通过通过节点级囊图神经网络预测废水质量来优化造纸废水处理
G Baskar1, A N Parameswaran2, R Sathyanathan3
1Head of the Department, Civil Engineering, Adhiyamaan College of Engineering, Hosur, 635130, India. hod_civil@adhiyamaan.ac.in.
Environmental monitoring and assessment
|January 17, 2025
概括
一种新方法通过使用节点级囊图神经网络预测化学氧需求 (COD) 来优化造纸废水处理. 这种方法提高了工业废水管理的监测准确性和效率.
科学领域:
- 环境科学 环境科学
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
背景情况:
- 造纸废水需要精确的实时监测废水指数,特别是化学氧气需求 (COD),由于复杂,非线性和时间变化的处理过程.
- 传统的COD预测模型经常表现出参数灵敏度和缺乏可解释性,需要在工业废水处理监测方面取得进展.
研究的目的:
- 为预测废水质量提出一个优化的造纸废水处理方法.
- 为了提高预测关键废水化学氧气需求 (COD) 指数的准确性.
主要方法:
- 开发一个节点级囊图神经网络 (NLCGNN) 模型,用于预测造纸废水废水质量 (PWWT-PEQ-NLCGNN).
- 使用隐士优化 (HCO) 算法优化NLCGNN重量参数,以提高COD预测准确度.
主要成果:
- 拟议的PWWT-PEQ-NLCGNN技术在现有方法上显示出了显著的改进.
- 与基准模型相比,实现了更高的准确度 (30.53%,23.34%,32.64%),精度 (20.53%,25.34%,29.64%) 和灵敏度 (20.53%,25.34%,29.64%).
- 超越性能的模型包括WQP-GPR-DL-CLPWTS,POEQ-PWTP-DKBELM,以及QRM-PWTP-DMPLS. 这些模型的性能都很好.
结论:
- PWWT-PEQ-NLCGNN方法提供了一个有希望的解决方案,用于及时和准确地监测造纸废水处理过程.
- 优化的NLCGNN与HCO算法有效地提高了对COD等关键废水质量指标的预测.
- 该研究强调了先进机器学习技术在改善工业废水管理和环境保护方面的潜力.
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