Modeling and uncertainty assessment of dynamical systems and digital twins

Abstract

This thesis presents an innovative methodology aimed to meet the growing demands and challenges in Scientific Machine Learning (SciML) applied to electrical submersible pumps (ESP) and pressure swing adsorption (PSA) processes through robust learning, parameter estimation, deep learning, and digital twins. The proposed methodology comprehensively evaluates multiple facets of uncertainty inherent to identifying the SciML model, considering the literature base, data sensitivity, and computational effort. The methodology identifies and validates deep learning models, using non-linear models to generate and overcome experimental data limitations. The methodology uses an integrated Bayesian method as a methodological step to estimate parameters, assess uncertainty, and validate phenomenological and data-driven models. The method is treated in steps that successfully align the model with experimental data, both dynamically and in a steady state, showing the methodology’s potential to represent the system’s behavior within existing uncertainties. This development enables the construction of reliable and computationally efficient dynamic AI models for planning, building, controlling, and optimizing digital twins. The methodology is put under test in several case studies. The results are composed of models validated against synthetic and experimental data that are compatible with the dynamic behavior of the nonlinear model and its uncertainties. The first validation of the method is carried out through a case study involving the development of a soft sensor for a polymerization reactor, which demonstrates robustness and consistency in the treatment of uncertainties in the SciML field. Two case studies are performed for ESP-based artificial lift systems. In those case studies, the technique showed promise for the characterization and representation of the system and paves the way for applications in oil production fields, particularly in production control, optimization, and assistance. In the context of PSA processes, a new approach is presented for developing a digital twin with uncertainty assessment capable of mapping PSA systems’ cyclical and complex behavior. Through continuous online learning and integrating a new feedback tracker, the digital twin accurately represents and adapts to the complexities of the PSA system, addressing challenges such as adsorbent degradation. This methodology offers answers about the applications of AI and digital twins in optimizing industrial processes and supporting sustainable development in various sectors. Together, these works contribute valuable results and methodologies to their respective fields, demonstrating the potential of advanced technologies to improve system representation, address uncertainties, and pave the way for future developments in industrial applications.