Back to Search
Start Over
Soft sensor predictor of E. coli concentration based on conventional monitoring parameters for wastewater disinfection control
- Source :
- Water research. 191
- Publication Year :
- 2020
-
Abstract
- 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.
- Subjects :
- Artificial neural network
Environmental Engineering
0208 environmental biotechnology
Portable water purification
02 engineering and technology
010501 environmental sciences
Wastewater
01 natural sciences
Water Purification
Partial least squares regression
Escherichia coli
Humans
Turbidity
Waste Management and Disposal
0105 earth and related environmental sciences
Water Science and Technology
Civil and Structural Engineering
Total suspended solids
Biological Oxygen Demand Analysis
Ecological Modeling
Chemical oxygen demand
E. coli
Soft sensor
Pollution
Peracetic acid
020801 environmental engineering
Disinfection
Environmental science
Principal component regression
Biological system
Subjects
Details
- ISSN :
- 18792448
- Volume :
- 191
- Database :
- OpenAIRE
- Journal :
- Water research
- Accession number :
- edsair.doi.dedup.....bb935c9e0f939db0911b4f5f3c88ea5f