• Corpus ID: 239016150

Reduced Order Dynamical Models For Complex Dynamics in Manufacturing and Natural Systems Using Machine Learning

@article{Farlessyost2021ReducedOD,
  title={Reduced Order Dynamical Models For Complex Dynamics in Manufacturing and Natural Systems Using Machine Learning},
  author={Will Farlessyost and Shweta Singh},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.08313}
}
Dynamical analysis of manufacturing and natural systems provides critical information about production of manufactured and natural resources respectively, thus playing an important role in assessing sustainability of these systems. However, current dynamic models for these systems exist as mechanistic models, simulation of which is computationally intensive and does not provide a simplified understanding of the mechanisms driving the overall dynamics. For such systems, lower-order models can… 

References

SHOWING 1-10 OF 15 REFERENCES
White-box Machine learning approaches to identify governing equations for overall dynamics of manufacturing systems: A case study on distillation column
TLDR
Test and compare the efficacy of two white-box ML approaches for predicting dynamics and structure of dynamical equations for overall dynamics in distillation column and demonstrate that a combination of ML approach should be used to identify full range of equations.
Different approaches for the dynamic model for the production of biodiesel
Abstract The industrial production of biodiesel requires mathematical models that adjust to real conditions. The present study is a proposal for the design and validation of a mathematical model for
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
TLDR
This work develops a novel framework to discover governing equations underlying a dynamical system simply from data measurements, leveraging advances in sparsity techniques and machine learning and using sparse regression to determine the fewest terms in the dynamic governing equations required to accurately represent the data.
Sparse Identification of Nonlinear Dynamics with Control (SINDYc)
TLDR
This work generalizes the sparse identification of nonlinear dynamics (SINDY) algorithm to include external inputs and feedback control and connects the present algorithm with the dynamic mode decomposition (DMD) and Koopman operator theory to provide a broader context.
Recovery of Differential Equations from Impulse Response Time Series Data for Model Identification and Feature Extraction
TLDR
In this work, a recent method, which allows reconstructing differential equations from time series data, is extended for higher degrees of automation and an optimization procedure is proposed that fine-tunes the reconstructed dynamical models with respect to model simplicity and error reduction.
"Plant-Friendly" system identification: a challenge for the process industries
The term "plant-friendly" system identification has been used within the chemical process control research community in reference to the broad-based goal of accomplishing informative identification
Modelling Chemical Kinetics of Soybean Oil Transesterification Process for Biodiesel Production: An Analysis of Molar Ratio between Alcohol and Soybean Oil Temperature Changes on the Process Conversion Rate
A mathematical model describing chemical kinetics of transesterification of soybean oil for biodiesel production has been developed. The model is based on the reverse mechanism of transesterification
A comparison of statistical downscaling methods suited for wildfire applications
Place-based data is required in wildfire analyses, particularly in regions of diverse terrain that foster not only strong gradients in meteorological variables, but also complex fire behaviour.
The performance of multilevel perturbation signals for nonlinear system identification
Abstract A method for determining the optimal levels of multilevel perturbation signals for nonlinear system identification is described. A performance index for the optimized signals, directly
Comparative Analysis of the Physico-Chemical, Thermal, and Oxidative Properties of Winged Bean and Soybean Oils
To explore possible food applications, the oxidative stability, antioxidants contents (tocopherols and tocotrienols), thermal properties, and solid fat content of winged bean oil were investigated
...
1
2
...