Records with Subject: System Identification
Determination of Holmquist−Johnson−Cook Constitutive Parameters of Coal: Laboratory Study and Numerical Simulation
Beijing Xie, Zheng Yan, Yujing Du, Zeming Zhao, Xiaoqian Zhang
August 15, 2019 (v1)
Keywords: Holmquist–Johnson–Cook constitutive model of briquette, numerical simulation, parameter acquisition, split Hopkinson pressure bar experiment
The main sensitivity parameters of the Holmquist−Johnson−Cook constitutive model for coal were obtained from a variety of tests such as uniaxial compression, uniaxial cyclic loading, splitting and triaxial compression tests, as well as the indirect derivation equation of a briquette. The mechanical properties of briquettes under dynamic impact were investigated using a split Hopkinson pressure bar experiment. Based on the experimental measurement of the Holmquist−Johnson−Cook constitutive model, the numerical simulation of briquette was performed using ANSYS/LS-DYNA software. A comparison between experimental and simulation results verified the correctness of simulation parameters. This research concluded that the failure of briquette at different impact velocities started from an axial crack in the middle of the coal body, and the sample was swollen to some extent. By the increase of impact velocity, the severity of damage in the coal body was increased, while the size of the coal blo... [more]
Data-Driven Estimation of Significant Kinetic Parameters Applied to the Synthesis of Polyolefins
Santiago D. Salas, Amanda L. T. Brandão, João B. P. Soares, José A. Romagnoli
July 31, 2019 (v1)
Keywords: data-driven parameter estimation, global sensitivity analysis, polyolefin synthesis, retrospective cost model refinement algorithm
A data-driven strategy for the online estimation of important kinetic parameters was assessed for the copolymerization of ethylene with 1,9-decadiene using a metallocene catalyst at different diene concentrations and reaction temperatures. An initial global sensitivity analysis selected the significant kinetic parameters of the system. The retrospective cost model refinement (RCMR) algorithm was adapted and implemented to estimate the significant kinetic parameters of the model in real time. After verifying stability and robustness, experimental data validated the algorithm performance. Results demonstrate the estimated kinetic parameters converge close to theoretical values without requiring prior knowledge of the polymerization model and the original kinetic values.
Structural Identifiability of Equivalent Circuit Models for Li-Ion Batteries
Thomas R. B. Grandjean, Andrew McGordon, Paul A. Jennings
July 26, 2019 (v1)
Keywords: equivalent circuit models, lithium ion battery modelling, structural identifiability
Structural identifiability is a critical aspect of modelling that has been overlooked in the vast majority of Li-ion battery modelling studies. It considers whether it is possible to obtain a unique solution for the unknown model parameters from experimental data. This is a fundamental prerequisite of the modelling process, especially when the parameters represent physical battery attributes and the proposed model is utilised to estimate them. Numerical estimates for unidentifiable parameters are effectively meaningless since unidentifiable parameters have an infinite number of possible numerical solutions. It is demonstrated that the physical phenomena assignment to a two-RC (resistor⁻capacitor) network equivalent circuit model (ECM) is not possible without additional information. Established methods to ascertain structural identifiability are applied to 12 ECMs covering the majority of model templates used previously. Seven ECMs are shown not to be uniquely identifiable, reducing the... [more]
Application of Parameter Optimization to Search for Oscillatory Mass-Action Networks Using Python
Veronica L. Porubsky, Herbert M. Sauro
July 25, 2019 (v1)
Keywords: biological networks, BioModels Database, bistable switch, differential evolution, evolutionary algorithm, Hopf bifurcation, mass-action networks, oscillator, parameter optimization, turning point bifurcation
Biological systems can be described mathematically to model the dynamics of metabolic, protein, or gene-regulatory networks, but locating parameter regimes that induce a particular dynamic behavior can be challenging due to the vast parameter landscape, particularly in large models. In the current work, a Pythonic implementation of existing bifurcation objective functions, which reward systems that achieve a desired bifurcation behavior, is implemented to search for parameter regimes that permit oscillations or bistability. A differential evolution algorithm progressively approximates the specified bifurcation type while performing a global search of parameter space for a candidate with the best fitness. The user-friendly format facilitates integration with systems biology tools, as Python is a ubiquitous programming language. The bifurcation−evolution software is validated on published models from the BioModels Database and used to search populations of randomly-generated mass-action... [more]
Incremental Parameter Estimation under Rank-Deficient Measurement Conditions
Kris Villez, Julien Billeter, Dominique Bonvin
May 16, 2019 (v1)
Keywords: extents, graph theory, model identification, observability, optimal clustering, parameter estimation, state decoupling
The computation and modeling of extents has been proposed to handle the complexity of large-scale model identification tasks. Unfortunately, the existing extent-based framework only applies when certain conditions apply. Most typically, it is required that a unique value for each extent can be computed. This severely limits the applicability of this approach. In this work, we propose a novel procedure for parameter estimation inspired by the existing extent-based framework. A key difference with prior work is that the proposed procedure combines structural observability labeling, matrix factorization, and graph-based system partitioning to split the original model parameter estimation problem into parameter estimation problems with the least number of parameters. The value of the proposed method is demonstrated with an extensive simulation study and a study based on a historical data set collected to characterize the isomerization of α -pinene. Most importantly, the obtained resul... [more]
Component Characterization in a Growth-Dependent Physiological Context: Optimal Experimental Design
Nathan Braniff, Matthew Scott, Brian Ingalls
April 15, 2019 (v1)
Keywords: cell physiology, characterization, host-context effects, model fitting, optimal control, optimal experimental design, synthetic biology
Synthetic biology design challenges have driven the use of mathematical models to characterize genetic components and to explore complex design spaces. Traditional approaches to characterization have largely ignored the effect of strain and growth conditions on the dynamics of synthetic genetic circuits, and have thus confounded intrinsic features of the circuit components with cell-level context effects. We present a model that distinguishes an activated gene’s intrinsic kinetics from its physiological context. We then demonstrate an optimal experimental design approach to identify dynamic induction experiments for efficient estimation of the component’s intrinsic parameters. Maximally informative experiments are chosen by formulating the design as an optimal control problem; direct multiple-shooting is used to identify the optimum. Our numerical results suggest that the intrinsic parameters of a genetic component can be more accurately estimated using optimal experimental designs, an... [more]
On-Line Optimal Input Design Increases the Efficiency and Accuracy of the Modelling of an Inducible Synthetic Promoter
Lucia Bandiera, Zhaozheng Hou, Varun B. Kothamachu, Eva Balsa-Canto, Peter S. Swain, Filippo Menolascina
April 8, 2019 (v1)
Keywords: model calibration, model-based optimal experimental design, optimal inputs, synthetic biology, system identification
Synthetic biology seeks to design biological parts and circuits that implement new functions in cells. Major accomplishments have been reported in this field, yet predicting a priori the in vivo behaviour of synthetic gene circuits is major a challenge. Mathematical models offer a means to address this bottleneck. However, in biology, modelling is perceived as an expensive, time-consuming task. Indeed, the quality of predictions depends on the accuracy of parameters, which are traditionally inferred from poorly informative data. How much can parameter accuracy be improved by using model-based optimal experimental design (MBOED)? To tackle this question, we considered an inducible promoter in the yeast S. cerevisiae. Using in vivo data, we re-fit a dynamic model for this component and then compared the performance of standard (e.g., step inputs) and optimally designed experiments for parameter inference. We found that MBOED improves the quality of model calibration by ∼60%. Results furt... [more]
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