Thermodynamic optimization of active and passive indirect solar dryers through hybrid ANN–RSM energy modeling
摘要
The non-uniformity of airflow and the thermodynamic irreversibility of indirect solar dryers are major factors limiting the operation of these equipment. This paper gives a comparative thermodynamic–airflow optimization of the active and passive indirect solar dryers using physics-based hybrid artificial neural network–response surface model (ANN–RSM). There were four configurations that were tested, namely passive baseline (C1), geometry-enhanced passive (C2), active low velocity (C3), and optimized active airflow (C4). Experimentally measured and modeled energy and exergy, entropy generation, moisture ratio, and uniformity of the airflow. The optimal active solution (C4) achieved a highest exergy efficiency (ηex) of 0.22, which is a 46.7% higher than that of the passive baseline (0.15). The decrease in entropy generation between 0.092 and 0.100 W K−1(C1) and 0.080 W/K(C4) was observed. The Pareto front represented predominance of configuration C4, and the ηex values were between 0.21 and 0.23. The optimal functioning was at an approximate of 820 W m−2 solar radiation and 1.05 m s−1 air flow speed as compared to passive systems which were at an approximate of 650 W m−2 and 0.45 m s−1. The physics-constrained ANN generated a stronger R2 > 0.98 and a R2 < 0.005; hence, the high predictive reliability was realized. The increased thermodynamic performance is aligned with Sustainable Development Goal 7 (Affordable and Clean Energy) by maximizing efficiency in the use of renewable energy and is aligned with Sustainable Development Goal 12 (Responsible Consumption and Production) by reducing the amount of post-harvest losses, as well as encouraging sustainable drying methods. The combined optimization scheme creates a thermodynamically sound operating domain of indirect solar drying using a high-efficiency system.