2026
Modeling and Safe Operation of Fatty Alcohol Ethoxylation in a Continuous Flow Calorimeter
A safe, continuous process for the base-catalyzed ethoxylation of fatty alcohols at the miniplant scale is developed in this work. A flow process model based on preexisting semibatch and literature data is used to design a Fluitec flow calorimeter miniplant. Validation experiments at 1.5 mol equiv (mequiv) of ethylene oxide (EO) agreed well with semibatch kinetics, while 3 mequiv experiments showed considerable discrepancies. Critically, observed temperature peaks increased at higher flow rates, while the model predicts the opposite behavior. Caloric measurements using 1.5 mequiv of EO resulted in an average reaction enthalpy of 88 ± 4 kJ/mol ethylene oxide, which is in good agreement with the literature. A conservative total-failure scenario model predicted maximum temperature rises of 60 K (1.5 mequiv) and 104 K (3 mequiv), whereas experimental values were significantly lower (22 and 38 K). This article shows how simulations and theoretical calculations should guide practical experimental work and vice versa. Moreover, it demonstrates the feasibility of transferring semibatch data to continuous flow systems and emphasizes the importance of model validation under flow conditions.
2024
Development of a Novel Measurement Setup to Study and Predict Electrostatic Discharges in Agitated Glass-Lined Vessels
Two glass lined reactors in a launch platform facility operated by Syngenta have been damaged during the crystallization of an organic compound due to electrostatic discharges. The goal of this work was to design and commission a novel setup to measure charges and currents generated by this slurry in a laboratory-scale reactor. An improved and more sophisticated setup was then proposed for possible implementation in Syngenta's own laboratories. With this novel setup, the electrostatic charging of stirred suspensions involving nonconductive solvents could be accurately measured in the context of a case study that involved the suspension that led to liner damages in the production facilities of Syngenta.
2023
New Scale-Up Technologies for Multipurpose Pharmaceutical Production Plants: Use Case of a Heterogeneous Hydrogenation Process
Minimizing the effort associated with the pilot and laboratory-scale experiments needed for a successful scale-up of a process from laboratory to production scale is a significant challenge in process development. Efficient scale-up is becoming increasingly important in process development due to the growing pressure to reduce costs and timelines while achieving a first-time-right approach. This article describes innovative technologies that enable direct and efficient process scale-up from the laboratory to production scale, while concurrently optimizing scale-dependent parameters through in-depth process understanding. Those technologies include a dynamic process model (based on a digital twin) and a laboratory-scale imitation (Scale-Down-Reactor) of a specific production-scale reactor (4000 L). The core component of the Scale-Down-Reactor is a 3D-printed metallic insert (H/C-Finger), designed to replicate the heat transfer behavior of the production reactor by maintaining a similar heat transfer coefficient and surface-to-volume ratio. In order to maintain comparable gas–liquid mass transfer between the scales, the Scale-Down-Reactor was designed with geometric similarity to its large-scale counterpart. Both mass transfer and heat transfer were experimentally evaluated for the two scales, and the comparison demonstrated an excellent agreement. To finally prove and validate the concept, a hydrogenation process currently running at the production scale was conducted in the Scale-Down-Reactor. As a second technology, a dynamic process model is described that includes a kinetic model of the chemical reactions and a heat/mass transfer model (digital twin) of the aforementioned production-scale reactor. For the gas–liquid mass transfer model, an improved mathematical description (equation) was developed. Moreover, the production-scale hydrogenation process conditions were efficiently optimized using the dynamic process model. The measured reaction mixture temperature profile of the optimized production batch demonstrated excellent agreement with the profile predicted by the dynamic process model. By enabling direct and efficient process scale-up while concurrently optimizing scale-dependent parameters, the technologies described within this article offer a promising approach to reducing costs and timelines while improving process understanding.
2021
New Scale-Up Technologies for Hydrogenation Reactions in Multipurpose Pharmaceutical Production Plants
The classical scale-up approach for hydrogenation reaction processes usually includes numerous laboratory- and pilot-scale experiments. With a novel scale-up strategy, a significant number of these exper-iments may be replaced by modern computational simulations in combination with scale-down experiments. With only a few laboratory-scale experiments and information about the production-scale reactor, a chemical process model is developed. This computational model can be used to simulate the production-scale process with a range of different process parameters. Those simulations are then validated by only a few experiments in an advanced scale-down reactor. The scale-down reactor has to be geometrically identical to the correspond-ing production-scale reactor and should show a similar mass transfer behaviour. Closest similarity in terms of heat transfer behaviour is ensured by a sophisticated 3D-printed heating/cooling finger, offering the same heat exchange area per volume and overall heat-transfer coefficient as in production-scale. The proposed scale-up strategy and the custom-designed scale-down reactor will be tested by proof of concept with model reactions. Those results will be described in a future publication. This project is an excellent example of a collaboration between academia and industry, which was funded by the Aargau Research Fund. The interest of academia is to study and understand all physical and chemical processes involved, whereas industry is interested in generating a robust and simple to use tool to improve scale-up and make reliable predictions.