Precise temperature control in high-temperature furnaces is challenged by nonlinearities, parameter drift, and high thermal inertia. This study proposes an adaptive control strategy combining a classical PID loop with real-time gain updates from a feed-forward artificial neural network (ANN). Implemented on an 18 kW retrofitted Blue-M furnace, the system was characterized by second-order transfer functions for heating and forced convection cooling. A dataset of 9702 samples was built from eight fixed PID configurations tested under a multi-ramp thermal profile. The selected 3-64-64-32-2 ANN, executed in Python and interfaced with LabVIEW, computes optimal gains in 0.054 ms while preserving real-time monitoring capabilities. Experimental results show that the ANN-assisted PID reduces the mean absolute error to 5.08 °C, limits overshoot to 41% (from 53%), and shortens settling time by 20% compared to the best fixed-gain loop. It also outperforms a fuzzy controller and remains stable under ±5% signal noise. Notably, gain reversals during cooling prevent temperature spikes, improving transient response. Relying on commodity hardware and open-source tools, this approach offers a cost-effective solution for legacy furnace upgrades and provides a replicable model for adaptive control in high-temperature, safety-critical environments like metal processing, battery cycling, and nuclear systems.

In order to join efforts to develop high-fidelity multi-physics tools for research reactor analysis, the KIT is conducting studies to modify the coupled multi-physics codes developed for power reactors. The coupled system uses the Monte Carlo Serpent 2 code for neutron analysis and the Subchanflow code for thermo-hydraulic analysis. Serpent treats temperature dependence using the target motion sampling method and Subchanflow was previously extended and validated with experimental data for plate-type reactor analysis. This work present for the first time the steady-state and transient neutron and thermo-hydraulic analysis of an MTR core defined in the IAEA 10 MW benchmark using Serpent2/Subchanflow. Important global and local parameters for nominal steady-state conditions were obtained, e.g., the lowest and highest core plate/channel power/temperature, the radial and axial core power profile at the plate level, and the core coolant temperature distribution at the subchannel level. The capabilities of Serpent2/Subchanflow to perform transient analysis with on-the-fly motion of the control plates were tested, namely with fast and slow reactivity insertion. Based on the unique results obtained for the first time at the subchannel and plate level, it can be stated that the coupled Serpent2/Subchanflow code is a very promising tool for research reactor safety-related investigations.

A representative urban driving cycle was developed for Quito, Ecuador, using the K-Means clustering method. From 64 samples and 188,713 geospatial and speed data points, a 2870 s driving cycle was constructed to capture real-world traffic characteristics. Key parameters include an average speed of 22.68 km/h, acceleration and deceleration rates of 0.55 m/s² and −0.57 m/s², and a dwell time of 9.66%. Due to Quito’s linear urban development, where mobility is limited to north–south/south–north corridors, the driving cycle reflects frequent accelerations and decelerations along congested arterial roads. A comparative analysis with international driving cycles revealed that Quito’s traffic follows a unique pattern shaped by its geographic constraints. The HK cycle in China showed the greatest similarities, although differences in instantaneous speeds highlight the need for localized models. While this study primarily focuses on methodological robustness, the developed driving cycle provides a foundational dataset for future research on traffic flow optimization, emissions estimation, and sustainable urban mobility strategies. These insights contribute to data-driven decision-making for improving transportation efficiency and environmental impact assessment in cities with similar urban structures.

In recent years, the use of simulation in industrial processes and research has grown significantly, largelydue to computational technology advancements. Techniques like Computational Fluid Dynamics (CFD), Finite ElementMethods (FEM), simulations are widely used across various fields. In the food industry, multidomain simulationcodes have optimized cooking and cooling times in critical equipment such as ovens and kettles. However, highlydetailed simulations, especially for heat transfer in cooking processes, require substantial computational resources, often surpassing the capabilities of multidomain approaches. This work focuses on simulating an oven and kettleused in ham production. The cooking process is modeled using COMSOL Multiphysics to determine optimal cookingtimes and temperatures. The resulting temperature and time data are exported to Wolfram SystemModeler to controlsteam of the on/off cycles of the heaters precisely.Results show a strong correlation between COMSOL and Wolframsimulations, with a 5 % error in cooking time for the kettle and a 15 % deviation in steam generation. Similarly, the ovenpresents a 5 % error between simulated and experimental cooking values. The integration of FEM-based simulationswith multidomain modeling enhances the accuracy of heating time predictions, contributing to the optimization of foodproduction processes.