Application Of Reduced-Order Methodologies For The Simulation And Optimization Of Chemical Processes For The Energy Industry;

The transition toward sustainable energy systems and carbon-neutral processes requires advanced modeling tools capable of accurately describing complex reactive and adsorptive systems. In this context, methane reactors for CO 2 methanation and hybrid adsorption-reaction systems represent promising technologies for carbon capture, utilization, and energy storage. However, their strongly nonlinear behavior, coupled mass and heat transport phenomena, and the possible occurrence of multiple steady states or oscillatory regimes make their numerical simulation and optimization computationally demanding. This thesis addresses these challenges through the development and application of reduced order modeling (ROM) methodologies for the efficient simulation and optimization of heterogeneous chemical systems. The core focus of the research is the application of reduced-order modeling techniques based on empirical functional basis representations to problems characterized by high- dimensional systems of Ordinary Differential Equations (ODEs), which arise from the discretization of the underlying Partial Differential Equations (PDEs). The problems and their solution through innovative methodologies are reported in Chapter 4 and 5 of this thesis, while Chapter 1, 2 and 3 include an introduction to the background, the mathematical models and the innovative methodologies applied in the thesis. By combining Proper Orthogonal Decomposition (POD) and Discrete Empirical Interpolation Method (DEIM), the work demonstrates that high dimensional models are reduced while preserving their essential physical and dynamical features. The proposed approaches significantly decrease computational cost, enabling accurate prediction of stationary, periodic, and bifurcation behaviors in methane reactors, as well as facilitating optimization studies in non-isothermal reactors. In addition to model-order reduction, the thesis explores the integration of data-driven surrogate models for multicomponent adsorption isotherms, which poses the basis for a hybrid modeling framework suitable for cyclic CO 2 capture and methanation processes. Overall, the research provides methodological advancements and practical tools that support efficient simulation, optimization, and control-oriented applications of complex reactive systems, contributing to the optimal design of energy conversion and carbon management technologies. Moreover, the thesis advances the modeling of self-ignition phenomena in reaction-diffusion systems. Reduced-order models based on POD and POD-DEIM successfully reproduce complex oscillatory and bifurcation behaviors while reducing the dimensionality of the system by orders of magnitude. The incorporation of DEIM proves particularly effective in lowering the computational burden associated with nonlinear terms, enabling speedups exceeding three orders of magnitude in two-dimensional simulations. These achievements demonstrate the robustness and versatility of the reduced-order model approach in both process design and safety-oriented analyses.