Lab-Scale Study of Desiccant Dehumidification System for Poultry Shed Air-Conditioning in Pakistan

Aleem, Muhammad; Hussain, Ghulam Sabir; Sultan, Muhammad; Miyazaki, Takahiko

doi:10.5109/4102470

Cited by 3 publications

(2 citation statements)

References 32 publications

(39 reference statements)

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“…In this regard, an MEC system integrated with a DAC (M-DAC) system combines characteristics of both S-DAC and MEC systems, and thereby has potential to achieve sensible load via MEC and latent load through the DAC system [8]. The M-DAC system has been investigated in literature for various agricultural applications which involve greenhouse AC [33,34], livestock AC [35,36], and poultry AC [37][38][39][40]. The M-DAC system has shown promising results for these agricultural applications.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Evaporative Cooling Assisted Solid Desiccant Dehumidification System for Agricultural Storage Application

et al. 2022

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The study aims to investigate Maisotsenko cycle evaporative cooling assisted solid desiccant air-conditioning (M-DAC) system for agricultural storage application. Conventional air-conditioning (AC) systems used for this application are refrigeration-based which are expensive as they consume excessive amount of primary-energy. In this regard, the study developed a lab-scale solid silica gel-based desiccant AC (DAC) system. Thermodynamic performance of the developed system was investigated using various adsorption/dehumidification and desorption/regeneration cycles. The system possesses maximum adsorption potential i.e., 4.88 g/kg-DA at higher regeneration temperature of 72.6 °C and long cycle time i.e., 60 min : 60 min. Moreover, the system’s energy consumption performance was investigated from viewpoints of maximum latent, sensible, and total heat as well as latent heat ratio (LHR), which were found to be 0.64 kW, 1.16 kW, and 1.80 kW, respectively with maximum LHR of 0.49. Additionally, the study compared standalone DAC (S-DAC), and M-DAC system thermodynamically to investigate the feasibility of these systems from the viewpoints of temperature and relative humidity ranges, cooling potential (Qp), and coefficient of performance (COP). The S-DAC system showed temperature and relative humidity ranging from 39 °C to 48 °C, and 35% to 66%, respectively, with Qp and COP of 17.55 kJ/kg, and 0.37, respectively. Conversely, the M-DAC system showed temperature and relative humidity ranging from 17 °C to 25 °C, and 76% to 98%, respectively, with Qp and COP of 41.80 kJ/kg, and 0.87, respectively. Additionally, the study investigated respiratory heat generation rate (Qres), and heat transfer rate (Qrate) by agricultural products at different temperature gradient (∆T) and air velocity. The Qres and Qrate by the products were increased with ∆T and air velocity, respectively, thereby generating heat loads in the storage house. Therefore, the study suggests that the M-DAC system could be a potential AC option for agricultural storage application.

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Section: Introductionmentioning

confidence: 99%

Evaluating Evaporative Cooling Assisted Solid Desiccant Dehumidification System for Agricultural Storage Application

et al. 2022

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“…Agricultural products keep their nutritional quality, flavor, and appearance at high relative humidity levels; however shriveling occurs at low relative humidity levels due to excessive transpiration [11,12]. Traditional storage solutions rely on vapor compression, which consumes a significant amount of primary energy while also degrading the environment using toxic refrigerants [13,14]. Furthermore, these systems, particularly for agricultural products, are unable to manage optimal temperature and relative humidity conditions [15,16].…”

Section: Introductionmentioning

confidence: 99%

Study on Solid Desiccant Dehumidification Cooling System for the Storage of Fruits and Vegetables

Hussain¹,

Sultan²,

Aleem³

2021

Proceedings of International Exchange and Innovation Conference on Engineering &Amp; Sciences (IEICES)

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Agriculture sector is a major contributor to meet rising food demand throughout the world. Fruits and vegetables cover a major portion of the food demand. However, about 35-40% portion of the fruits and vegetables is lost because of post-harvest losses. The major reason for the post-harvest losses is improper storage. After harvesting, the fruits and vegetables behave like living organisms and produce sensible as well as latent heat load. Optimum storage could be achieved by removing these heat loads which are a function of environmental conditions like temperature and humidity. Conventional systems like vapor compression-based air-conditioning consume an excessive amount of primary energy. In this principle, Maisotsenko cycle evaporative cooling assisted desiccant air-conditioning (M-DAC) system was explored. The performance of the proposed M-DAC system has thermodynamically investigated the perception of dehumidification potential, cooling capacity, COP, and ideal storage temperature as well as relative humidity ranges. The results reveal that the M-DAC system has achieved dehumidification potential, cooling capacity, and COP of 0.003 kg/kg, 35.5 kJ/kg, and 0.72, respectively.

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Artificial Intelligence for the Prediction of the Thermal Performance of Evaporative Cooling Systems

et al. 2021

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The present study reports the development of a deep learning artificial intelligence (AI) model for predicting the thermal performance of evaporative cooling systems, which are widely used for thermal comfort in different applications. The existing, conventional methods for the analysis of evaporation-assisted cooling systems rely on experimental, mathematical, and empirical approaches in order to determine their thermal performance, which limits their applications in diverse and ambient spatiotemporal conditions. The objective of this research was to predict the thermal performance of three evaporation-assisted air-conditioning systems—direct, indirect, and Maisotsenko evaporative cooling systems—by using an AI approach. For this purpose, a deep learning algorithm was developed and lumped hyperparameters were initially chosen. A correlation analysis was performed prior to the development of the AI model in order to identify the input features that could be the most influential for the prediction efficiency. The deep learning algorithm was then optimized to increase the learning rate and predictive accuracy with respect to experimental data by tuning the hyperparameters, such as by manipulating the activation functions, the number of hidden layers, and the neurons in each layer by incorporating optimizers, including Adam and RMsprop. The results confirmed the applicability of the method with an overall value of R2 = 0.987 between the input data and ground-truth data, showing that the most competent model could predict the designated output features (Toutdb, wout, and Eoutair). The suggested method is straightforward and was found to be practical in the evaluation of the thermal performance of deployed air conditioning systems under different conditions. The results supported the hypothesis that the proposed deep learning AI algorithm has the potential to explore the feasibility of the three evaporative cooling systems in dynamic ambient conditions for various agricultural and livestock applications.

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Lab-Scale Study of Desiccant Dehumidification System for Poultry Shed Air-Conditioning in Pakistan

Cited by 3 publications

References 32 publications

Evaluating Evaporative Cooling Assisted Solid Desiccant Dehumidification System for Agricultural Storage Application

Evaluating Evaporative Cooling Assisted Solid Desiccant Dehumidification System for Agricultural Storage Application

Study on Solid Desiccant Dehumidification Cooling System for the Storage of Fruits and Vegetables

Artificial Intelligence for the Prediction of the Thermal Performance of Evaporative Cooling Systems

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