SALib: An open-source Python library for Sensitivity Analysis

Herman, Jonathan D.; Usher, Will

doi:10.21105/joss.00097

Cited by 904 publications

(515 citation statements)

References 11 publications

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“…In order to ensure a consistent simulation of the SOC dynamics, data from long‐term field experiments carried out in Müncheberg, where alternative residue managements and organic fertilization levels are tested (Rogasik, Schroetter, Funder, Schnug, & Kurtinecz, ), were used for calibrating model parameters (Stella et al, ). The most influential parameters on the relative change of SOC in NRW during the projection period were identified via sensitivity analysis (Herman & Usher, ; Morris, ) and then calibrated using an automatized algorithm for optimization (DE‐MCz, Houska, Kraft, Chamorro‐Chavez, & Breuer, ). After calibration, the model achieved satisfactory performance in reproducing the time courses of SOC measured in the fields (Stella et al, ; Figure S5).…”

Section: Methodsmentioning

confidence: 99%

Sustainable intensification of crop residue exploitation for bioenergy: Opportunities and challenges

et al. 2019

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Crop residue exploitation for bioenergy can play an important role in climate change mitigation without jeopardizing food security, but it may be constrained by impacts on soil organic carbon (SOC) stocks, and market, logistic and conversion challenges. We explore opportunities to increase bioenergy potentials from residues while reducing environmental impacts, in line with sustainable intensification. Using the case study of North Rhine‐Westphalia in Germany, we employ a spatiotemporally explicit approach combined with stakeholder interviews. First, the interviews identify agronomic and environmental impacts due to the potential reduction in SOC as the most critical challenge associated with enhanced crop residue exploitation. Market and technological challenges and competition with other residue uses are also identified as significant barriers. Second, with the use of agroecosystem modelling and estimations of bioenergy potentials and greenhouse gas emissions till mid‐century, we evaluate the ability of agricultural management to tackle the identified agronomic and environmental challenges. Integrated site‐specific management based on (a) humus balancing, (b) optimized fertilization and (c) winter soil cover performs better than our reference scenario with respect to all investigated variables. At the regional level, we estimate (a) a 5% increase in technical residue potentials and displaced emissions from substituting fossil fuels by bioethanol, (b) an 8% decrease in SOC losses and associated emissions, (c) an 18% decrease in nitrous oxide emissions, (d) a 37% decrease in mineral fertilizer requirements and emissions from their production and (e) a 16% decrease in nitrate leaching. Results are spatially variable and, despite improvements induced by management, limited amounts of crop residues are exploitable for bioenergy in areas prone to SOC decline. In order to sustainably intensify crop residue exploitation for bioenergy and reconcile climate change mitigation with other sustainability objectives, such as those on soil and water quality, residue management needs to be designed in an integrated and site‐specific manner.

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Section: Methodsmentioning

confidence: 99%

Sustainable intensification of crop residue exploitation for bioenergy: Opportunities and challenges

et al. 2019

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“…Sampling the parameter space of the several dozen inputs to the P2D model necessitates a large number of simulations. The Sobol' sampling sequence 36 was used to efficiently explore the high dimensional space and the SALib 38 and savvy 39 python packages were used to generate the sampling sequences.…”

Section: Methodsmentioning

confidence: 99%

Analysis of Li-Ion Battery Electrochemical Impedance Spectroscopy Data: An Easy-to-Implement Approach for Physics-Based Parameter Estimation Using an Open-Source Tool

Murbach

Schwartz

2018

J. Electrochem. Soc.

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The quantitative analysis of electrochemical impedance spectroscopy (EIS) data is important for both characterization and prognostic applications in many electrochemical systems. Here we describe an open-source platform, the ImpedanceAnalyzer, for easy-to-use physics-based analysis of experimental EIS spectra. To demonstrate the use of the platform, we explore the basic capabilities of the pseudo two-dimensional (P2D) battery model to predict publicly available experimental EIS data from a 1500 mAh commercial lithium-ion (LiCoO 2 /graphite) cell. An a priori computed dataset of 38,800 P2D-based impedance spectra simulations, covering a wide range of frequencies (1 mHz to 100 kHz) and model parameters, enables a straightforward least squares matching approach for analyzing experimental spectra. We find an average error of 1.73% between the best-matching computed spectrum from the 38,800 member library and the experimental spectrum being analyzed. Our analysis shows there is significant opportunity to improve the fit between experimental data and physics-based impedance simulations by a combination of a larger computed dataset, local optimization, and further additions to the model physics. Electrochemical impedance spectroscopy (EIS) is a powerful tool for investigating a wide variety of electrochemical systems. 1-3 EIS spectra separate individual electrochemical processes by their characteristic timescales, enabling both qualitative and quantitative analysis of electron transport, 4,5 reaction rates and mechanisms, 6,7 intercalation processes, 8 mass transport, 9,10 and electrode structure. 11,12 The noninvasive nature of EIS also makes impedance measurements useful in prognostic applications such as fuel cell health estimations 13,14 or prediction of remaining useful lifetime in batteries. 15,16 Qualitative analysis of EIS spectra generally involves assessing the shape of Nyquist plot features to determine the relative importance of different physicochemical processes. 17,18 In contrast, quantitative analysis relies on fitting a model to the data in order extract values for specific thermodynamic, transport, and/or kinetic parameters. Most experimental datasets are analyzed quantitatively using an equivalent circuit analog. Fitting an equivalent circuit to EIS data is straightforward using standard least squares regression techniques. 19,20 A good fit can often be found with a relatively simple equivalent circuit, particularly if non-ideal elements like the constant phase element are used. Moreover, many simple equivalent circuits, like the Randles' circuit, 21 have physically interpretable parameters based on linearized electrochemical processes. However, as more complex equivalent circuits are derived and utilized, the lumped parameters can lose their direct physical interpretability and the structure of the equivalent circuit analogs themselves can be degenerate. 22 An alternative to equivalent circuits for quantitative analysis of EIS data is to directly fit the data with a physics-based mathematical model ...

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“…Global Sobol sensitivity analysis was implemented in the open source Sensitivity Analysis Library in Python (SALib). 14,15 SALib was used to determine the sensitivity of the area under the effect curve (AUEC) of bortezomibinduced cell death in 24 hours to changes in the individual protein turnover rate constant parameters, keeping all the other model parameters (stimulatory coefficients and power coefficients) fixed to their estimated values. The AUEC was calculated as the difference between the area under the cell proliferation and cell death curves.…”

Section: Global Sensitivity Analysismentioning

confidence: 99%

Pharmacodynamic Models of Differential Bortezomib Signaling Across Several Cell Lines of Multiple Myeloma

Ramakrishnan

Mager

2018

CPT Pharmacom & Syst Pharma

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The heterogeneous polyclonal nature of multiple myeloma complicates the identification of protein biomarkers predictive of drug response. In this study, a pharmacodynamic systems modeling approach was used to link in vitro bortezomib exposure and myeloma cell death. The exposure‐response was integrated through a network of important protein biomarker dynamics activated by bortezomib in four myeloma cell lines. The pharmacodynamic models reasonably characterized the protein and myeloma cell dynamics simultaneously following bortezomib (20 nM) treatment. The models were used to identify differences in pathway dynamics across cell lines from model‐estimated protein biomarker turnover parameters and global sensitivity analyses. Additionally, a statistical correlation analysis between drug sensitivity and model‐fitted protein activation profiles (i.e., cumulative area under the protein expression‐time curves) supported the identification of shared biomarkers associated with sensitivity differences among the cell lines. Both types of analysis identified similar important proteins associated with bortezomib pharmacodynamics, such as phosphorylated Nuclear Factor kappa‐light‐chain‐enhancer of activated B cells (pNFkappaB), phosphorylated protein kinase B (pAKT), and caspase‐8 (Cas 8).

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SALib: An open-source Python library for Sensitivity Analysis

Cited by 904 publications

References 11 publications

Sustainable intensification of crop residue exploitation for bioenergy: Opportunities and challenges

Sustainable intensification of crop residue exploitation for bioenergy: Opportunities and challenges

Analysis of Li-Ion Battery Electrochemical Impedance Spectroscopy Data: An Easy-to-Implement Approach for Physics-Based Parameter Estimation Using an Open-Source Tool

Pharmacodynamic Models of Differential Bortezomib Signaling Across Several Cell Lines of Multiple Myeloma

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