Optimal humidity control is essential for enhancing crop yields and ensuring favourable growth conditions in greenhouse agriculture. Packed bed devices are effective tools for regulating humidity levels; however, accurately assessing their performance, especially for temperate oceanic climates, is yet explicitly unexplored. The current paper presents a packed bed system using water as the working fluid to increase humidity during winter for greenhouse cultivation. An experimental setup is developed, and a detailed parametric study is conducted. Also, an artificial intelligence (AI) based multi-layer perceptron neural network (MLPNN) is designed to evaluate the performance of packed bed systems under varying environmental conditions with different inlet air flow rates (176 m³/hr, 286 m³/hr, 383 m³/hr, and 428 m³/hr). The results show that the system achieves a significant 50% increase in humidity ratio, transitioning from an inlet humidity ratio of 6 g/kg_da to an outlet ratio of 9 g/kg_da when operating with water at an average temperature of 15.7°C and a flow rate of 12.8 kg/min. The MLPNN is trained with 112 non-repeated datasets and observed that a topology of 2-10-10-1 includes 2 input neurons, 2 hidden layers with 10 neurons each, and 1 output neuron, has high prediction accuracy in estimating Δω_a values for the packed bed system. The predictions closely align with experimental data, showing a maximum discrepancy within ±2.5%. This research advances the use of packed bed systems by providing a comprehensive framework for assessing and improving humidity control in greenhouse environments.

humidity control, packed bed systems, greenhouse cultivation, multi-layer perceptron neural network