一个智能预测和优化的废水处理厂
Anandrao R Deshmukh1, Rajkuwar Dubal2, Minaxi R Sharma2
1Department of Civil Engineering, JSPM's Rajarshi Shahu College of Engineering, Pune, 411033, India. anand21desh@gmail.com.
概括
本研究介绍了一种使用基于黑猩猩的GoogleLeNet (CbG) 的智能废水处理方法,以优化运营. 这种先进的模型提高了效率,降低了成本,并改善了废水管理中的环境结果.
科学领域:
- 环境工程环境工程
- 计算智能是一种计算智能.
- 水资源管理 水资源管理
背景情况:
- 传统的废水处理厂在效率和可持续性方面面临着挑战.
- 数据驱动的预测建模和自动化提供了潜在的解决方案.
- 优化操作参数对于有效的废水管理至关重要.
研究的目的:
- 开发一种智能,可预测和优化的废水处理厂方法.
- 通过一种新的计算框架,提高废水处理操作的效率和可持续性.
- 为了降低运营成本和环境影响.
主要方法:
- 整合基于黑猩猩的GoogLeNet (CbG),这是一个混合模型,将深层卷积架构 (GoogLeNet) 与生物灵感优化算法 (黑猩猩优化) 结合起来.
- 数据收集,预处理,特征分析和性能预测.
- 优化操作参数,包括pH值,剂量速率,废水质量和能源消耗.
主要成果:
- 在CbG模型中,RMSE,MAE,MAPE和R2分数分别为1.103,0.233,0.012和0.002,实现了高精度.
- 显示电力使用量减少了大约1.4%.
- 与现有技术相比,实现了0.12%的温室气体排放显著减少.
结论:
- 智能预测和优化废水处理厂方法在运营效率和可持续性方面提供了显著的改进.
- CbG模型为优化废水处理提供了一个强大的框架,从而节省成本和环境效益.
- 这种方法代表了可持续废水管理实践的重大进步.
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