Politecnico di Milano, Department of Civil and Environmental Engineering (DICA), Piazza Leonardo da Vinci 32, 20133, Milano, Italy.
Water Res. 2021 Mar 1;191:116806. doi: 10.1016/j.watres.2021.116806. Epub 2021 Jan 4.
Real-time acquisition of indicator bacteria concentration at the inlet of disinfection unit is a fundamental support to the control of chemical and ultraviolet wastewater disinfection. Culture-based enumeration methods need time-consuming laboratory analyses, which give results after several hours or days, while newest biosensors rarely provide information about specific strains and outputs are not directly comparable with regulatory limits as a consequence of measurement principles. In this work, a novel soft sensor approach for virtual real-time monitoring of E. coli concentration is proposed. Conventional wastewater physical and chemical indicators (chemical oxygen demand, total nitrogen, nitrate, ammonia, total suspended solids, conductivity, pH, turbidity and absorbance at 254 nm) and flowrate were studied as potential predictors of E. coli concentration relying on data collected from three full-scale wastewater treatment plants. Different methods were compared: (i) linear modeling via ordinary least squares; (ii) ridge regression; (iii) principal component regression and partial least squares; (iv) non-linear modeling through artificial neural networks. Linear soft sensors reached some degree of accuracy, but performances of the artificial neural network based models were by far superior. Sensitivity analysis allowed to prioritize the importance of each predictor and to highlight the site-specific nature of the approach, because of the site-specific nature of relationships between predictors and E. coli concentration. In one case study, pH and conductivity worked as good proxy variables when the occurrence of intense rain events caused sharp increases in E. coli concentration. Differently, in other case studies, chemical oxygen demand, total suspended solids, turbidity and absorbance at 254 nm accounted for the positive correlation between low wastewater quality and E. coli concentration. Moreover, sensitivity analysis of artificial neural network models highlighted the importance of interactions among predictors, contributing to 25 to 30% of the model output variance. This evidence, along with performance results, supported the idea that nonlinear families of models should be preferred in the estimation of E. coli concentration. The artificial neural network based soft sensor deployment for control of peracetic acid disinfectant dosage was simulated over a realistic scenario of wastewater quality recorded by on-line sensors over 2 months. The scenario simulations highlighted the significant benefit of an E. coli soft sensor, which provided up to 57% of disinfectant saving.
实时获取消毒单元入口处指示菌浓度是控制化学和紫外线废水消毒的基本支撑。基于培养的计数方法需要耗时的实验室分析，结果需要数小时甚至数天才能得出，而最新的生物传感器很少提供有关特定菌株的信息，并且由于测量原理的原因，输出结果与法规限制无法直接比较。在这项工作中，提出了一种用于虚拟实时监测大肠杆菌浓度的新型软传感器方法。基于从三个全规模污水处理厂收集的数据，研究了常规废水理化指标（化学需氧量、总氮、硝酸盐、氨、总悬浮固体、电导率、pH 值、浊度和 254nm 处的吸光度）和流量，作为大肠杆菌浓度的潜在预测因子。比较了不同的方法：（i）通过普通最小二乘法进行线性建模；（ii）岭回归；（iii）主成分回归和偏最小二乘法；（iv）通过人工神经网络进行非线性建模。线性软传感器达到了一定的准确性，但基于人工神经网络的模型的性能要好得多。敏感性分析允许对每个预测因子的重要性进行优先级排序，并突出方法的特定于站点的性质，因为预测因子与大肠杆菌浓度之间的关系具有特定于站点的性质。在一个案例研究中，当强烈的降雨事件导致大肠杆菌浓度急剧增加时，pH 值和电导率可以作为很好的代理变量。在其他案例研究中，化学需氧量、总悬浮固体、浊度和 254nm 处的吸光度则解释了低废水质量与大肠杆菌浓度之间的正相关关系。此外，人工神经网络模型的敏感性分析突出了预测因子之间相互作用的重要性，这些相互作用占模型输出方差的 25%至 30%。这些证据以及性能结果支持了这样一种观点，即应该优先选择非线性模型族来估计大肠杆菌浓度。基于人工神经网络的软传感器在通过在线传感器记录的两个月废水质量的实际场景中，针对过乙酸消毒剂剂量的控制进行了部署模拟。场景模拟突出了大肠杆菌软传感器的显著优势，它可以节省高达 57%的消毒剂。
