Design, commissioning and operation of a large-scale solar linear Fresnel system integrated with evacuated compound receiver: Field testing, thermodynamic analysis, and enhanced machine learning-based optimization

The advancement of solar linear Fresnel reflector (LFR) systems as a viable technology for green heat and power generation has recently garnered significant attention from both researchers and authorities. Regrettably, this technology's lack of experimental performance data and operational parameters hinders its in-depth characterization, complicates modeling and design efforts, and constrains its practical applications. A large utility-scale solar LFR system with evacuated compound receiver is experimentally investigated and numerically modeled in this work. The site selection, technical design, and experimental implementation of the system are technically introduced. Moreover, the experimental performance of system is examined in the harsh outdoor conditions of Al-Khobar, Saudi Arabia and thoroughly assessed in terms of specific heating power per solar collection area, total heat power production, exit hot oil temperature, energy efficiency, and net exergy efficiency. More so, the long-term operation performance of the established LFR system, for the first time, is modeled and predicted using the relevance vector machine (RVM) modeling as a novel machine learning benchmark. Additionally, an innovative version of metaheuristic algorithms, namely simulated annealing algorithm (SAA) is incorporated with the original RVM to explore the optimum RVM hyperparameters towards maximizing the prediction accuracy. Moreover, using eight different statistical measures, the RVM-SAA model is compared with typical RVM and classical ANN. The experimental findings demonstrate the capability of the LFR system to generate approximately 62.30 kW of useful thermal power, with energy, thermohydraulic, and exergy efficiencies reaching up to 35.10 %, 25.95 % and 7.82 %, respectively. Furthermore, the operation of this new LFR system can lead to temperature levels up to 213.25 °C at a continuous thermal oil flow at a rate of 9.0 m³/h. Furthermore, the statistical analyses demonstrated the superiority of the RVM-SAA approach compared to other models investigated for predicting the system performance profiles. Specifically, RVM-SAA achieved the highest deterministic coefficient values of 0.9997 and 0.9988 and the lowest RMSE of 0.4090 and 2.5150 for predicting the system's thermal power production and the outlet oil temperature, respectively.