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Marcus Johan Schytt

Publications and source records attributed to Marcus Johan Schytt.

3 recordsLinked to original sources

A Systematic Modeling Framework for Dynamic Simulation of Fixed-Bed Reactors

We present a modular and thermodynamically consistent modeling framework for simulating steady-state and transient behavior in fixed-bed reactors. Accurate simulation of dynamic reactor behavior is essential for enabling flexible operation in Power-to-X (P2X) applications, such as Power-to-Ammonia and Power-to-Methanol, where fluctuating renewable energy inputs demand robust and responsive process control. The proposed models integrate non-ideal thermodynamics through cubic equations of state and account for both advective and dispersive transport phenomena. We derive consistent mass and energy balances using internal energy as the energy state variable, and obtain temperature and pressure from thermodynamic constraints. Our simulation methodology provides the necessary model functions for steady-state and dynamic simulations, as well as parametric sensitivity analysis. It is applied to two fundamental fixed-bed reactor units, the fixed-bed reactor (FBR) and the direct-cooled reactor (DCR). In the context of ammonia synthesis, we simulate representative reactor variants, the adiabatic fixed-bed reactor (AFBR) and the isothermal direct-cooled reactor (IDCR). Simulations assess the impact of real and ideal thermodynamic models, transport assumptions, and steady-state approximations. Results show that real-fluid effects at elevated pressures significantly influence steady-state outlet temperatures and conversions for the IDCR, while common literature model assumptions generally provide accurate dynamic predictions. Altogether, the framework supports systematic reactor model development and analysis under variable operating conditions and model assumptions relevant to Power-to-X applications.

math.DS↗

Hybrid Optimization Methods for Parameter Estimation of Reactive Transport Systems

This paper presents a hybrid optimization methodology for parameter estimation of reactive transport systems. Using reduced-order advection-diffusion-reaction (ADR) models, the computational requirements of global optimization with dynamic PDE constraints are addressed by combining metaheuristics with gradient-based optimizers. A case study in preparative liquid chromatography shows that the method achieves superior computational efficiency compared to traditional multi-start methods, demonstrating the potential of hybrid strategies to advance parameter estimation in large-scale, dynamic chemical engineering applications.

math.OC↗

Numerical Methods for Optimal Boundary Control of Advection-Diffusion-Reaction Systems

This paper considers the optimal boundary control of chemical systems described by advection-diffusion-reaction (ADR) equations. We use a discontinuous Galerkin finite element method (DG-FEM) for the spatial discretization of the governing partial differential equations, and the optimal control problem is directly discretized using multiple shooting. The temporal discretization and the corresponding sensitivity calculations are achieved by an explicit singly diagonally-implicit Runge Kutta (ESDIRK) method. ADR systems arise in process systems engineering and their operation can potentially be improved by nonlinear model predictive control (NMPC). We demonstrate a numerical approach for the solution to their optimal control problems (OCPs) in a chromatography case study. Preparative liquid chromatography is an important downstream process in biopharmaceutical manufacturing. We show that multi-step elution trajectories for batch processes can be optimized for economic objectives, providing superior performance compared to classical gradient elution trajectories.

math.NA↗