The measurement of the spatially distributed flow field in large industrial vessels, e.g. biogas fermenters, is not possible with commercially available measurement equipment. Therefore the concept of flow following sensor particles has been developed. They act as data loggers within the vessel. The sensor particles are equipped with a pressure sensor and an inertial measurement unit to measure their motion. The measurements are taken in the coordinate frame of the sensor particle and need to be transformed into the vessel’s coordinate frame to analyze the flow field. This is done by an error-state Kalman filter which estimates the orientation of the sensor particle with respect to the vessel based on the direction of the gravitational acceleration and the local magnetic field. Since no other aiding sensors are available or usable within an industrial vessel, particular emphasis was given to the inertial sensors’ stochastic modelling and the accelerometer’s calibration. Based on the orientation estimation, the measured acceleration is transformed into the vessel frame. We performed two experiments in a 1.4 m³ lab reactor to qualify this approach. The results show, that the transformed acceleration is in good qualitative agreement with the known flow field inside the lab reactor.

The growing demand for bio-pharmaceuticals necessitates improved methods for the characterization of stirred tank reactors (STRs) and their mixing heterogeneities. Traditional Eulerian measurement approaches fall short, culminating in the use of Lagrangian Sensor Particles (LSPs) to map large-scale STRs and track the lifelines of microorganisms such as Chinese Hamster Ovary cells. This study investigates the hydrodynamic characteristics of LSPs, specifically examining the effects that the size and position of the Center of Mass (CoM) have on their flow-following capabilities. Two Lagrangian Particle (LP) designs are evaluated, one with the CoM and a Geometric Center aligned, and another with a shifted CoM. The experimental study is conducted in a rectangular vessel filled with deionized water featuring a stationary circular flow. Off-center LPs exhibit higher velocities, an increased number of floor contacts, and moreover, a less homogeneous particle probability of presence within the vessel compared to LPs with CoM and Geometric Center aligned. Lattice Boltzmann Large Eddy Simulations provide complementary undisturbed fluid velocity data for the calculation of the Stokes number $St$. Building upon these findings, differences in the Stokes number $St$ between the two LP variants of $\Delta St$ = 0.01 (25 mm LP) and $\Delta St$ = 0.13 (40 mm LP) are calculated, highlighting the difference in flow behavior. Furthermore, this study offers a more representative calculation of particle response time approach, as the traditional Stokes number definition does not account for non-homogeneous particles, resulting in an alternative Stokes number ($\Delta {St}_{\mathrm{alt}}$ = 0.84 (25 mm LP) and $\Delta {St}_{\mathrm{alt}}$ = 2.72 (40 mm LP)). This study contributes to the improved characterization of STRs through the use of Lagrangian Sensor Particles. Results highlight the implications the internal mass distribution has on LSP design, offering crucial considerations for researchers in the field.

Biogas is an important driver in carbon-neutral energy sources. Many biogas digester setups, however, are not well optimized and waste energy or fail to maximize their gas output potential. To optimize these systems, a framework was developed to measure and predict digester systems’ efficiencies by closely monitoring fluid movements. This framework includes a numerical calculation of fluid behavior (Computational Fluid Dynamics (CFD)), and Deep Learning to estimate the fluid shear-rates introduced by the agitator’s action. Additionally, a novel measurement system is presented that can measure the same metrics, as simulated, in real-world environments. Lastly, an outlook is given that presents the options and extensions of the presented setup to reduce prediction error, minimize measuring efforts further, and recommend optimization approaches to the operator.