的吸附剂.
为了研究pH值,吸附剂剂量,接触时间和初始度对吸附的影响.
通过人工神经网络评估酸修饰的顿石的性能,并优化该过程.

主要方法:

使用原料和酸修饰 (HNO3,H2SO4) 顿石的吸附实验.
使用富里埃变换红外光谱 (FTIR) 和X射线衍射 (XRD) 进行顿石的表征.
吸附等温的建模 (Langmuir,Temkin) 和使用人工神经网络 (ANN) 的优化与贝叶斯规则化.

主要成果:

原生本托尼特获得了95%的去除,明显高于酸修饰的本托尼特 (58.4%与HNO3,43.8%与H2SO4).
吸附数据与Langmuir和Temkin异热模型相吻合.
在ANN的贝叶斯规范化方法提供了最佳的过程性能,R2=0.995和MSE=0.0008.8.

结论:

奥尔杜-乌涅托尼特是从水溶液中去除的高效吸附剂.
酸性修饰降低了本托尼特对的吸附效率.
人工神经网络,特别是贝叶斯规则化,可以有效地优化吸附过程.

关键词:

吸附方式吸附方式吸附方式人工神经网络的人工神经网络托尼特 (Bentonite) 是一种托尼特.异热体模型的模型建模建模模型是什么优化优化优化优化可以在中找到.

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