Revisiting adsorption cooling cycle from mathematical modelling to system development

Teng, Wenchao; Leong, K.C.; Chakraborty, Anutosh

doi:10.1016/j.rser.2016.05.059

Cited by 23 publications

(6 citation statements)

References 126 publications

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“…By applyingh eat recovery, al arge amounto fw asted energy is saved in the form of sensible heat and heat of adsorption during the adsorption/desorption process. [8] An experimental report from Wang showedt hat heat recoverye nhancedt he COP by 25 %, relative to that of the basic cycle. [9] Chua et al presented at ransient model for at wo-bed silica gel/water adsorption chiller.…”

Section: Introductionmentioning

confidence: 99%

Numerical Study on the Combined Heat and Mass Recovery Adsorption Cooling Cycle

et al. 2017

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A numerical simulation of the performance of a fin‐tube‐type adsorption bed with silica gel/water working pairs was conducted. Three models of the heat recovery cycle, the mass recovery cycle, and a combined heat and mass recovery cycle were closely examined. The main goals were to determine 1) the conditions under which these advanced cycles were most effective and 2) the optimum recovery time. Mass recovery enhanced both the coefficient of performance (COP) and specific cooling power (SCP) by up to 24 and 37.5 %, respectively, at 60 °C, and the enhancements of the COP and SCP were 5.0 and 16.0 %, respectively, at 90 °C. Heat recovery increased the COP by 12.56 %, but reduced the SCP by 10.84 % at 60 °C, whereas, at 90 °C, the COP increased by 11.83 % and SCP decreased by 5.96 %. The mass recovery is more influential at a low heating temperature than that at a high heating temperature. Therefore, in the combined heat and mass recovery cycles, the main contribution to the enhancement of the COP comes from mass recovery at lower water temperature. However, at a high heating temperature, the COP increases mainly due to heat recovery.

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Section: Introductionmentioning

confidence: 99%

Numerical Study on the Combined Heat and Mass Recovery Adsorption Cooling Cycle

et al. 2017

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“…Modeling of adsorption systems was usually conducted by means of sets of differential equations. The instantaneous solution of energy balances, mass balances, and adsorption kinetics allowed researchers to predict the performance of cooling devices with reasonable accuracy [10]. However, using such models in pilot plant conditions leads to substantial errors because the device is driven by less stable and only partially-predictable energy sources.…”

Section: Introductionmentioning

confidence: 99%

Predicting Performance of a District Heat Powered Adsorption Chiller by Means of an Artificial Neural Network

Halon

Pelińska-Olko

Szyc³

et al. 2019

Energies

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In this paper, the feasibility of a multi-layer artificial neural network to predict both the cooling capacity and the COP of an adsorption chiller working in a real pilot plant is presented. The ANN was trained to accurately predict the performance of the device using data acquired over several years of operation. The number of neurons used by the ANN should be selected individually depending on the size of the training base. The optimal number of datasets in a training base is suggested to be 35. The predicted cooling capacity curves for a given adsorption chiller driven by the district heating are presented. Predictions of the artificial neural network used show good correlation with experimental results, with the mean relative deviation as low as 1.36%. The character of the cooling capacity curve is physically accurate, and during normal operation for cooling capacities ≥8 kW, the errors rarely exceed 1%.

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“…Mathematical modeling of heat exchangers using both First and Second law of thermodynamics is usually basic point for further analysis of heat transfer systems. Development of adsorption cooling technique in three aspects is shown in [21]. Mathematical modeling, intrinsic and extrinsic developments are used in order to present review of that field from mathematical modeling to innovative applications.…”

Section: Introductionmentioning

confidence: 99%

Energy analysis of heat exchanger in a heat exchanger network

Rauch

Galović

2018

THERM SCI

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Original scientific paper https://doi.org/10.2298/TSCI171231216R For many years now, heat exchanger optimization has been a field of research for a lot of scientists. Aims of optimization are different, having in mind heat exchanger networks with different temperatures of certain streams. In this paper mathematical model in dimensionless form is developed, describing operation of one heat exchanger in a heat exchanger network, with given overall area, based on the maximum heat-flow rate criterion. Under the presumption of heat exchanger being a part of the heat exchanger network, solution for the given task is resting in a possibility of connecting an additional fluid stream with certain temperature on a certain point of observed heat exchanger area. The connection point of additional fluid stream determines the exchanging areas of both heat exchangers and it needs to allow the maximum exchanged heat-flow rate. This needed heat-flow rate achieves higher value than the heat-flow rate acquired by either of streams. In other words, a criterion for the existence of the maximum heat-flow rate, as a local extremum, is obtained within this mathematical model. Results of the research are presented by the adequate diagrams and are interpreted, with emphasis on the cases which fulfill and those which do not fulfill the given condition for achieving the maximum heat-flow rate.

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Revisiting adsorption cooling cycle from mathematical modelling to system development

Cited by 23 publications

References 126 publications

Numerical Study on the Combined Heat and Mass Recovery Adsorption Cooling Cycle

Numerical Study on the Combined Heat and Mass Recovery Adsorption Cooling Cycle

Predicting Performance of a District Heat Powered Adsorption Chiller by Means of an Artificial Neural Network

Energy analysis of heat exchanger in a heat exchanger network

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