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Artificial neural networks for rapid WWTP performance evaluation: Methodology and case study

Raduly, B; Gernaey, Krist LU ; Capodaglio, A. G.; Mikkelsen, P. S. and Henze, M. (2007) In Environmental Modelling & Software 22(8). p.1208-1216
Abstract
Reliable performance evaluation of wastewater treatment plants (WWTPs) can be done by simulating the plant behavior over a wide range of influent disturbances, including series of rain events with different intensity and duration, seasonal temperature variations, holiday effects, etc. Such simulation-based WWTP performance evaluations are in practice limited by the long simulation time of the mechanistic WWTP models. By moderate simplification (avoiding big losses in prediction accuracy) of the mechanistic WWTP model only a limited reduction of the simulation time can be achieved. The approach proposed in this paper combines an influent disturbance generator with a mechanistic WWTP model for generating a limited sequence of training data... (More)
Reliable performance evaluation of wastewater treatment plants (WWTPs) can be done by simulating the plant behavior over a wide range of influent disturbances, including series of rain events with different intensity and duration, seasonal temperature variations, holiday effects, etc. Such simulation-based WWTP performance evaluations are in practice limited by the long simulation time of the mechanistic WWTP models. By moderate simplification (avoiding big losses in prediction accuracy) of the mechanistic WWTP model only a limited reduction of the simulation time can be achieved. The approach proposed in this paper combines an influent disturbance generator with a mechanistic WWTP model for generating a limited sequence of training data (4 months of dynamic data). An antificial neural network (ANN) is then trained on the available WWTP input-output data, and is subsequently used to simulate the remainder of the influent time series (20 years of dynamic data) generated with the influent disturbance generator. It is demonstrated that the ANN reduces simulation time by a factor of 36, even when including the time needed for the generation of training data and for ANN training. For repeated integrated urban wastewater system simulations that do not require repeated training of the ANN, the ANN reduces simulation time by a factor of 1300 compared to the mechanistic model. ANN prediction of effluent ammonium, BOD5 and total suspended solids was good when compared to mechanistic WWTP model predictions, whereas prediction of effluent COD and total nitrogen concentrations was a bit less satisfactory. With correlation coefficients R-2 > 0.95 and prediction errors lower than 10%, the accuracy of the ANN is sufficient for applications in simulation-based WWTP design and simulation of integrated urban wastewater systems, especially when taking into account the uncertainties related to mechanistic WWTP modeling. (c) 2006 Elsevier Ltd. All rights reserved. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
design, plant, performance evaluation, artificial neural networks, modeling, simulation speed, time series, wastewater treatment plant
in
Environmental Modelling & Software
volume
22
issue
8
pages
1208 - 1216
publisher
Elsevier
external identifiers
  • wos:000246308900013
  • scopus:33947712235
ISSN
1364-8152
DOI
10.1016/j.envsoft.2006.07.003
language
English
LU publication?
yes
id
ea906661-0e72-40e4-bdc0-37ef8c1e8d37 (old id 662718)
date added to LUP
2007-12-05 14:48:31
date last changed
2017-08-27 03:56:29
@article{ea906661-0e72-40e4-bdc0-37ef8c1e8d37,
  abstract     = {Reliable performance evaluation of wastewater treatment plants (WWTPs) can be done by simulating the plant behavior over a wide range of influent disturbances, including series of rain events with different intensity and duration, seasonal temperature variations, holiday effects, etc. Such simulation-based WWTP performance evaluations are in practice limited by the long simulation time of the mechanistic WWTP models. By moderate simplification (avoiding big losses in prediction accuracy) of the mechanistic WWTP model only a limited reduction of the simulation time can be achieved. The approach proposed in this paper combines an influent disturbance generator with a mechanistic WWTP model for generating a limited sequence of training data (4 months of dynamic data). An antificial neural network (ANN) is then trained on the available WWTP input-output data, and is subsequently used to simulate the remainder of the influent time series (20 years of dynamic data) generated with the influent disturbance generator. It is demonstrated that the ANN reduces simulation time by a factor of 36, even when including the time needed for the generation of training data and for ANN training. For repeated integrated urban wastewater system simulations that do not require repeated training of the ANN, the ANN reduces simulation time by a factor of 1300 compared to the mechanistic model. ANN prediction of effluent ammonium, BOD5 and total suspended solids was good when compared to mechanistic WWTP model predictions, whereas prediction of effluent COD and total nitrogen concentrations was a bit less satisfactory. With correlation coefficients R-2 > 0.95 and prediction errors lower than 10%, the accuracy of the ANN is sufficient for applications in simulation-based WWTP design and simulation of integrated urban wastewater systems, especially when taking into account the uncertainties related to mechanistic WWTP modeling. (c) 2006 Elsevier Ltd. All rights reserved.},
  author       = {Raduly, B and Gernaey, Krist and Capodaglio, A. G. and Mikkelsen, P. S. and Henze, M.},
  issn         = {1364-8152},
  keyword      = {design,plant,performance evaluation,artificial neural networks,modeling,simulation speed,time series,wastewater treatment plant},
  language     = {eng},
  number       = {8},
  pages        = {1208--1216},
  publisher    = {Elsevier},
  series       = {Environmental Modelling & Software},
  title        = {Artificial neural networks for rapid WWTP performance evaluation: Methodology and case study},
  url          = {http://dx.doi.org/10.1016/j.envsoft.2006.07.003},
  volume       = {22},
  year         = {2007},
}