Parameter sensitivity analysis

Model parameters are usually determined from experimental data. In doing this, sensitivity analysis is valuable in identifying the best experimental conditions for the estimation of a particular model parameter. Sensitivity analysis is easy effected with MADONNA, and sensitivity analysis is also provided in other more advanced software packages, such as ACSL-OPTIMIZE. 

Figure 10.4 Parameter sensitivity analysis performed around the 700mg dose. RadPart particle radius RefSol reference solubility.

B. KanyAr, Parameter sensitivity analysis for designing of experiments in kinetics, Acta Biochim. Biophys. Acad. Sci. Hung. 12 (1978) 24-28. 

Many methods have been developed for model analysis for instance, bifurcation and stability analysis [88, 89], parameter sensitivity analysis [90], metabolic control analysis [16, 17, 91] and biochemical systems analysis [18]. One highly important method for model analysis and especially for large models, such as many silicon cell models, is model reduction. Model reduction has a long history in the analysis of biochemical reaction networks and in the analysis of nonlinear dynamics (slow and fast manifolds) [92-104]. In all cases, the aim of model reduction is to derive a simplified model from a larger ancestral model that satisfies a number of criteria. In the following sections we describe a relatively new form of model reduction for biochemical reaction networks, such as metabolic, signaling, or genetic networks. 

P9-2a Review the example problems in this chapter, choose one, and use a software package such as POLYMATH or MATLAB to carry out a parameter sensitivity analysis. 

A parameter sensitivity analysis is required for this problem in which an isomerization is earned out over a 20-mesh gauze screen. [2nd Ed. PlO-12]... 

Drews, T.O., Braatz, R.D. and Alkire, R.C. (2003) Parameter Sensitivity Analysis of Monte Carlo Simulations of Copper Electrodeposition with Multiple Additives./. Electrochem. Soc., 150, C807-C812. 

Itraconazole, a widely used broad-spectrum anti-fungal agent has been used as an example (De Beule and Van Gestel, 2001 Peeters et al., 2002). As indicated in Figure 3, the compound is predicted to have extremely low water solubility but useful intestinal permeability. The parameter sensitivity analysis indicated that fraction absorbed could be substantially increased if the apparent solubility of the drug could be increased to 100 pg/mL or greater. Such manipulation is possible through the use of hydrophilic cyclodextrins such as 2-hydroxypropyl-... 

See R. M. Felder. Chem. Eng. Educ.. /d (4 k 176 (1085). The August 1985 issue of Chemicai Engineering Progress may be useful for pan Ick P9 2, Review the example problems in this chapter, choose one, and u,se a software package such as Polymath or MATLAB to carry out a parameter sensitivity analysis. 

A.K. Mahapatra and L. hme. Parameter sensitivity analysis of a directly irradiated solar dryer with integrated collector, in Proceedings of ISES Solar World Congress, Harare, Zimbabwe, Maha 0044,1995. 

Hamby, D.M. 1994. A Review of Techniques for Parameter Sensitivity Analysis of Environmental Models. Environmental Monitoring and Assessment 32(2) 135-154. 

The model is valid only within certain boundaries, which correspond to the models purposes. No model can completely replace experimentation. Models can, however, contribute to saving some of the time and money necessary for experiments. Parameter sensitivity analysis is obligatory. 

Chapter 4 shows how incremental bond graphs enable a matrix-based determination of parameter sensitivities of transfer functions for direct as well as for inverse linear models. The necessary matrices can be generated from a bond graph and its incremental bond graph by means of existing software. Furthermore, incremental bond graphs also support a parameter sensitivity analysis of ARR residuals. 

Parameter sensitivity analysis of dampers is adequate because it can reveal the correlation between dampers and structural responses, therefore is conducive to the damper optimization. Various damping coefficients C are employed to conduct the parameter sensitivity study of dampers. The value of C ranges from 1 x 10 to 20x 10 kN-s/m, and a is 1.0, according to the linear damper. 

Dampers with appropriate parameters can reduce the seismic displacement of super-long-span suspension bridges significantly. The parameter sensitive analysis involved in influence of damping coefficient C on the structural responses will necessarily facilitate the damper optimization. 

Models can also be used for parameter sensitivity analysis. Due to the complexity of reaction networks and hydrodynamics, the effects of various factors on the reactor performance are complex. Model analysis provides a guiding tool for process development. The effects of PCa on the yield of acrylonitrile are shown in Fig. 30. As shown, when Pe is less than 0.05, the yield of acrylonitrile changes marginally with Pea, and the reactor can be considered well mixed. When Pea is greater than 10, the yield of acrylonitrile is almost the same as that in a plug-flow reactor. Model simulation also reveal the existence of a Pea-sensitive range... 

DYNAMITE will be extended to perform parameter-sensitivity analysis (with analytically generated sensitivity matrices) and to handle small and large elastic deformations. Other possible extensions are numerical methods for statically indetermined systems, adequate methods for stiff systems. 

One useful way to determine the mutual connection between these quantities and properties and in what maimer they influence the sulfur capture process, is by the application of a mathematical sulfur retention model. Based on a parameter sensitivity analysis it is generally possible to focuss in on the main parameters that influence the sulfation process. In that way the model is used as a practical engineering tool for the analysis of combustors in design or operation (diagnostic or screening tool). However, the model may also be used as a predictive tool for the calculation of the required process operating conditions in an existing FBC facility. 

Sensitivity analyses are greatly facilitated by process simulation tools as well. The objective of such studies is to evaluate the impact of critical parameters on various key performance indicators (KPIs), such as production cost, cycle times, and plant throughput. If there is uncertainty for certain input parameters, sensitivity analysis can be supplemented with Monte Carlo simulation to quantify the impact of uncertainty. 

Jemei et al. (2005) reported a Dynamic Recurrent Neural Network (DRNN) model of a PEMFC for a 500 W fuel cell. The proposed black box model can easily be extrapolated to more powerful fuel cell systems. For black-box models, simulation results are strongly dependant on the choice of input parameters. Thus, a sensitivity analysis is performed to assess the influences or relative importance of each input parameter on the output variable. Many different ways to perform sensitivity analysis are possible. A Multi Parameter Sensitivity Analysis (MPSA) is proposed to evaluate the relative importance of each input parameter independently on the fuel cell voltage. 

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