Solar thermal technologies are already available on the market, and they are robust and relatively cheap. Unfortunately, solar heat is seldom used in the industrial processes, and the main obstacle of solar heat diffusion is often the lack of adequate predictive modelling of solar plant integration that identifies its energy potential, economic feasibility, and environmental benefits. In this paper, we aim to investigate and evaluate the possibility of supplying solar heat to the pasta-drying process located in the northeast of the Italian Alps (“Felicetti”). The methodology proposed is structured with the combination of several software, namely, PVGIS^®, Matlab^®, Dymola^®. The methodology developed is tested, considering solar thermal energy as the primary source, in different geographical contexts.

The integration of Hydrogen technologies in different end-uses such as transport, electric microgrids, residential and industrial applications, will increase exponentially soon. Hydrogen as energy carrier allows more favourable energy conversion than other conventional systems and is crucial in worldwide decarbonize end uses. The production of green hydrogen, using RES, is a key area for the evolution of this technology. In this context, SWITCH is a Horizon 2020 European Project that aims to design, build and test an in-situ fully integrated and continuous multisource hydrogen production system, based on solid oxide cell technology. Reversible Solid Oxide Cell (rSOCs) technologies allow to convert renewable energy as hydrogen in the power-to-gas application (P2G) and in reversible mode is able to produce electricity from hydrogen stored, power-to-power application (P2P). rSOCs are really interesting to stabilize the random nature of RES because a combined electrolysis and fuel cell system should be able to switch between the two modes as quickly as possible in order to optimize the integration and the use of RES. However, rSOCs need a complex BoP from the thermal point of view, able to guarantee high efficiency even at partial load mode as well as easy start-up and shutdown procedures. In this work, a Stack Box Module dynamic model was developed in Modelica environment as a dynamic tool for the definition and optimization of BoP requirements. Stack model was validated in SOFC (Solid Oxide Fuel Cell) and SOE (Solid Oxide Electrolyser). The results of the simulation provide verification of the technical/thermodynamic behaviour and flexibility of a stack box of 70 cells. Dynamic modelling allows to evaluate the effect of the reagent inlet temperatures on the operation and hydrogen production/consumption in terms of yield as well as the transients between the different operative modes. Model has been validated by experimental measurements performed in the laboratory. In particular, the kinetics of the reactions governing steam methane reforming (SMR) was considered from data found in the literature, while the ASR (Area Specific Resistance) value was calibrated according to experimental data. The results of the dynamic model show as model can be a useful design and optimization tool for the SOCs technology.

The Functional Mock-up Interface (FMI) standard is aflagship in the co-simulation and model exchange domain.However, the integration of graph-based computationalmodels—particularly neural networks—into Functional Mock-upUnits (FMUs) has remained a technical challenge due tointeroperability and platform-specific limitations.To address this, we propose ONNX2FMU, a command-line Pythontool that facilitates the deployment of Open Neural NetworkExchange (ONNX) models into FMUs. According to FMI's goodpractices, ONNX2FMU generates C source code to wrap ONNXmodels in Functional Mockup Units, supports FMI versions2.0 and 3.0, and provides multi-platform compilationcapabilities. The tool simplifies the mapping processbetween model description and ONNX model inputs and outputsvia JSON files, ensuring accessibility and flexibility.This paper presents the tool architecture and methodologyand showcases its applicability through illustrativeexamples, including a reduced-order model powered by arecurrent neural network.

Ammonia is a promising zero carbon and sustainable hydrogencarrier that can be used as a fuel in solid oxide fuelcells (SOFC) by offering advantages related to the ease ofstorage and the possibility of being used directly withoutan external reformer. In this study, a Modelica-baseddynamic model of an 'Ammonia to Power' (A2P) system wasdeveloped by integrating ammonia decomposition kinetics,electrochemical reactions, all the system-level componentsand the main control loops. A novel Balance of Plant (BoP)configuration is proposed, featuring a five-way heatexchanger that recovers waste heat primarily using the fuelstream as the thermal energy vector instead of air. Themodel evaluates transient responses to operationalperturbations, the behavior of the different control loops,and recirculation percentage rates to optimize systemperformance. Efficiency is calculated as the ratio of thepower output from the SOFC to the power derived from thefresh ammonia line.