Multi-stage liquid/liquid extraction with a zaiput apparatus

Poorly separable liquid/liquid mixtures often pose a major challenge in pharmaceutical extraction. In particular, compounds containing a low difference of density are difficult to separate using mixer-settler setups. The Zaiput device can prove an advantage over present liquid/liquid extractors. The research work involves characterizing the Zaiput apparatus for liquid/liquid extraction of pharmaceutical compounds with the model solvent system toluene-water. The extraction efficiency was investigated for a solvent mixture containing toluene-acetone-water residence times. To evaluate the separation success, the outlet’s concentrations are measured via IR spectroscopy

Ligands for M-NHC Synthesis: Continuous Flow Di-N-Alkylation of 1H-Benzimidazole in a fixed Bed Reactor

The successful transfer from batch to a continuous flow process in a fixed bed reactor of a ligand in metalorganic API synthesis, the diazolium salt 1,3-methyl-benzoimidazol-3-ium iodide, is presented. Results show similar yields and conversion rates at corresponding process parameters in batch and continuous mode. By exceeding temperature limitation of a non-pressurized batch process, the pressurized continuous reactor system shows the potential for outperforming the batch synthesis regarding space time yield. Hence, process intensification by continuous flow presents itself as a viable approach for the heterogeneous di-N-alkylation of diazoles. Alternative basic reagents and solvents further enhance the viability of a continuous approach by addressing limitations such as side reactions and solubility of the reagent

Process Model Inversion in the Data-Driven Engineering Context for Improved Parameter Sensitivities

Industry 4.0 has embraced process models in recent years, and the use of model-based digital twins has become even more critical in process systems engineering, monitoring, and control. However, the reliability of these models depends on the model parameters available. The accuracy of the estimated parameters is, in turn, determined by the amount and quality of the measurement data and the algorithm used for parameter identification. For the definition of the parameter identification problem, the ordinary least squares framework is still state-of-the-art in the literature, and better parameter estimates are only possible with additional data. In this work, we present an alternative strategy to identify model parameters by incorporating differential flatness for model inversion and neural ordinary differential equations for surrogate modeling. The novel concept results in an input-least-squares-based parameter identification problem with significant parameter sensitivity changes. To study these sensitivity effects, we use a classic one-dimensional diffusion-type problem, i.e., an omnipresent equation in process systems engineering and transport phenomena. As shown, the proposed concept ensures higher parameter sensitivities for two relevant scenarios. Based on the results derived, we also discuss general implications for data-driven engineering concepts used to identify process model parameters in the recent literature

Parameter Identification Concept for Process Models Combining Systems Theory and Deep Learning

In recent years, dynamic process models have grown even more important in the context of Industry 4.0 and the use of digital twins. However, the accuracy of the corresponding model parameter estimates is determined by the quantity and quality of data and the parameter identification solving methodologies used. Standard methods are based on the ordinary least squares framework. Still, other options are available that might be more sensitive to model parameter variations and ensure more precise parameter estimates. The paper presents a novel technique for parameter identification based on incorporating neural ordinary differential equations for surrogate modeling and differential flatness, i.e., a systems theory concept in control engineering. This approach may lead to improved parameter sensitivities, as demonstrated with a simulation study of a distributed-parameter identification problem assuming a diffusion-type parabolic partial differential equation