论文和技术资料

Convective drying of fruits and vegetables is a widely used preservation method and serves to reduce food waste, to make them available off-season and to reduce weight-related transport costs. However, the high energy consumption and impact of the process set-up and process settings on quality are challenges that need to be addressed from a sustainability and consumer perspective. It is known that the necessary optimizations can be carried out by means of physics-based simulation – especially if measurement data allow a comparison with reality, as in the case of a digital twin. However, the heterogeneity of raw materials remains a challenge: no two goods inside the dryer are the same. Here we show how Monte Carlo simulations can be used to account for natural variability by considering process, product and design parameters stochastically instead of deterministically, thus enabling statements to be made not only about drying behavior but also about the distribution of the drying materials before, during and after the drying process. We found that using the Normal Random function, it is possible to insert natural variability into a physics-based model in such a way that the results produced can be statistically evaluated. In addition, we show how the data obtained can be used to determine ideal drying time and the impact this has on energy demand and quality loss. In conclusion we show that when simulating the behavior of natural materials, their heterogeneity must also be considered and evaluated. A combination of physics-based models and Monte Carlo simulation are the tools of choice for this task. In future, this will provide comprehensive information about the totality of the fruits and vegetables processed and can be used to make drying and other food processing methods more quality-conscious and sustainable.