Bioreactor Simulation with Quadratic Neural Network Model Approximations and Cost Optimization with Markov Decision Process

  • Tamás Koncsos
    Affiliation

    Department of Sanitary and Environmental Engineering, Faculty of Civil Engineering, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rkp. 3., Hungary

Abstract

The efficient operation of activated sludge type wastewater treatment plants is an ongoing topic for the utility providers, where electric energy consumption shares are high, giving cca. 30 % of total operational costs. Intervention methods for intensification include fine tuning of aeration settings, sludge removal and the adjustment of recirculation rates. In order to analyze the effects of various process control strategies, activated sludge models (ASM) are used for the purpose of biokinetic modeling. In practice, most model simulators do not incorporate optimization and necessary auto-calibration of the latter, due to high computational demand of timeseries evaluation. In this paper, a new mathematical model is presented, which makes biokinetic simulations suitable for the use in decision support systems. Namely, the ASM model is approximated with a computanional inexpesive quadratic model solution, fed into a set of mass-balance corrected neural networks. Cost optimization is achieved with Markov decision process model. The developed method was illustrated for a case of Hungarian, large wastewater treatment plant. It was proven, the model is able to find better aeration schemes for the plant in aspect of cost of operation and nitrogen removal efficiency. The model can be used to find cost-optimal policies under arbitrary defined conditions. As a benefit, results can be implemented into industrial logic controllers.

Keywords: wastewater treatment, neural network model, Markov decision process, cost optimization
Published online
2020-05-06
How to Cite
Koncsos, T. “Bioreactor Simulation with Quadratic Neural Network Model Approximations and Cost Optimization with Markov Decision Process”, Periodica Polytechnica Civil Engineering, 64(2), pp. 614-622, 2020. https://doi.org/10.3311/PPci.14734
Section
Research Article