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Adaptive soft sensing of river flow prediction for wastewater treatment operation and risk management
Water Research ( IF 12.8 ) Pub Date : 2022-06-04 , DOI: 10.1016/j.watres.2022.118714
Jun-Jie Zhu 1 , Nathan Q Sima 2 , Ting Lu 3 , Adrienne Menniti 3 , Peter Schauer 3 , Zhiyong Jason Ren 1
Affiliation  

Many wastewater utilities have discharge permits directly tied with the receiving river flow, so it is critical to have accurate prediction of the hydraulic throughput to ensure safe operation and environment protection. Current empirical knowledge-based operation faces many challenges, so in this study we developed and assessed daily-adaptive, probabilistic soft sensor prediction models to forecast the next month's average receiving river flowrate and guide the utility operations. By comparing 11 machine-learning methods, extra trees regression exhibits desired deterministic prediction accuracy at day 0 (overall accuracy index: 3.9 × 10−3 1/cms2) (cms: cubic meter per second), which also increases steadily over the course of the month (e.g., MAPE and RMSE decrease from 41.46% and 23.31 cms to 3.31% and 2.81 cms, respectively). The overall classification accuracy of three river flow classes reaches 0.79 at the beginning and increases to about 0.97 over the course of the predicted month. To manage the uncertainty caused by potential false negative classification as overestimations, a probabilistic assessment on the predictions based on 95% lower PI is developed and successfully reduces the false negative classification from 17% to nearly zero with a slight sacrifice of overall classification accuracy.



中文翻译:

用于废水处理运营和风险管理的河流流量预测的自适应软传感

许多污水处理设施的排放许可证与接收河流流量直接相关,因此准确预测水流量以确保安全运行和环境保护至关重要。当前基于经验的知识运营面临许多挑战,因此在本研究中,我们开发和评估了每日自适应的概率软传感器预测模型,以预测下个月的平均接收河流流量并指导公用事业运营。通过比较 11 种机器学习方法,额外树回归在第 0 天表现出所需的确定性预测准确度(总体准确度指数:3.9 × 10 -3 1/cms 2)(cms:立方米每秒),在整个月内也稳步增加(例如,MAPE 和 RMSE 分别从 41.46% 和 23.31 cms 下降到 3.31% 和 2.81 cms)。三个河流流量类别的总体分类精度在开始时达到 0.79,并在预测月份的过程中增加到约 0.97。为了将潜在的假阴性分类作为高估而导致的不确定性进行管理,基于低 95% 的 PI 对预测进行概率评估,并成功地将假阴性分类从 17% 降低到几乎为零,同时略微牺牲整体分类准确性。

更新日期:2022-06-08
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