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相关概念视频

Extraction: Advanced Methods00:56

Extraction: Advanced Methods

1.1K
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
1.1K
Coagulation01:06

Coagulation

1.2K
Colloidal solids are solid particles suspended in solution. They are usually negatively charged, attracting a compact primary layer of positively charged ions, which attract more counterions to form an electrical double layer. Electrostatic repulsion between the charged double layers prevents the particles from colliding, stabilizing the colloids. These solids are often undesirable because they can contain toxins that are difficult to remove. Coagulation is a technique that helps aggregate and...
1.2K
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

4.0K
Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
4.0K
Qualitative Analysis03:46

Qualitative Analysis

23.6K
For solutions containing mixtures of different cations, the identity of each cation can be determined by qualitative analysis. This technique involves a series of selective precipitations with different chemical reagents, each reaction producing a characteristic precipitate for a specific group of cations. Metal ions within a group are further separated by varying the pH, heating the mixture to redissolve a precipitate, or adding other reagents to form complex ions.
For instance, group IV...
23.6K

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使用机器学习模型预测基于色素的浮性重金属去除.

Zaher Mundher Yaseen1,2, Ziaul Haq Doost1, Rauf Khan1

  • 1Department of Civil and Environmental Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.

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概括

机器学习模型准确地预测了使用基托基花剂从废水中去除重金属的情况. 升级梯度增强回归器 (HGBR) 在联合金属去除方面表现出强的性能,有助于环境监测.

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科学领域:

  • 环境科学 环境科学
  • 水处理技术水处理技术
  • 计算化学计算化学

背景情况:

  • 重金属污染对环境和公共健康构成重大风险.
  • 有效的废水处理需要精确的监测和修复策略.
  • 基于酸盐的花剂 (CBF) 显示出重金属去除的前景.

研究的目的:

  • 评估新的机器学习 (ML) 模型,以预测使用CBF的重金属 (HM) 清除效率.
  • 评估梯度增强回归器 (GBR),历史梯度增强回归器 (HGBR),随机森林回归器 (RFR) 和极端梯度增强回归器 (XGBR) 的性能.
  • 通过结合K-means集群标签来提高ML模型的准确性.

主要方法:

  • 开发了四个ML模型 (GBR,HGBR,RFR,XGBR) 使用484个花试验的数据集.
  • 包括K-means集群标签作为改进模型学习的额外功能.
  • 测试的模型预测了 (Cd2+),铜 (Cu2+), (Ni2+), (Pb2+) 和 (Zn2+) 的去除.

主要成果:

  • HGBR模型在组合的HM移除中表现出优异的性能 (R2 = 0.94/0.97用于测试/培训).
  • 所有模型都实现了高精度的单个金属去除,特别是 (Ni2+).
  • 在单个金属测试中,GBR模型的错误率最低.

结论:

  • 由于其强大的概括能力,HGBR模型是环境监测的可靠工具.
  • ML模型显示了优化废水处理中的HM清除过程的巨大潜力.
  • 未来的工作重点应该是将这些模型集成到实时监测系统中,并探索更广泛的环境应用.