Optimal multivariable conditions in the operation of an absorption heat transformer with energy recycling solved by the genetic algorithm in artificial neural network inverse

Conde-Gutiérrez, R.A.; Cruz-Jacobo, U.; Huicochea, A.; Casolco, S.R.; Hernández, J.A.

doi:10.1016/j.asoc.2018.08.008

Cited by 15 publications

(3 citation statements)

References 44 publications

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“…ANN appeared in the late 1940s (Hastings et al , 1947) and started to gain popularity in 1980s (Cochocki and Unbehauen, 1993) as the computational capabilities of computers started to meet the demands of an ANN. ANN has the characteristics of parallel processing and distributed information storage and is widely applied to calculate, forecast and classify data in different application fields (Affonso et al , 2015; Conde-Gutiérrez et al , 2018; Paradarami et al , 2017). Therefore, the ANN model is adopted to classify the decision maker’s risk attitudes in this research.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated supplier selection approach incorporating decision maker’s risk attitude using ANN, AHP and TOPSIS methods

Zou

Shao

et al. 2019

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Purpose The purpose of this paper is to develop a novel integrated supplier selection approach incorporating decision maker’s risk attitude using the artificial neural network (ANN), analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) methods. Design/methodology/approach In the proposed approach, the ANN model is used to classify decision maker’s risk attitude; the fuzzy AHP method is used to determine the relative weights of evaluation criteria; and the fuzzy TOPSIS method is used to evaluate ratings of suppliers. Finally, experiments are conducted to verify the feasibility and efficiency of the proposed integrated approach. Findings Experiments results show that the proposed integrated approach is effective and efficient to help decision makers to select suitable suppliers according to their risk attitudes. Originality/value The aim of this paper is to develop a novel integrated supplier selection approach incorporating decision maker’s risk attitude using the ANN, AHP and TOPSIS methods. The decision maker’s risk attitude toward procurement transaction is originally considered in supplier selection process.

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Section: Literature Reviewmentioning

confidence: 99%

An integrated supplier selection approach incorporating decision maker’s risk attitude using ANN, AHP and TOPSIS methods

Zou

Shao

et al. 2019

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“…On the other hand, Conde et al [28] studied the conditions to optimize the performance of a heat transformer with energy recycling. It was found that the generation temperature is one of the main variables to pay attention when it is desired to increase the COP and get energy savings.…”

Section: Experimental Investigationsmentioning

confidence: 99%

Modeling of a Double Effect Heat Transformer Operating with Water/Lithium Bromide

2019

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Absorption heat transformers are effective systems for a wide variety of applications; however, their main purpose is to upgrade thermal energy from several sources at low-temperature up to a higher temperature level. In the literature, several advanced configurations for absorption heat transformers have been reported which are mainly focused on the improvement of the gross temperature lift by the use of a double absorption process; however, these systems usually offer a reduced coefficient of performance. The present study proposes a new advanced configuration of an absorption heat transformer that improves the coefficient of performance utilizing a double generation process. The operation of the new configuration was numerically modeled, and the main findings were discussed and presented emphasizing the effect of several parameters on the system performance. The highest coefficient of performance and gross temperature lift were 0.63 and 48 °C, respectively. From its comparison with a single-stage heat transformer, it is concluded that the proposed system may achieve coefficient of performance values up to 25.8% higher than those obtained with the single-stage system, although achieving lower gross temperature lifts.

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“…In the second stage, optimization algorithms, mainly heuristics, are applied to perform the inverse process and obtain the operational parameters that guarantee the desired value. This approach has been successfully used to improve the performance of thermal processes such as heat pumps (Conde-Gutiérrez et al, 2018; Solís-Pérez et al, 2019) and industrial solar heat systems (Ajbar et al, 2021; May Tzuc et al, 2017; Moheno-Barrueta et al, 2021); Nevertheless, according to the review, there are no reports of its use to improve the performance of absorption refrigeration systems.…”

Section: Introductionmentioning

confidence: 99%

Multivariate inverse artificial neural network to analyze and improve the mass transfer of ammonia in a Plate Heat Exchanger-Type Absorber with NH₃/H₂O for solar cooling applications

Tzuc

Chan

Castañeda-Robles

et al. 2022

Energy Exploration & Exploitation

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This work presents a numerical approach to compute optimal operating conditions that maximize the absorption flux into a heat exchanger designed for absorption refrigeration systems. Experimental data were obtained from a test circuit that operates in bubble absorption mode with an inner vapor distributor into a Plate Heat Exchanger-type (PHE-type) and interacts with ammonia vapor, NH3-H2O refrigerant, and cooling water. An artificial neural network (ANN) was trained to correlate the thermal properties of the solution and absorption flux in function of easily measurable parameters (concentrations, mass flows, and pressures of saturated and diluted solutions, flow and temperature of the ammonium vapor, environment temperature, and solution temperature). According to results, ANN is adequate to correlate the operational parameters and the transport phenomena inside the heat exchanger with a precision > 99%. ANN also quantitatively identified the ammonium vapor flow (43.1%), dilute solution flow (18.1%), and dilute solution concentration (13.1%) as the variables most importantly in influencing absorption flux optimization. Subsequently, a multivariable inverse artificial neural network was applied to improve the mass transfer into the PHE-type.It was identified that simultaneous optimization of the ammonia and dilute concentration flow rates improves the absorption flow performance by up to 96.3% under a worst-case scenario (ammonia flow rate<1.4 kg/min) and even 7.04% when even when operating near the amino vapor flow limit (ammonia flow rate>2.0 kg/min). Finally, it was confirmed that incorporating the diluted solution concentration into the optimization contributes to improving the performance of the absorption process 1%. Results obtained are relevant in the search to produce more competitive absorption cooling systems, demonstrating the feasibility of improving the performance of heat exchangers without structural modifications. The proposed methodology represents an interesting option to be implemented to improve performance in solar cooling systems.

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Optimal multivariable conditions in the operation of an absorption heat transformer with energy recycling solved by the genetic algorithm in artificial neural network inverse

Cited by 15 publications

References 44 publications

An integrated supplier selection approach incorporating decision maker’s risk attitude using ANN, AHP and TOPSIS methods

An integrated supplier selection approach incorporating decision maker’s risk attitude using ANN, AHP and TOPSIS methods

Modeling of a Double Effect Heat Transformer Operating with Water/Lithium Bromide

Multivariate inverse artificial neural network to analyze and improve the mass transfer of ammonia in a Plate Heat Exchanger-Type Absorber with NH₃/H₂O for solar cooling applications

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Optimal multivariable conditions in the operation of an absorption heat transformer with energy recycling solved by the genetic algorithm in artificial neural network inverse

Cited by 15 publications

References 44 publications

An integrated supplier selection approach incorporating decision maker’s risk attitude using ANN, AHP and TOPSIS methods

An integrated supplier selection approach incorporating decision maker’s risk attitude using ANN, AHP and TOPSIS methods

Modeling of a Double Effect Heat Transformer Operating with Water/Lithium Bromide

Multivariate inverse artificial neural network to analyze and improve the mass transfer of ammonia in a Plate Heat Exchanger-Type Absorber with NH3/H2O for solar cooling applications

Contact Info

Product

Resources

About

Multivariate inverse artificial neural network to analyze and improve the mass transfer of ammonia in a Plate Heat Exchanger-Type Absorber with NH₃/H₂O for solar cooling applications