Adaptive temperature control for high-precision solar furnace operation

Abstract

Dedicated to the processing and analysis of materials with concentrated solar energy, solar furnaces serve as fundamental tools in experimental contexts. However, the control and precise operation of these systems presents significant challenges due to their nonlinear dynamics, the presence of unpredicted disturbances, and the specific operating requirements of the system. This work presents an adaptive controller based on a Model Reference Adaptive Control (MRAC) methodology for temperature control in solar furnaces. The proposed controller makes progress regarding the existing literature as it outperforms other control strategies in tracking ramping reference signals, which are essential for thermal cycling processes in solar furnaces. Moreover, this solution only includes two additional tuning parameters to the conventional Proportional, Integral, and Derivative (PID) control structure used in these systems, facilitating its application in daily operations. The MRAC controller was rigorously tested and compared with traditional PID solutions used in solar furnaces in a simulated environment using actual data and a validated nonlinear model. Furthermore, the MRAC strategy was tested in the existing solar furnace SF60 at the Plataforma Solar de Almería (Spain). The proposed controller demonstrated substantially lower error metrics than conventional strategies in simulation, reducing the IAE by approximately 75% and 50% compared to the PID and adaptive PID controllers, respectively, and achieving an RMSE of only 0.5 °C. Moreover, it showed promising performance in addressing real-world operational challenges. These achievements validate the proposed adaptive MRAC strategy and position it as a valuable tool for regular operations.