Performance assessment of packed bed systems for humidity control in greenhouse applications: An experimental-based AI modeling approach

Abstract

Optimal humidity control is essential for enhancing crop yields and ensuring favorable growth conditions in greenhouse agriculture. Packed bed systems have emerged as effective tools for regulating humidity levels, yet accurately assessing their performance remains unexplored, especially for temperate oceanic climates. This paper presents a packed-bed system with water as the working fluid to increase the humidity level during the winter for greenhouse cultivation. Accordingly, an experimental setup is developed, and a detailed parametric study is conducted. Further, an artificial intelligence (AI)-based modeling approach is developed for evaluating the performance of packed bed systems for greenhouse applications under varying environmental conditions with various inlet air flowrates, such as 176 m³/hr, 286 m³/hr, 383 m³/hr, and 428 m³/hr. Operating with water at an average temperature of 15.7 °C and a flow rate of 12.8 kg/min, the system achieves a significant 50% increase in humidity ratio, transitioning from an inlet humidity ratio of 6 g/kgda to an outlet ratio of 9 g/kgda, indicating its efficiency in elevating air humidity levels. The multi-layer perceptron neural network, trained with 112 non-repeated datasets and employing a 2-10-10-1 topology, demonstrates high accuracy and resilience in estimating Δωa values for the packed bed system, with predictions closely aligning with experimental data and exhibiting a maximum discrepancy within ± 2.5%. This research contributes to the advancement of precision agriculture practices by providing a comprehensive framework for assessing and improving humidity control in greenhouse environments.