With the worldwide industrialization of black soldier fly (BSF) production, it is necessary to better understand how the rearing scale and larvae density influence the performance of larvae and the quality of the final product. In this study, a factorial experiment was conducted to test the effect of rearing scale and density on the growth and composition of the BSF larvae. The larvae were grown in four different scales (box sizes), keeping the area and feed provided to each larva constant and in two different densities. The results reveal significant differences in the larval growth depending on the scale and density, which could be attributed to the higher temperatures achieved in the bigger scales with a temperature difference of more than 5 °C between the smallest and the biggest scale. Both the scale and the density influenced the composition of the larvae. The crude protein levels were higher on the smallest scale, and the lower density (ranging from 32.5% to 36.5%), and crude fat concentrations were the opposite (ranging from 31.7% to 20.1%). The density also influenced the concentrations of S, Mg, K, P, Fe, Zn, Cu, Al, B, and Co, in addition to the analyzed free amino acids PPS, ALA, CIT, and ANS. Furthermore, the rearing scale influenced the concentration of S, Zn, Cu, and Mo. The results provide further insight into the optimization of BSF production processes and the transfer of lab-scale results into big-scale production.

Controlled environment agriculture is a promising alternative to conventional production methods, as it is less affected by climate change and is often more sustainable, especially in circular and recycling frameworks such as aquaponics. A major cost factor in such facilities, however, is the need for skilled labor. Depending on available resources, there are endless possibilities on how to choose ingredients to realize a desired nutrient solution. At the same time, the composition of the desired solution is subject to fluctuations in fish water quality, fertilizer availability, weather, and plant development. In high-evaporation scenarios, e.g., summer, nutrient solutions might be mixed multiple times per day. This results in a complex, multi-variable task that is time-consuming to solve manually, yet requires frequent resolution. This work aims to help solve this challenge by providing methods to automate the nutrient mixing procedure. A simple mass-balance-based model of a nutrient mixing tank with connections to different water sources, drains, and fertilizers is provided. Using methods of static optimization, a program was developed which, in consideration of various process constraints and optimization variables, is able to calculate the necessary steps to mix the desired solution. The program code is provided in an open-source repository. The flexibility of the method is demonstrated in simulation scenarios. The program is easy to use and to adapt, and all necessary steps are explained in this paper. This work contributes to a higher automation level in CEA.

Trains are a corner stone of public transport and play an important role in daily life. A challenging task in train operation is to avoid skidding and sliding, i.e. spinning or blocking of the wheels, during fast changes of traction conditions, which can, for example, occur due to changing weather conditions, crossings, tunnels or forest entries. The latter depends on local track conditions and can be recorded in a map together with other location-dependent information like speed limits and inclination. In this paper, a model predictive control (MPC) approach is developed. Thanks to the knowledge of future changes of traction conditions, the approach is able to avoid short-term skidding and sliding even under fast changes of traction conditions. In a first step, an optimal reference trajectory is determined by a multiple-shooting approach. In a second step, the reference trajectory is tracked by an MPC setup. The developed method is simulated along a track with fast-changing traction conditions for different scenarios, like changing weather conditions and unexpected delays. In all cases, skidding and sliding is avoided.