Advanced

Generation of synthetic influent data to perform (micro)pollutant wastewater treatment modelling studies

Snip, L. J P; Flores-Alsina, X.; Aymerich, I; Rodríguez-Mozaz, S.; Barceló, D.; Plósz, B. G.; Corominas, Ll; Rodriguez-Roda, I.; Jeppsson, U. LU and Gernaey, K. V. LU (2016) In Science of the Total Environment 569-570. p.278-290
Abstract

The use of process models to simulate the fate of micropollutants in wastewater treatment plants is constantly growing. However, due to the high workload and cost of measuring campaigns, many simulation studies lack sufficiently long time series representing realistic wastewater influent dynamics. In this paper, the feasibility of the Benchmark Simulation Model No. 2 (BSM2) influent generator is tested to create realistic dynamic influent (micro)pollutant disturbance scenarios. The presented set of models is adjusted to describe the occurrence of three pharmaceutical compounds and one of each of its metabolites with samples taken every 2–4 h: the anti-inflammatory drug ibuprofen (IBU), the antibiotic sulfamethoxazole (SMX) and the... (More)

The use of process models to simulate the fate of micropollutants in wastewater treatment plants is constantly growing. However, due to the high workload and cost of measuring campaigns, many simulation studies lack sufficiently long time series representing realistic wastewater influent dynamics. In this paper, the feasibility of the Benchmark Simulation Model No. 2 (BSM2) influent generator is tested to create realistic dynamic influent (micro)pollutant disturbance scenarios. The presented set of models is adjusted to describe the occurrence of three pharmaceutical compounds and one of each of its metabolites with samples taken every 2–4 h: the anti-inflammatory drug ibuprofen (IBU), the antibiotic sulfamethoxazole (SMX) and the psychoactive carbamazepine (CMZ). Information about type of excretion and total consumption rates forms the basis for creating the data-defined profiles used to generate the dynamic time series. In addition, the traditional influent characteristics such as flow rate, ammonium, particulate chemical oxygen demand and temperature are also modelled using the same framework with high frequency data. The calibration is performed semi-automatically with two different methods depending on data availability. The ‘traditional’ variables are calibrated with the Bootstrap method while the pharmaceutical loads are estimated with a least squares approach. The simulation results demonstrate that the BSM2 influent generator can describe the dynamics of both traditional variables and pharmaceuticals. Lastly, the study is complemented with: 1) the generation of longer time series for IBU following the same catchment principles; 2) the study of the impact of in-sewer SMX biotransformation when estimating the average daily load; and, 3) a critical discussion of the results, and the future opportunities of the presented approach balancing model structure/calibration procedure complexity versus predictive capabilities.

(Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
BSM2 influent generator, Calibration, Micropollutant occurrence, Trace chemicals, Xenobiotics
in
Science of the Total Environment
volume
569-570
pages
13 pages
publisher
Elsevier
external identifiers
  • Scopus:84975810869
ISSN
0048-9697
DOI
10.1016/j.scitotenv.2016.05.012
language
English
LU publication?
yes
id
531a5d20-5e7e-44fb-bdc2-59dc426aa98b
date added to LUP
2016-10-13 13:49:02
date last changed
2016-10-14 03:00:03
@misc{531a5d20-5e7e-44fb-bdc2-59dc426aa98b,
  abstract     = {<p>The use of process models to simulate the fate of micropollutants in wastewater treatment plants is constantly growing. However, due to the high workload and cost of measuring campaigns, many simulation studies lack sufficiently long time series representing realistic wastewater influent dynamics. In this paper, the feasibility of the Benchmark Simulation Model No. 2 (BSM2) influent generator is tested to create realistic dynamic influent (micro)pollutant disturbance scenarios. The presented set of models is adjusted to describe the occurrence of three pharmaceutical compounds and one of each of its metabolites with samples taken every 2–4 h: the anti-inflammatory drug ibuprofen (IBU), the antibiotic sulfamethoxazole (SMX) and the psychoactive carbamazepine (CMZ). Information about type of excretion and total consumption rates forms the basis for creating the data-defined profiles used to generate the dynamic time series. In addition, the traditional influent characteristics such as flow rate, ammonium, particulate chemical oxygen demand and temperature are also modelled using the same framework with high frequency data. The calibration is performed semi-automatically with two different methods depending on data availability. The ‘traditional’ variables are calibrated with the Bootstrap method while the pharmaceutical loads are estimated with a least squares approach. The simulation results demonstrate that the BSM2 influent generator can describe the dynamics of both traditional variables and pharmaceuticals. Lastly, the study is complemented with: 1) the generation of longer time series for IBU following the same catchment principles; 2) the study of the impact of in-sewer SMX biotransformation when estimating the average daily load; and, 3) a critical discussion of the results, and the future opportunities of the presented approach balancing model structure/calibration procedure complexity versus predictive capabilities.</p>},
  author       = {Snip, L. J P and Flores-Alsina, X. and Aymerich, I and Rodríguez-Mozaz, S. and Barceló, D. and Plósz, B. G. and Corominas, Ll and Rodriguez-Roda, I. and Jeppsson, U. and Gernaey, K. V.},
  issn         = {0048-9697},
  keyword      = {BSM2 influent generator,Calibration,Micropollutant occurrence,Trace chemicals,Xenobiotics},
  language     = {eng},
  month        = {11},
  pages        = {278--290},
  publisher    = {ARRAY(0x9d8c1f0)},
  series       = {Science of the Total Environment},
  title        = {Generation of synthetic influent data to perform (micro)pollutant wastewater treatment modelling studies},
  url          = {http://dx.doi.org/10.1016/j.scitotenv.2016.05.012},
  volume       = {569-570},
  year         = {2016},
}