Work

Drying characteristics, sustainability assessment and machine learning predictions of glass greenhouse drying system with photovoltaic roof

Year: 2025

Type: article

Abstract: To enhance the applicability of photovoltaic drying systems in practical applications, a glass greenhouse drying system with photovoltaic roof system were proposed and tested by drying lemon experiment. The sustainability assessment and color analysis of the system was analyzed based on the environment-economic analysis. Furthermore, operation performances of the system were predicted by five machine learning methods. The results shown that the maximum electrical efficiency of this system was 23.80 %. Under the same outdoor conditions, the average drying rate of the proposed system and open sun drying were 0.2658 (g/g)/h and 0.3033 (g/g)/h, respectively. However, the lemon total color change value dried in the system with 3.15 has better than dried in the open sun drying with 4.28. The Prakash and Kumar model was founded to be the best kinetics model to describe lemon drying in the system. In addition, the economic payback period was 2.04 years, and the CO 2 mitigation was 6.33 ton/year of the drying system with a lifespan of 20 years. Finally, The Convolutional Neural Network (CNN) model was selected to be the optimal model to describe the temperature, humidity and power generation of the drying system with R 2 value 0.9917, 0.9928 and 0.9937, respectively.

Source: Case Studies in Thermal Engineering

DOI: 10.1016/j.csite.2025.106044

Authors: Xiyu Wang, Rurui Xue, Ping Gong, Baoyue Wang, Wengang Hao, Lei Wang

Institutions: Yantai University, Yantai Institute of Technology

Language: English

Topic: Food Drying and Modeling

Subfield: Food Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Climate action, Affordable and clean energy, Responsible consumption and production, Zero hunger, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,620