Optimization of forced periodic operations in milli-scale fixed bed reactor for Fischer-Tropsch synthesis

Hydrogenation of CO/CO2 mixtures under unsteady-state conditions: Effect of the carbon oxides on the dynamic methanation process

“…Most recent studies concentrate on the theoretical investigation of the unsteady-state reactor behavior for CO and CO 2 methanation by means of modeling and simulation. Emphasis is on temperature control (Li et al, 2015;Try et al, 2017;Bremer and Sundmacher, 2019;Kreitz et al, 2019b;Theurich et al, 2020), optimal reactor (Kiewidt and Thöming, 2015;Kreitz et al, 2019b;Fischer and Freund, 2020) or catalyst (Zimmermann et al, 2020) design, and possible performance enhancement (Currie et al, 2018;Nikačević et al, 2020), since the exothermic methanation is limited by thermodynamic constraints at higher temperatures and puts high demands for safe operation. Those studies, however, rely on reaction kinetics obtained under steady-state conditions, which are not suitable to investigate the reactor behavior under highly dynamic conditions, as will be shown in the present contribution.…”

Rapid Multi-Objective Optimization of Periodically Operated Processes Based on the Computer-Aided Nonlinear Frequency Response Method

“…The surge of the computer industry at the end of the 20th century and the use of advanced numerical solvers and new algorithms had a great impact in the area of chemical engineering and led to new PI concepts in the field of process synthesis and design [7]. Different dynamic single-objective optimization (SOO) techniques were available for research on a plant-level [8] and the optimization of processes on a unit-level (e.g., reactor operation) [9][10][11][12][13][14].…”

“…For optimizing periodic operations in chemical engineering, a variety of different numerical methods have been introduced [11,[14][15][16][17][18][19][20][21][22]. Most methods, in the beginning, were single-objective optimization algorithms that computed the time-average performance of a process [15,16].…”

“…Most methods, in the beginning, were single-objective optimization algorithms that computed the time-average performance of a process [15,16]. Dynamic optimization methods, which were very suitable for the optimization of periodic processes, allowed users to find an optimal solution around a predefined SS point and rely on local optimization algorithms [11]. In cases where there were many different possibilities regarding the modulated inputs and no fast and accurate way of calculating time-average performance, the dynamic local SOO proved to be time-consuming, numerically expensive, and inaccurate (far from the global optimum), leading to only marginal process improvements [14].…”

“…Additionally, MOO does not depend on user-supplied initialization procedures. However, not many examples can be found where global dynamic SOO or MOO were used for the optimization of FP operation with modulation of several input variables [11,14].…”