通过基于机器学习架构的纳米颗粒组成盐水组合来优化水的再利用和建模
Koppula Srinivas Rao1, Vineet Tirth2, Hamad Almujibah3
1Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, India.
概括
这项研究引入了一种机器学习方法,通过分析盐水中的纳米粒子来优化水的再利用. 该方法提高了盐水处理效率,提高了水处理厂的性能.
科学领域:
- 环境科学 环境科学
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
背景情况:
- 淡水短缺需要先进的解决方案来处理盐水.
- 现有的海水处理设施在可靠性和有效性方面存在局限性.
- 深度学习为改善淡化过程中的盐颗粒分析提供了潜力.
研究的目的:
- 提出一种基于机器学习的新技术,以优化水的再利用.
- 通过纳米粒子分析和太阳能电池提高盐水处理效率.
- 用渐变区分器随机场分析盐酸成分.
主要方法:
- 实现机器学习架构用于盐水中的纳米粒子分析.
- 利用纳米粒子太阳能电池来有效处理盐水.
- 采用梯度分辨器随机场用于盐水组成分析.
- 使用特异性,准确性和TEM图像数据集的平均精度等指标来评估性能.
主要成果:
- 与现有的人工神经网络 (ANN) 方法相比,拟议的方法显示出更好的性能.
- 亮场TEM (BF-TEM) 数据集实现了75%的特异性,44%的卡帕系数,81%的训练精度和61%的平均精度.
- 年度暗场扫描TEM (ADF-STEM) 数据集产生了更高的结果:79%的特异性,49%的卡帕系数,85%的训练精度和66%的平均精度.
结论:
- 开发的机器学习技术通过对盐水处理的增强纳米粒子分析,有效地优化了水的再利用.
- 该方法显示了提高水处理厂效率和准确性的巨大潜力.
- 进一步的研究可以探索这种方法在水资源管理中的更广泛应用.
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