scholarly journals Application of Monte Carlo Methods to Perform Uncertainty and Sensitivity Analysis on Inverse Water-Rock Reactions with NETPATH

2016 ◽  
Author(s):  
David McGraw ◽  
Ronald L. Hershey
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Monte Carlo Methods ◽  
Uncertainty And Sensitivity Analysis

scholarly journals Uncertainty and sensitivity analysis of control strategies using the benchmark simulation model No1 (BSM1)

2009 ◽  
Vol 59 (3) ◽  
pp. 491-499 ◽  
Author(s):  
Xavier Flores-Alsina ◽  
Ignasi Rodriguez-Roda ◽  
Gürkan Sin ◽  
Krist V. Gernaey
Keyword(s):  
Growth Rate ◽  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Carbon Source ◽  
Simulation Model ◽  
Correction Factor ◽  
Control Strategies ◽  
Hydrolysis Rate ◽  
External Carbon Source ◽  
Uncertainty And Sensitivity Analysis

The objective of this paper is to perform an uncertainty and sensitivity analysis of the predictions of the Benchmark Simulation Model (BSM) No. 1, when comparing four activated sludge control strategies. The Monte Carlo simulation technique is used to evaluate the uncertainty in the BSM1 predictions, considering the ASM1 bio-kinetic parameters and influent fractions as input uncertainties while the Effluent Quality Index (EQI) and the Operating Cost Index (OCI) are focused on as model outputs. The resulting Monte Carlo simulations are presented using descriptive statistics indicating the degree of uncertainty in the predicted EQI and OCI. Next, the Standard Regression Coefficients (SRC) method is used for sensitivity analysis to identify which input parameters influence the uncertainty in the EQI predictions the most. The results show that control strategies including an ammonium (SNH) controller reduce uncertainty in both overall pollution removal and effluent total Kjeldahl nitrogen. Also, control strategies with an external carbon source reduce the effluent nitrate (SNO) uncertainty increasing both their economical cost and variability as a trade-off. Finally, the maximum specific autotrophic growth rate (μA) causes most of the variance in the effluent for all the evaluated control strategies. The influence of denitrification related parameters, e.g. ηg (anoxic growth rate correction factor) and ηh (anoxic hydrolysis rate correction factor), becomes less important when a SNO controller manipulating an external carbon source addition is implemented.

ANALYSIS AND QUANTIFICATION OF DATA ASSIMILATION BASED ON SEQUENTIAL MONTE CARLO METHODS FOR WILDFIRE SPREAD SIMULATION

2010 ◽  
Vol 01 (04) ◽  
pp. 445-468 ◽  
Author(s):  
FENG GU ◽  
XIAOLIN HU
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Data Assimilation ◽  
Monte Carlo Methods ◽  
Sequential Monte Carlo ◽  
Sensor Data ◽  
Sequential Monte Carlo Methods ◽  
Systematic Analysis ◽  
Wildfire Spread ◽  
Simulation Results

Data assimilation is an important technique to improve simulation results by assimilating real time sensor data into a simulation model. A data assimilation framework based on Sequential Monte Carlo (SMC) methods for wildfire spread simulation has been developed in previous work. This paper provides systematic analysis and measurement to quantify the effectiveness and robustness of the developed data assimilation method. Measurement metrics are used to evaluate the robustness of SMC methods in data assimilation for wildfire spread simulation. Sensitivity analysis is carried out to examine the influences of important parameters to the data assimilation results. This work of analysis and quantification provides information to assess the effectiveness of the data assimilation method and suggests guidelines to further improve the data assimilation method for wildfire spread simulation.

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