| Optimization of energy parameters in a photovoltaic-thermal solar system by Response Surface Methodology |
| Paper ID : 1234-NICAME1402 |
| Authors: |
|
Sabour Shoja Pour *1, Ali Motevali2, Seyed Hashem Samadi3, Azadeh Ranjbar Nedamani4, Pourya Biparva5 1Department of Biosystem Mechanical Engineering , Faculty of Agricultural Engineering, University of Sari Agricultural Sciences and Natural Resources, Iran 2Department of Biosystem Mechanics Engineering, University of Sari Agricultural Sciences and Natural Resources, Iran 3Department of Biosystems Mechanical Engineering, University of Tarbiat Modares 4Department of Biosystems Mechanical Engineering, University of Sari Agricultural Sciences and Natural Resources 5Department of Basic Sciences, University of Sari Agricultural Sciences and Natural Resources |
| Abstract: |
| The agriculture sector is considered one of the vital economic and environmental sectors in any society, and it holds great importance. In this regard, the utilization of green and sustainable approaches such as greenhouses and renewable energy sources has garnered significant attention in agriculture. In this study, mathematical modeling was used to simulate the overall performance data recorded in a greenhouse. The modeling was carried out using the response surface methodology to optimize the performance of the photovoltaic-thermal system. With the assistance of Design Expert software and employing the factorial design, the optimal optimization treatments were obtained.To achieve this, the study focused on different factors, including time intervals (from 10 am to 3 pm), fluid types (pure water, SiO2, AL2O3, and AL2O3-SiO2), nanoparticle concentrations (0.1%, 0.3%, and 0.5%), and the location of the photovoltaic-thermal system (inside and outside the greenhouse). The main objective of the optimization conditions was to maximize the total efficiency. The response optimization design was then examined for the total efficiency.The results showed a good correlation between the statistical model and the data, with an adjusted R-squared value of over 0.90, indicating a satisfactory fit of the model to the data. Based on the experimental design treatments, the environmental condition of the system and the type of nanofluid had the most significant impact on the response. Following the implementation of the design, the proposed optimal solution with 99% desirability was to use 5.0% concentration of AL2O3-SiO2 nanofluid in the system outside the greenhouse at 3 pm. |
| Keywords: |
| Total Efficiency, Optimization, Energy, Greenhouse, Renewable, RSM |
| Status : Paper Accepted (Poster Presentation) |
