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相关概念视频

Coagulation01:06

Coagulation

302
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...
302

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相关实验视频

Updated: Jul 7, 2025

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
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使用具有大规模数据的深度学习模型优化凝固剂剂量.

Jiwoong Kim1, Chuanbo Hua2, Kyoungpil Kim3

  • 1Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea; Korea Water Resources Corporation (k-water), 200 Sintanjin-ro, Daedeok-gu, Deajeon, 34350, South Korea.

Chemosphere
|December 22, 2023
PubMed
概括

这项研究使用深度学习来优化水处理中的凝固剂剂量,实现显著的成本节约和减少化学品的使用. 这种先进的模型提高了饮用水流程的效率和自动化.

关键词:
凝固剂剂量 凝固剂剂量卷积神经网络是一种卷积神经网络.深度学习模型深度学习模型有门的经常性单位.优化优化 优化优化

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科学领域:

  • 环境工程 环境工程
  • 人工智能在水处理中的应用
  • 过程自动化 过程自动化

背景情况:

  • 由于运营方面的挑战,水处理厂需要先进的自动化技术.
  • 优化凝固剂剂量对于高效和成本效益的净水至关重要.
  • 现有的方法缺乏实时,数据驱动的剂量调整的精度.

研究的目的:

  • 开发和验证一个深度学习模型,以优化饮用水处理中的凝固剂剂量.
  • 为了利用全面的五年数据集来进行先进的时间序列建模.
  • 证明模型在减少凝固剂使用和提高工艺效率方面的能力.

主要方法:

  • 利用了结合一维卷积神经网络 (Conv1D) 和封闭循环单元 (GRU) 的深度学习模型.
  • 在5年的分钟为分钟的实时水质数据上训练模型.
  • 根据物理化学模型进行验证预测,并根据度指南应用优化策略.

主要成果:

  • 深度学习模型准确地预测了凝固剂剂量和沉积盆度.
  • 优化凝固剂剂量策略导致化学品使用量显著减少 (约1. 这是22%的22%).
  • 实现了大幅度的成本节约 (约. 2100万韩元/年),同时保持水质标准.

结论:

  • 深度学习模型为优化水处理过程提供了强大的工具.
  • 拟议的Conv1D-GRU模型提高了效率,成本效益,并促进了水处理中的自动化.
  • 这种方法代表了将人工智能应用于饮用水流程管理中的重大进步.