Use Of Global Sensitivity Analysis Research Articles

The Use of Global Sensitivity Analysis to Assess the Oral Absorption of Weakly Basic Compounds: A Case Example of Dipyridamole.

To utilize the global system analysis (GSA) in oral absorption modeling to gain a deeper understanding of system behavior, improve model accuracy, and make informed decisions during drug development. GSA was utilized to give insight into which drug substance (DS), drug product (DP), and/or physiological parameter would have an impact on peak plasma concentration (Cmax) and area under the curve (AUC) of dipyridamole as a model weakly basic compound. GSA guided the design of in vitro experiments and oral absorption risk assessment using FormulatedProducts v2202.1.0. The solubility and precipitation profiles of dipyridamole in different bile salt concentrations were measured. The results were then used to build a mechanistic oral absorption model. GSA warranted further investigation into the precipitation kinetics and its link to the levels of bile salt concentrations. Mechanistic modeling studies demonstrated that a precipitation-integrated modeling approach appropriately predicted the mean plasma profiles, Cmax, and AUC from the clinical studies. This work shows the value of GSA utilization in early development to guide in vitro experimentation and build more confidence in identifying the critical parameters for the mathematical models.

NMPC in active subspaces: Dimensionality reduction with recursive feasibility guarantees

Dimensionality reduction of decision variables is a practical and classic method to reduce the computational burden in linear and Nonlinear Model Predictive Control (NMPC). Available results range from early move-blocking ideas to singular-value decomposition. For schemes more complex than move-blocking it is seemingly not straightforward to guarantee recursive feasibility of the receding-horizon optimization. Decomposing the space of decision variables related to the inputs into active and inactive complements, this paper proposes a general framework for effective feasibility-preserving dimensionality reduction in NMPC. We show how – independently of the actual choice of the subspaces – recursive feasibility can be established. Moreover, we propose the use of global sensitivity analysis to construct the active subspace in data-driven fashion based on user-defined criteria. Numerical examples illustrate the efficacy of the proposed scheme. Specifically, for a chemical reactor we obtain a significant reduction by factor 20−40 at a closed-loop performance decay of less than 0.05%.

Sensitivity Analysis of the Transfer Impedance of Shielded Cables With Braided Shields Using a Parameterized FEM Model

A parameterized finite-element model of braided cable shields is developed to investigate the analytical model and study the influence of parameter and material dependencies of the transfer impedance of braided cable shields. The parameter influences and their interrelations are analyzed by the use of global sensitivity analysis. Measurements are only needed for the validation of the finite-element model but not for parameter variations. This reduces time and costs and eliminates measurement uncertainties occurring at different measurement procedures.

Parametric sensitivity analysis of SEL-421 distance relay algorithms used in compensated line

This paper presents the use of Global Sensitivity Analysis (GSA) methods to analyze the sensitivity level of the performance of the impedance measurement algorithm of SEL-421 distance relay protection when used to perform transmission line protection with compensation circuit. This GSA technique was developed to see the dominance effect of system uncertainty (or factors). Two techniques that might be used for sensitivity analysis using GSA are Quasi-Monte Carlo (QMC) and data sampling with Sobol technique. Experimental is done with DIgSILENT software to do modeling of the power system, simulation, and calculation of impedance and algorithm error. The testing is carried out automatically with the help of the developed algorithm using DPL (DIgSILENT Programming Language). SIMLAB software, in this case, (with Sobol technique) is applied to generate some non-linear factor data and analyze the output of variations in impedance measurement errors.

Multi-temporal assessment of power system flexibility requirement

In power systems, flexibility can be defined as the ability to adapt to variability and uncertainty in demand and generation. Various ongoing changes in the power system are impacting the need for flexibility. We propose a novel methodology to (i) evaluate annual, weekly and daily flexibility requirements through a set of frequency spectrum analysis based metrics, (ii) examine the sensitivity of these flexibility requirements to five variables: the degree of network interconnection and the penetration of wind power, solar power, electric heating and cooling. The proposed methodology is validated on a case study focusing on the French power system, while accounting for its electrically connected neighbours. We provide an estimation of how flexibility requirements are likely to evolve in years to come; the use of global sensitivity analysis allows the identification of the variables responsible for these evolutions. The presented methodology and results can be used to identify future challenges, to evaluate the market potential of flexibility solutions and to assess the implications of policy decisions.

Optimizing the configuration of integrated nutrient and energy recovery treatment trains: A new application of global sensitivity analysis to the generic nutrient recovery model (NRM) library

This paper describes the use of global sensitivity analysis (GSA) for factor prioritization in nutrient recovery model (NRM) applications. The aim was to select the most important factors influencing important NRM model outputs such as biogas production, digestate composition and pH, ammonium sulfate recovery, struvite production, product purity, particle size and density, air and chemical requirements, scaling potential, among others. Factors considered for GSA involve: 1) input waste stream characteristics, 2) process operational factors, and 3) kinetic parameters incorporated in the NRMs. Linear regression analyses on Monte Carlo simulation outputs were performed, and the impact of the standardized regression coefficients on major performance indicators was evaluated. Finally, based on the results, the paper describes the original use of GSA to obtain insight in complex nutrient recovery systems and to propose an optimal nutrient and energy recovery treatment train configuration that maximizes resource recovery and minimizes energy and chemical requirements.

Analysis of a multi-axial quantum-informed ferroelectric continuum model: Part 2—sensitivity analysis

We illustrate the use of global sensitivity analysis, and a parameter subset selection algorithm based on local sensitivity analysis, to quantify the relative influence of parameters in polarization and electrostrictive energy relations for a quantum-informed, single-domain, ferroelectric material model. A motivating objective is to determine which parameters are identifiable or influential in the sense that they are uniquely determined by density functional theory–generated data. Noninfluential parameters will be fixed at nominal values for subsequent Bayesian inference, uncertainty propagation, and material design since variations in these parameters are minimally reflected in responses. Whereas global sensitivity analysis is typically based on the assumption of mutually independent, uniformly distributed parameters, we demonstrate that inherent parameter correlations must be accommodated to achieve correct interpretations of parameter influence. For the considered energy functionals, we demonstrate that all of the parameters are influential and will be informed by density functional theory–simulated data.

A socially responsible consumption index based on non-linear dimensionality reduction and global sensitivity analysis

Higher-order factor analysis is a statistical method that consists of repeating steps of factor analysis. Studies of this type allow researchers and practitioners to visualize the hierarchical structure of the concept being studied. Unfortunately, the Socially Responsible Consumer (SRC) research community still remains unable to construct a second-order SRC index. Most researchers argue that the statistical requirements for the construction of the second-order index are not met. They typically try to construct the second-order index by applying linear factor analysis techniques. It is worth mentioning that this is a widespread practice in the social sciences. In this manuscript, we aim to show how better indices can be created by applying non-linear dimensionality reduction techniques. Specifically, we have modified the Unsupervised Extreme Learning Machine (UELM) method to promote orthogonality in the basis function space. These methods are able to model interactions among the input variables, but unfortunately, they are usually considered black boxes. To overcome this limitation, we propose the use of Global Sensitivity Analysis (GSA) techniques, which are able to estimate the importance of each variable by itself and in conjunction with the others. To test the methodology, we have used a sample of 703 Spanish consumers and a multidimensional SRC metric that considers both social and environmental issues. As expected, the non-linear techniques tend to enhance the results provided by the linear techniques.

On the use of global sensitivity analysis for the numerical analysis of permeable pavements

The aim of this study is to investigate the use of different global sensitivity analysis techniques in conjunction with a mechanistic model in the numerical analysis of a permeable pavement installed at the University of Calabria. The Morris method and the variance-based E-FAST procedure are applied to investigate the influence of soil hydraulic parameters on the pavement’s behavior. The analysis reveals that the Morris method represents a reliable computationally cheap alternative to variance-based procedures for screening important factors and provides the first inspection of the model. The study is completed by a combined GSA-GLUE uncertainty analysis used to evaluate the model accuracy.

Evaluating the Use of Global Sensitivity Analysis in Dynamic MFA

SummaryDynamic material flow analysis (MFA) provides information about material usage over time and consequent changes in material stocks and flows. In order to understand the effect of limited data quality and model assumptions on MFA results, the use of sensitivity analysis methods in dynamic MFA studies has been on the increase. So far, sensitivity analysis in dynamic MFA has been conducted by means of a one‐at‐a‐time method, which tests parameter perturbations individually and observes the outcomes on output. In contrast to that, variance‐based global sensitivity analysis decomposes the variance of the model output into fractions caused by the uncertainty or variability of input parameters. The present study investigates interaction and time‐delay effects of uncertain parameters on the output of an archetypal input‐driven dynamic material flow model using variance‐based global sensitivity analysis. The results show that determining the main (first‐order) effects of parameter variations is often sufficient in dynamic MFA because substantial effects attributed to the simultaneous variation of several parameters (higher‐order effects) do not appear for classical setups of dynamic material flow models. For models with time‐varying parameters, time‐delay effects of parameter variation on model outputs need to be considered, potentially boosting the computational cost of global sensitivity analysis. Finally, the implications of exploring the sensitivities of model outputs with respect to parameter variations in the archetypical model are used to derive model‐ and goal‐specific recommendations on choosing appropriate sensitivity analysis methods in dynamic MFA.

Improving multi-objective reservoir operation optimization with sensitivity-informed dimension reduction

Abstract. This study investigates the effectiveness of a sensitivity-informed method for multi-objective operation of reservoir systems, which uses global sensitivity analysis as a screening tool to reduce computational demands. Sobol's method is used to screen insensitive decision variables and guide the formulation of the optimization problems with a significantly reduced number of decision variables. This sensitivity-informed method dramatically reduces the computational demands required for attaining high-quality approximations of optimal trade-off relationships between conflicting design objectives. The search results obtained from the reduced complexity multi-objective reservoir operation problems are then used to pre-condition the full search of the original optimization problem. In two case studies, the Dahuofang reservoir and the inter-basin multi-reservoir system in Liaoning province, China, sensitivity analysis results show that reservoir performance is strongly controlled by a small proportion of decision variables. Sensitivity-informed dimension reduction and pre-conditioning are evaluated in their ability to improve the efficiency and effectiveness of multi-objective evolutionary optimization. Overall, this study illustrates the efficiency and effectiveness of the sensitivity-informed method and the use of global sensitivity analysis to inform dimension reduction of optimization problems when solving complex multi-objective reservoir operation problems.

The use of global sensitivity analysis for improving processes: Applications to mineral processing

This paper analyzes the application of global sensitivity analysis (GSA) to the improvement of processes using various case studies. First, a brief description of the methods applied is given, and several case studies are examined to show how GSA can be applied to the study to improve the processes. The case studies include the identification of processes; comparisons of the Sobol, E-FAST and Morris GSA methods; a comparison of GSA with local sensitivity analysis; an examination of the effect of uncertainty levels and the type of distribution function on the input factors; and the application of GSA to the improvement of a copper flotation circuit. We conclude that GSA can be a useful tool in the analysis, comparison, design and characterization of separation circuits. In addition, we conclude that using the stage's recoveries of each species as input factors is a suitable choice for the GSA of a flotation plant.

Limits of variance-based sensitivity analysis for non-identifiability testing in high dimensional dynamic models

In systems biology, a common approach to model biological processes is to use large systems of nonlinear differential equations. The associated parameter estimation problem then requires a prior handling of the global identifiability question in a realistic experimental framework. The lack of a method able to solve this issue has indirectly encouraged the use of global sensitivity analysis to select the subset of parameters to estimate. Nevertheless, the links between these two global analyses are not yet fully explored.The present work reveals new bridges between sensitivity analyses and global non-identifiability, through the use of functions derived from the Sobol’ high dimensional representation of the model output. We particularly specify limits of variance-based sensitivity tools to completely conclude on global non-identifiability of parameters in a given experimental context.

Reducing the Complexity of Multiobjective Water Distribution System Optimization through Global Sensitivity Analysis

AbstractThis study investigates the use of global sensitivity analysis as a screening tool to reduce the computational demands associated with multiobjective design and rehabilitation of water distribution systems (WDS). Sobol’s method is used to screen insensitive decision variables and guide the formulation of reduced complexity WDS optimization problems (i.e., fewer decision variables). This sensitivity-informed problem decomposition dramatically reduces the computational demands associated with attaining high-quality approximations for optimal WDS trade-offs. This study demonstrates that the results for the reduced-complexity WDS problems can then be used to precondition and significantly enhance full search of the original WDS problem. Two case studies of increasing complexity—the New York Tunnels network and the Anytown network—are used to demonstrate the proposed methodology. In both cases, sensitivity analysis results reveal that WDS performance is strongly controlled by a small proportion of deci...

Global sensitivity analysis for complex ecological models: a case study of riparian cottonwood population dynamics

Mechanism-based ecological models are a valuable tool for understanding the drivers of complex ecological systems and for making informed resource-management decisions. However, inaccurate conclusions can be drawn from models with a large degree of uncertainty around multiple parameter estimates if uncertainty is ignored. This is especially true in nonlinear systems with multiple interacting variables. We addressed these issues for a mechanism-based, demographic model of Populus fremontii (Fremont cottonwood), the dominant riparian tree species along southwestern U.S. rivers. Many cottonwood populations have declined following widespread floodplain conversion and flow regulation. As a result, accurate predictive models are needed to analyze effects of future climate change and water management decisions. To quantify effects of parameter uncertainty, we developed an analytical approach that combines global sensitivity analysis (GSA) with classification and regression trees (CART) and Random Forest, a bootstrapping CART method. We used GSA to quantify the interacting effects of the full range of uncertainty around all parameter estimates, Random Forest to rank parameters according to their total effect on model predictions, and CART to identify higher-order interactions. GSA simulations yielded a wide range of predictions, including annual germination frequency of 10-100%, annual first-year survival frequency of 0-50%, and patch occupancy of 0-100%. This variance was explained primarily by complex interactions among abiotic parameters including capillary fringe height, stage-discharge relationship, and floodplain accretion rate, which interacted with biotic factors to affect survival. Model precision was primarily influenced by well-studied parameter estimates with minimal associated uncertainty and was virtually unaffected by parameter estimates for which there are no available empirical data and thus a large degree of uncertainty. Therefore, research to improve model predictions should not always focus on the least-studied parameters, but rather those to which model predictions are most sensitive. We advocate the combined use of global sensitivity analysis, CART, and Random Forest to: (1) prioritize research efforts by ranking variable importance; (2) efficiently improve models by focusing on the most important parameters; and (3) illuminate complex model properties including nonlinear interactions. We present an analytical framework that can be applied to any model with multiple uncertain parameter estimates.

Use of global sensitivity analysis and polynomial chaos expansion for interpretation of nonreactive transport experiments in laboratory‐scale porous media

In this work, we show how the use of global sensitivity analysis (GSA) in conjunction with the polynomial chaos expansion (PCE) methodology can provide relevant information for the interpretation of transport experiments in laboratory‐scale heterogeneous porous media. We perform GSA by calculating the Sobol indices, which provide a variance‐based importance measure of the effects of uncertain parameters on the output of a chosen interpretive transport model. The choice of PCE has the following two benefits: (1) it provides the global sensitivity indices in a straightforward manner, and (2) PCE can serve as a surrogate model for the calibration of parameters. The coefficients of the PCE are computed by probabilistic collocation. The methodology is applied to two nonreactive transport experiments available in the literature, while considering both transient and pseudo steady state transport regimes. This method allows a rigorous investigation of the relative effects and importance of different uncertain quantities, which include boundary conditions as well as porous medium hydraulic and dispersive parameters. The parameters that are most relevant to depicting the system's behavior can then be evaluated. In addition, one can assess the space‐time distribution of measurement points, which is the most influential factor for the identifiability of parameters. Our work indicates that these methods can be valuable tools in the proper design of model‐based transport experiments.

Use of global sensitivity analysis in quantitative microbial risk assessment: Application to the evaluation of a biological time temperature integrator as a quality and safety indicator for cold smoked salmon

The aim of this study was to apply a global sensitivity analysis (SA) method in model simplification and to evaluate (eO) ®, a biological Time Temperature Integrator (TTI) as a quality and safety indicator for cold smoked salmon (CSS). Models were thus developed to predict the evolutions of Listeria monocytogenes and the indigenous food flora in CSS and to predict TTIs endpoint. A global SA was then applied on the three models to identify the less important factors and simplify the models accordingly. Results showed that the subset of the most important factors of the three models was mainly composed of the durations and temperatures of two chill chain links, out of the control of the manufacturers: the domestic refrigerator and the retail/cabinet links. Then, the simplified versions of the three models were run with 10 4 time temperature profiles representing the variability associated to the microbial behavior, to the TTIs evolution and to the French chill chain characteristics. The results were used to assess the distributions of the microbial contaminations obtained at the TTI endpoint and at the end of the simulated profiles and proved that, in the case of poor storage conditions, the TTI use could reduce the number of unacceptable foods by 50%.

Sensitivity-guided reduction of parametric dimensionality for multi-objective calibration of watershed models

Problem complexity for watershed model calibration is heavily dependent on the number of parameters that can be identified during model calibration. This study investigates the use of global sensitivity analysis as a screening tool to reduce the parametric dimensionality of multi-objective hydrological model calibration problems while maximizing the information extracted from hydrological response data. This study shows that by expanding calibration problem formulations beyond traditional, statistical error metrics to also include metrics that capture indices or signatures of hydrological function, it is possible to reduce the complexity of calibration while maintaining high quality model predictions. The sensitivity-guided calibration is demonstrated using the Sacramento Soil Moisture Accounting (SAC-SMA) conceptual rainfall–runoff model of moderate complexity (i.e., up to 14 freely varying parameters). Using both statistical and hydrological metrics, optimization results demonstrate that parameters controlling at least 20% of the model output variance (through individual effects and interactions) should be included in the calibration process. This threshold generally yields 30–40% reductions in the number of SAC-SMA parameters requiring calibration – setting the others to a priori values – while maintaining high quality predictions. Two parameters are recommended to be calibrated in all cases (percent impervious area and lower zone tension water storage), three parameters are needed in drier watersheds (additional impervious area, riparian zone vegetation, and percent of percolation going to tension storage), and the lower zone parameters are crucial unless the watershed is very dry. Overall, this study demonstrates that a coupled, multi-objective sensitivity and calibration analysis better captures differences between watersheds during model calibration and serves to maximize the value of available watershed response time series. These contributions are particularly important given the ongoing development of more complex integrated models, which will require new tools to address the growing discrepancy between the information content of hydrological data and the number of model parameters that have to be estimated.

Global sensitivity analysis for latent factor credit risk models

This paper proposes the use of global sensitivity analysis to evaluate the risk associated with a credit portfolio model. Although successfully applied in many disciplines such as chemistry, biology and environmental science, the use of global sensitivity analysis is still rare in financial contexts. One-at-a-time sensitivity analysis is usually applied despite its shortcomings. The scope of this paper is to demonstrate the advantages of a more comprehensive analysis, that is a global sensitivity analysis. As an example, following the works by Frey et al. (2001) and Kiesel and Kleinow (2002), we analyse the static and time-varying uncertainties of three inputs in a latent factor credit risk model: the multivariate distribution of the latent variables, the correlation, and the default probabilities of the obligors. Results show that the relative importance of the inputs strongly depends on the average default probability of the portfolio and the analysed quantiles of the default distribution.

A robust stochastic approach for design optimization of air cooled heat exchangers

This study investigates the use of global sensitivity analysis (GSA) and harmony search (HS) algorithm for design optimization of air cooled heat exchangers (ACHEs) from the economic viewpoint. In order to reduce the size of the optimization problem, GSA is performed to examine the effect of the design parameters and to identify the non-influential parameters. Then HS is applied to optimize influential parameters. To demonstrate the ability of the HS algorithm a case study is considered and for validation purpose, genetic algorithm (GA) is also applied to this case study. Results reveal that the HS algorithm converges to optimum solution with higher accuracy in comparison with GA.