Modeling Nitrogen Dynamics in a Waste Stabilization Pond System Using Flexible Modeling Environment with MCMC

Hussnain Mukhtar, Yu-Pin Lin, Oleg V. Shipin and Joy R. Petway
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Hussnain Mukhtar: Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan
Yu-Pin Lin: Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan
Oleg V. Shipin: Environmental Engineering and Management Program, School of Environment, Resources and Development, Asian Institute of Technology, Pathum Thani 12120, Thailand
Joy R. Petway: Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan

IJERPH, 2017, vol. 14, issue 7, 1-15

Abstract: This study presents an approach for obtaining realization sets of parameters for nitrogen removal in a pilot-scale waste stabilization pond (WSP) system. The proposed approach was designed for optimal parameterization, local sensitivity analysis, and global uncertainty analysis of a dynamic simulation model for the WSP by using the R software package Flexible Modeling Environment (R-FME) with the Markov chain Monte Carlo (MCMC) method. Additionally, generalized likelihood uncertainty estimation (GLUE) was integrated into the FME to evaluate the major parameters that affect the simulation outputs in the study WSP. Comprehensive modeling analysis was used to simulate and assess nine parameters and concentrations of ON-N, NH 3 -N and NO 3 -N. Results indicate that the integrated FME-GLUE-based model, with good Nash–Sutcliffe coefficients (0.53–0.69) and correlation coefficients (0.76–0.83), successfully simulates the concentrations of ON-N, NH 3 -N and NO 3 -N. Moreover, the Arrhenius constant was the only parameter sensitive to model performances of ON-N and NH 3 -N simulations. However, Nitrosomonas growth rate, the denitrification constant, and the maximum growth rate at 20 °C were sensitive to ON-N and NO 3 -N simulation, which was measured using global sensitivity.

Keywords: flexible modeling environment; waste stabilization pond; nitrogen dynamic; parameterization; sensitivity; MCMC; GLUE; global uncertainty (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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