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人工智能优化和可控制的缓释硫化铁实现了强烈酸性废水中铜和的有效分离

Xingfei Zhang¹, Chenglong Lu², Jia Tian¹

¹School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, China.

Journal of environmental sciences (China)

|December 17, 2023

概括

硫化铁 (FeS) 有效地将铜和从酸性废水中分离出来. 优化老化和预测模型提高了分离效率,这对于环境修复至关重要.

科学领域:

环境化学环境化学
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:

由于重金属污染,酸性废水带来了环境挑战.
从工业废水中分离铜和需要高效和有选择性的方法.
硫化铁 (FeS) 显示了通过缓慢的硫化物释放来选择性去除金属离子的潜力.

研究的目的:

开发可控制的硫化铁 (FeS) 纳米粒子,用于选择性地分离铜和.
确定影响酸性废水分离效率的关键因素.
为优化分离过程建立一个预测模型.

主要方法:

使用老化方法制备硫化铁 (FeS) 纳米粒子.
相互对应的实验设计,以研究影响因素 (Cu/As比,H2SO4度,FeS剂量).
使用遗传算法 (GA) 开发和优化反向传播人工神经网络 (BP-ANN) 模型.

主要成果:

老化FeS纳米颗粒允许削弱去除,同时保持铜去除.
确定了Cu/As比率,硫酸度和FeS剂量作为关键因素.
优化的BP-GA模型实现了高相关系数 (R = 0.9923).
预测的最佳条件 (高温,长时间老化) 产生了1400的最大分离因子.

结论:

可控制的FeS纳米粒子和预测性BP-GA模型为铜和分离提供了灵活的方法.
开发的方法对于处理高度酸性废水是有效的.
这项研究提供了一个可行的材料和方法,用于重金属污染的环境补救.

关键词:

衰老的衰老衰老的衰老人工神经网络的人工神经网络这是FeS纳米粒子.在正交实验中进行了正交实验.预测预测预测硫化物沉的沉情况

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