Green Energy and Sustainability ISSN 2771-1641
Green Energy and Sustainability 2024;4(4):0005 | https://doi.org/10.47248/ges2404040005
Original Research Open Access
Performance assessment of packed bed systems for humidity control in greenhouse applications: An experimental based AI modelling approachMrinal Bhowmik , Alessandro Giampieri , Anthony Paul Roskilly , Zhiwei Ma
Correspondence: Mrinal Bhowmik
Academic Editor(s): Christos N. Markides
Received: Jun 3, 2024 | Accepted: Sep 17, 2024 | Published: Oct 1, 2024
Cite this article: Bhowmik M, Giampieri A, Roskilly A, Ma Z. Performance assessment of packed bed systems for humidity control in greenhouse applications: An experimental based AI modelling approach. Green Energy Sustain 2024; 4(4):0005. https://doi.org/10.47248/ges2404040005
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/kgda to an outlet ratio of 9 g/kgda 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 Δωa 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.
Keywordshumidity control, packed bed systems, greenhouse cultivation, multi-layer perceptron neural network
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