Traditionally, methanol is produced in large amounts from synthesis gas with heterogeneous Cu/ZnO/Al₂O₃ catalysts under steady state conditions. In this paper, the potential of alternative forced periodic operation modes is studied using numerical optimization. The focus is a well-mixed isothermal reactor with two periodic inputs, namely, CO concentration in the feed and total feed flow rate. Exploiting a detailed kinetic model which also describes the dynamics of the catalyst, a sequential NLP optimization approach is applied to compare optimal steady state solutions with optimal periodic regimes. Periodic solutions are calculated using dynamic optimization with a periodicity constraint. The NLP optimization is embedded in a multi-objective optimization framework to optimize the process with respect to two objective functions and generate the corresponding Pareto fronts. The first objective is the methanol outlet flow rate. The second objective is the methanol yield based on the total carbon in the feed. Additional constraints arising from the complex methanol reaction and the practical limitations are introduced step by step. The results show that significant improvements for both objective functions are possible through periodic forcing of the two inputs considered here.

Methanol synthesis from synthesis gas with heterogeneous Cu/ZnO/Al₂O₃ catalysts in an isothermal gradientless reactor is described. In a theoretical study, the potential of forced periodic operation (FPO) for improving reactor performance in terms of methanol production rate and methanol yield is explored. The approach is based on a detailed kinetic model and combines nonlinear frequency response (NFR) analysis with rigorous numerical multi‐objective optimization. Optimal steady‐state operation is compared with optimal forced periodic operation for a given benchmark problem with and without inert nitrogen in the feed. Further, the significant influence of the saturation capacity of the solid phase on the dynamic behavior in response to step changes and periodic input modulations is studied.

For efficient operation, modern control approaches for biochemical process engineering require information on the states of the process such as temperature, humidity or chemical composition. Those measurement are gathered from a set of sensors which differ with respect to sampling rates and measurement quality. Furthermore, for biochemical processes in particular, analysis of physical samples is necessary, e.g., to infer cellular composition resulting in delayed information. As an alternative for the use of this delayed measurement for control, so-called soft-sensor approaches can be used to fuse delayed multirate measurements with the help of a mathematical process model and provide information on the current state of the process. In this manuscript we present a complete methodology based on cascaded unscented Kalman filters for state estimation from delayed and multi-rate measurements. The approach is demonstrated for two examples, an exothermic chemical reactor and a recently developed model for biopolymer production. The results indicate that the the current state of the systems can be accurately reconstructed and therefore represent a promising tool for further application in advanced model-based control not only of the considered processes but also of related processes.