Coffee bean particle motion in a spouted bed measured using Positron Emission Particle Tracking (PEPT)

Application of computational fluid dynamics simulations in food industry

Computational fluid dynamics (CFD) is a tool for modelling and simulating processes in many industries. It is usually used as a choice to solve problem involving flow of fluids, heat transfer, mass transfer and chemical reaction. Moreover, it has also found application in the optimization of processes in branches of the food industry, including bread baking, cooling beef roast, or spray drying. CFD has enormous potential and many opportunities to improve the quality and safety of food products, as well as to reduce the costs of production and the use of machines and production equipment. In addition, empirical models only permit data to be extracted at a limited number of locations in the system (where sensors and gauges are placed). CFD allows the designer to examine any location in the region of interest, and interpret its performance through a set of thermal and flow parameters. Computer simulations are the future of every field of science, and the presented overview provides the latest information on experts and experiences related to CFD application in food production. Despite some disadvantages, such as the need to have a large reserve of computing power, the development of digital and IT technologies will make this problem insignificant in the nearest future. Then the CFD will become an indispensable element in the design of equipment and technological lines in the food industry.

Coffee bean particle motion in a rotating drum measured using Positron Emission Particle Tracking (PEPT)

Physicochemical transformation of coffee during roasting depends on the applied time-temperature profile (i.e., rate of heat transfer), with heat transfer phenomena governed by particle dynamics. Positron Emission Particle Tracking (PEPT), a non-invasive imaging technique, was used here to characterise the granular flow of coffee in a real, pilot-scale rotating drum roaster. The experimental study established the impact of drum speed, batch size and bean density (i.e., roast degree) on the system's particle dynamics. Particle motion data revealed two distinct regions: (i) a disperse (low occupancy, high velocity) region of in-flight particles and (ii) a dense (high occupancy, low velocity) bean bed. Implications of these results for heat transfer suggest that controlling drum speed for different density coffees will provide roaster operators with a tool to modulate conductive heat transfer from the heated drum to the bean bed. These comprehensive data thus inform roasting best practices and support the development of physics-driven models coupling heat and mass transfer to particle dynamics.

Autonomous digitizer calibration of a Monte Carlo detector model through evolutionary simulation

Simulating the response of a radiation detector is a modelling challenge due to the stochastic nature of radiation, often complex geometries, and multi-stage signal processing. While sophisticated tools for Monte Carlo simulation have been developed for radiation transport, emulating signal processing and data loss must be accomplished using a simplified model of the electronics called the digitizer. Due to a large number of free parameters, calibrating a digitizer quickly becomes an optimisation problem. To address this, we propose a novel technique by which evolutionary algorithms calibrate a digitizer autonomously. We demonstrate this by calibrating six free parameters in a digitizer model for the ADAC Forte. The accuracy of solutions is quantified via a cost function measuring the absolute percent difference between simulated and experimental coincidence count rates across a robust characterisation data set, including three detector configurations and a range of source activities. Ultimately, this calibration produces a count rate response with 5.8% mean difference to the experiment, improving from 18.3% difference when manually calibrated. Using evolutionary algorithms for model calibration is a notable advancement because this method is novel, autonomous, fault-tolerant, and achieved through a direct comparison of simulation to reality. The software used in this work has been made freely available through a GitHub repository.