Keywords
activated sludge, anaerobic digestion, bsm2, mathematical modelling, numerical methods
Start Date
1-7-2008 12:00 AM
Abstract
Wastewater treatment plant control and monitoring can help to achieve good effluent quality, in a complex, highly non-linear process. The Benchmark Simulation Model no. 2 (BSM2) is a useful tool to competitively evaluate plant-wide control on a long-term basis. A key component to characterise the system for control is outputparameter sensitivity. This paper brings the results of a global sensitivity analysis performed on the BSM2 model in its open loop version, by means of Monte Carlo (MC) experiments and linear regression. This study presents methods that were applied to make computationally demanding MC experiments on such a complex model feasible, by reducing the computation time for a single simulation and by setting low but sufficient number of runs for the MC experiments; it was found that 50 times the number of uncertain parameters was necessary. The most sensitive parameters turned out to be the design and operation parameters, followed by the wastewater treatment model parameters, while the adopted BSM2 evaluation criteria are rather insensitive to variations in sludge treatment models parameters. The results are verified on a closed loop version of BSM2, and allow future uncertainty analysis studies on BSM2 to be conducted on a smaller set of parameters and to focus the attention on the most critical parameters.
Global sensitivity analysis of biochemical, design and operational parameters of the Benchmark Simulation Model no. 2
Wastewater treatment plant control and monitoring can help to achieve good effluent quality, in a complex, highly non-linear process. The Benchmark Simulation Model no. 2 (BSM2) is a useful tool to competitively evaluate plant-wide control on a long-term basis. A key component to characterise the system for control is outputparameter sensitivity. This paper brings the results of a global sensitivity analysis performed on the BSM2 model in its open loop version, by means of Monte Carlo (MC) experiments and linear regression. This study presents methods that were applied to make computationally demanding MC experiments on such a complex model feasible, by reducing the computation time for a single simulation and by setting low but sufficient number of runs for the MC experiments; it was found that 50 times the number of uncertain parameters was necessary. The most sensitive parameters turned out to be the design and operation parameters, followed by the wastewater treatment model parameters, while the adopted BSM2 evaluation criteria are rather insensitive to variations in sludge treatment models parameters. The results are verified on a closed loop version of BSM2, and allow future uncertainty analysis studies on BSM2 to be conducted on a smaller set of parameters and to focus the attention on the most critical parameters.
