The successful application of poly(N-vinylcaprolactam)-based microgels requires a profound understanding of their synthesis. For this purpose, a validated process model for the microgels synthesis by precipitation copolymerization with the cross-linker N,N′-methylenebis-(acrylamide) is formulated. Unknown reaction rate constants, reaction enthalpies, and partition coefficients are obtained by quantum mechanical calculations. The remaining parameter values are estimated from reaction calorimetry and Raman spectroscopy measurements of experiments with different monomer/cross-linker compositions. Because of high crosspropagation reaction rate constants, simulations predict a fast incorporation of the cross-linker. This agrees with reaction calorimetry measurements. Furthermore, the gel phase is predicted as the major reaction locus. The model is utilized for a prediction of the internal particle structure regarding its crosslink distribution. The highly cross-linked core reported in the literature corresponds to the predictions of the model.

The flexible operation of energy-intensive processes, such as cryogenic air separation, has economic potential due to increasing fluctuations of the electricity markets. Multiproduct air separation processes with high ratios of liquid product are very promising for flexible operation due to storable products. We present a process design with an integrated liquefication cycle and liquid assist operation, that facilitates a high liquid product ratio and a flexible process operation. We use a mechanistic dynamic process model in steady-state process optimizations covering the wide operational range of the proposed process. The optimization results show that the power demand can be varied in a range from 3.5 to 28 MW without violating operational constraints by changing the nitrogen and oxygen production rates. Thus, the proposed process is a promising air separation candidate for flexible operation with respect to fluctuating electricity markets.

The availability of reduced‐dimensional, accurate dynamic models is crucial for the optimal operation of chemical processes in fast‐changing environments. Herein, we present a reduced modeling approach for rectification columns. The model combines compartmentalization to reduce the number of differential equations with artificial neural networks to express the nonlinear input–output relations within compartments. We apply the model to the optimal control of an air separation unit. We reduce the size of the differential equation system by 90% while limiting the additional error in product purities to below 1 ppm compared to a full‐order stage‐by‐stage model. We demonstrate that the proposed model enables savings in computational times for optimal control problems by ~95% compared to a full order and ~99% to a standard compartment model. The presented model enables a trade‐off between accuracy and computational efficiency, which is superior to what has recently been reported for similar applications using collocation‐based reduction approaches.

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Functional microgels with tailored structure and specific properties are required for medical and technical applications, thus motivating model-based optimization of their fabrication processes. An important step in the creation of accurate models is parameter estimation. We present a methodology for a parameter identifiability analysis, which approximates the feasible parameter set as a box by solving a series of constrained dynamic optimization problems. The method is applied to the synthesis of microgels based on two monomers, N-vinylcaprolactam and N-isopropylacrylamide, and the cross-linker N,N-methylenebis(acrylamide). The results show that kinetic parameters corresponding to the reaction of the monomers are identifiable as are a subset of the kinetic parameters involving the cross-linker. The reaction kinetics of the cross-linking are faster in comparison to the main polymerization reaction for N-vinylcaprolactam; this allows for an improved understanding of the occurring reaction phenomena. The reaction kinetics of the cross-linking are not identifiable for N-isopropylacrylamide for the given experimental setup; model-based experimental design for parameter precision might enable their identification. The results also indicate potential for model simplification and allow us to make suggestions toward the enhancement of Raman spectroscopy measurements.