Experimental Investigation and Neural network based parametric prediction in a multistage reciprocating humidifier

Salins, Sampath Suranjan; Reddy, S. Rama

doi:10.1016/j.apenergy.2021.116958

Cited by 19 publications

(2 citation statements)

References 36 publications

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“…To optimize the operating conditions and configuration of the counter-flow HFMEC, the cooling capacity (Q c ) and COP are selected as performance evaluation indicators [9], and the calculated values are based on a module filled with 1000 hollow fiber tubes. 27 sets of simulation tests based on Through the range analysis [8,10] of table 2 data, figure 4 is obtained.…”

Section: Sensitivity Analysis Resultsmentioning

confidence: 99%

Experimental and numerical investigation of a counter-flow hollow fiber membrane-based evaporative cooler

Yan

Thiam²,

Meng

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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A counter-flow hollow fiber membrane-based evaporative cooler (HFMEC) was proposed. An experimental setup was developed to investigate the air handling performance of the HFMEC under various operating conditions. A numerical model was established and validated. The outlet air temperature predicted by the numerical model using free surface model showed a maximum deviation of 5% comparing with the experimental data. The model was further used to conduct a parametric analysis of the HFMEC. Six key parameters, including geometric specifications and operating conditions, were selected as factors for the sensitivity analysis. The cooling capacity and coefficient of performance (COP) were chosen as performance evaluation indicators. The simulation was performed with 27 cases for the orthogonal test. The range analysis revealed that the inlet air relative humidity and velocity, as well as packing fraction had significant impact on its cooling capacity and COP. Ranking the influence degree of key design parameters on performance evaluation indicators, this paper provides a convenient method to design and optimize the counter-flow HFMEC.

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Section: Sensitivity Analysis Resultsmentioning

confidence: 99%

Experimental and numerical investigation of a counter-flow hollow fiber membrane-based evaporative cooler

Yan

Thiam²,

Meng

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…Details of the ANN modelFor the Artifi cial Neural Network (ANN) in this study, the Levenberg-Marquardt algorithm, also known as TrainLM, was selected due to its superior convergence properties, particularly in regression tasks. Before the commencement of training, bias and weight values are assigned randomly[20][21][22][23][24].These values are then iteratively updated by the TrainLM function, which employs the gradient descent method to optimize the network. The training of the multilayer perceptron (MLP) model adheres to specifi c stopping criteria, namely a minimum gradient of 10-7 and a maximum of 10,000 epochs[25][26][27][28][29][30].…”

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confidence: 99%

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

Mrinal,

Alessandro,

Anthony Paul

et al. 2024

Int J Agric Sc Food Technol

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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.

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Machine learning assisted modeling of thermohydraulic correlations for heat exchangers with twisted tape inserts

Panda

Kumar²,

Patil³

et al. 2022

Acta Mech. Sin.

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Experimental Investigation and Neural network based parametric prediction in a multistage reciprocating humidifier

Cited by 19 publications

References 36 publications

Experimental and numerical investigation of a counter-flow hollow fiber membrane-based evaporative cooler

Experimental and numerical investigation of a counter-flow hollow fiber membrane-based evaporative cooler

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

Machine learning assisted modeling of thermohydraulic correlations for heat exchangers with twisted tape inserts

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