在废水处理过程中计算建模的审查
M Salomé Duarte1,2, Gilberto Martins1,2, Pedro Oliveira3
1CEB - Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal.
概括
机器学习模型为废水处理厂 (WWTP) 提供先进的解决方案,改善废水预测,异常检测和能源效率. 结合机械学和机器学习方法的混合模型显示出优化WWTP操作的重大前景.
科学领域:
- 环境工程 环境工程
- 计算科学 计算科学
- 水资源管理 水资源管理
背景情况:
- 污水处理厂 (WWTP) 在能源效率,水质标准和资源回收方面面临着挑战.
- 计算模型,特别是机械模型,用于WWTP预测,但具有模型不确定性和校准需求等局限性.
- 数据驱动模型的兴起为WWTP管理和优化提供了新的机会.
研究的目的:
- 审查废水处理中机器学习 (ML) 模型的实施情况.
- 探索ML在预测WWTP废水特性和废水流入中的应用.
- 讨论用于异常检测,能源消耗优化和混合建模方法的ML.
主要方法:
- 对废水处理中的计算和数据驱动模型现有文献的审查.
- 分析用于预测建模和异常检测的机器学习技术.
- 探索结合机械学和机器学习方法的混合模型.
主要成果:
- 机器学习模型对于预测WWTP性能和优化操作越来越有价值.
- 数据驱动型号在处理废水数据中的复杂关系方面具有优势.
- 结合机械和ML方法的混合模型代表了加强WWTP管理的有希望的方向.
结论:
- 机器学习模型对于提高废水处理效率和预测能力至关重要.
- 未来的研究应该侧重于数据驱动模型的解释性和在WWTP中转移学习.
- 整合机械和机器学习模型为数字双胞胎和实时过程模拟提供了强大的策略.
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