Enhancing solar furnace thermal stress testing using an adaptive model and nonlinear predictive control

Solar furnaces rely on renewable energy for thermal tests using concentrated solar direct irradiance. To address the complex dynamics and constraints of these systems, a novel control approach is introduced based on an adaptive model strategy. The proposed adaptive model representation with an Adaptive Practical Nonlinear Predictive Controller (A-PNMPC) strategy is employed in the solar furnace SF60 at Plataforma Solar de Almeria (Spain), demonstrating enhanced model prediction that culminates in a finer control performance. A Recursive Least Squares solution for model adaptation adjusts the model dynamics to the actual behavior of the solar furnace. Then, the predictive control strategy uses the updated model to calculate optimal control actions that lead the sample temperature to the desired reference. Simulation studies demonstrated the superior performance of the A-PNMPC in temperature control and prediction compared to the constant parameter model PNMPC strategy. The proposed A-PNMPC was compared to the conventional PNMPC in simulations, showing up to a 5% improvement in control performance and an 80% in large model predictions. Additionally, the A-PNMPC was tested on the actual system for various temperature profiles, achieving a mean error of less than 10 degrees C in sample temperature ramp tracking