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

  • Authors:

  • B. Ráduly;K. V. Gernaey;A. G. Capodaglio;P. S. Mikkelsen;M. Henze

  • Affiliations:

  • Department of Hydraulic and Environmental Engineering, University of Pavia, Via Ferrata 1, 27100 Pavia, PV, Italy;Department of Industrial Electrical Engineering and Automation (IEA), Lund University, Box 118, SE-221 00 Lund, Sweden and Department of Chemical Engineering, Technical University of Denmark, Buil ...;Department of Hydraulic and Environmental Engineering, University of Pavia, Via Ferrata 1, 27100 Pavia, PV, Italy;Institute of Environment and Resources, Technical University of Denmark, Bygningstorvet, Building 115, DK-2800 Kgs. Lyngby, Denmark;Institute of Environment and Resources, Technical University of Denmark, Bygningstorvet, Building 115, DK-2800 Kgs. Lyngby, Denmark

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2007

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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 (4months of dynamic data). An artificial 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 (20years 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, BOD"5 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^20.95 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.