优化带来了高能效的化和酸对各种材料的吸附,并利用机器学习进行了优化
Jinsheng Huang1, Waqar Muhammad Ashraf2, Talha Ansar3
1School of Environmental Science and Engineering, Guangzhou University, Guangzhou 510006, PR China.
Water research
|December 4, 2024
概括
机器学习模型准确地预测了各种材料上的 (酸和酸盐) 吸附,优化了水处理. 这种方法识别了有效的材料和条件去除,确保更安全的饮用水.
科学领域:
- 环境科学与工程环境科学与工程
- 材料科学 材料科学 材料科学
- 计算化学计算化学
背景情况:
- 水中的污染是全球环境和健康问题.
- 预测在材料上的吸附对于水资源整治至关重要,但具有挑战性.
- 能源消耗是优化吸附过程的关键因素.
研究的目的:
- 开发精确的机器学习模型,用于预测化物 () 和化物 () 的吸附能力.
- 确定最佳的材料和条件,以节能地从水中去除.
- 创建一个用户友好的Web应用程序来估计吸附.
主要方法:
- 收集了各种材料中吸附的文献数据.
- 训练有素的机器学习模型 (CatBoost,XGBoost,LGBoost) 使用材料特性和反应参数.
- 采用遗传优化来确定最大吸附能力与低能耗.
主要成果:
- CatBoost模型实现了高准确度 (R2=0.99) 对于As(III) 和As(V) 吸附预测.
- 最初的度是吸附的关键影响因素.
- 使用特定的复合材料,确定了291.66 mg/g的As (III) 和271.56 mg/g的As (V) 的最佳吸附能力.
结论:
- 机器学习可以有效地预测和优化用于水处理的节能吸附.
- 开发的模型和一个Web应用程序促进了去除的实用设计.
- 这种方法对于在水生环境中推进无机处理至关重要.
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