Techno-economic analysis of an indirect solar dryer with thermal energy storage: An approach with machine learning algorithms for decision making

Abstract

Machine learning models effectively forecast and improve engineering systems as solar dryers, making them valuable replacements for traditional physics-based models. Also, according to the literature, there is a research gap in solar drying regarding the lack of a comprehensive comparison of machine learning algorithms, neglecting the importance of cross-validation and tuning parameters. Therefore, this research focuses on employing machine learning models as effective surrogate models for solar dryers that attend to this gap, reducing the computational burden. A methodology for developing and comparing machine learning surrogate models of three indirect solar drying technologies (conventional, energy storage with beach sand, and storage with limestone) is proposed. The algorithms were trained using the database generated from a tomato solar drying process, where the solar dryer type, solar irradiance, ambient temperature, wind velocity, and relative humidity were considered model inputs to estimate the absorber plate, solar collector outlet air, and drying chamber outlet air temperatures. Subsequently, from the best predictive algorithm, a techno-economic analysis was conducted annually. The results revealed a high accuracy (R2 of 0.9917) and similar values regarding metrics that measure precision and variance. The solar dryer with thermal energy storage using beach sand exhibited the highest thermal and drying efficiency values (52.52 % and 12.57 %, respectively), while the limestone variant reported the shortest drying time (22 h). The economic evaluation indicated minimal monetary loss (3.09 %) compared to the current system’s annual projection. Finally, the framework presented in this research emphasizes using machine learning models to forecast the system’s energetic and economic performance indicators and contributes as a substitute for experimental studies that involve high cost and long duration. This work lays the bases and guides future research where machine learning algorithms are needed to compare energetic systems’ best computational and techno-economic performance.