Application of artificial neural network, evolutionary polynomial regression, and life cycle assessment techniques to predict the performance of a new designed solar air ventilator with phase change material

• Study of solar ventilator system with phase change material in copper tubes. • Thermal efficiency improved from 61.2 % to 68.9 % using phase change material . • Artificial Neural Network method slightly overestimated outlet velocity by 2.76 %. • Life Cycle Assessment shows solar ventilators have lower environmental impact . Global warming, greenhouse gas emissions, and environmental pollution are pressing issues worldwide, primarily stemming from the overuse of non-renewable energies and fossil fuels. To address this critical situation, modern building designs must incorporate natural ventilation techniques. A study was conducted to assess the effectiveness of a solar ventilator integrated with phase change material . The study compared the output velocity of the solar ventilator using artificial neural network methodology and empirical data, focusing on air flow speed and conditions with and without phase change material in a 2–4-1 proposed structure. The findings revealed a strong correlation with the experimental data (R 2 > 0.96) and minimal relative error (RE < 2.76 %). Also, for predicting the performance of the solar air ventilator, Evolutionary Polynomial Regression was used. This method validated high accuracy (CoD > 0.99) in predicting airflow. The results also showed that the EPR method had better performance in comparison with the ANN to predict the outlet airflow rate. But the difference wasn’t significant. Furthermore, a life cycle assessment was performed to prioritize a solar ventilators with and without phase change material based on environmental impacts. The results indicated that solar ventilator, particularly those with phase change material, exhibited lower pollution levels compared to without phase change material ventilation system. Despite concerns regarding pollution risks associated with raw materials, the overall reduction in pollution levels was attributed to the clean and renewable nature of solar energy. The study emphasized the importance of transitioning towards sustainable energy sources to mitigate environmental pollution effectively.