Optimal COP prediction of a solar intermittent refrigeration system for ice production by means of direct and inverse artificial neural networks

Hernández, J.A.; Rivera, W.; Colorado, D.; Moreno-Quintanar, G.

doi:10.1016/j.solener.2011.12.021

Cited by 27 publications

(5 citation statements)

References 16 publications

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“…To determine the sensitivities of the input factors on the suddenly expanded flow process, an expression provided by Garson [43] was employed, as mentioned by Hernandez et al [44], Hamzaoui et al [45], and Reyes-Te ´llez et al [46]. This method was later improved by Goh [47].…”

Section: Sensitivity Of Input Parametersmentioning

confidence: 99%

Investigation of suddenly expanded flows at subsonic Mach numbers using an artificial neural networks approach

Quadros

Nagpal²,

Khan³

et al. 2022

PLoS ONE

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The purpose of this study is to explore two concepts: first, the use of artificial neural networks (ANN) to forecast the base pressure (β) and wall pressure (ω) originating from a suddenly expanded flow field at subsonic Mach numbers. Second, the implementation of Garson approach to determine the critical operating parameters affecting the suddenly expanded subsonic flow process in the subsonic range. In a MATLAB environment, a network model was constructed based on a multilayer perceptron with an input, hidden, and output layer. The network input parameters were the Mach number (M), nozzle pressure ratio (η), area ratio (α), length to diameter ratio (γ), micro jet control (ϵ), and duct location to length ratio (δ). The network output included two variables; base pressure (β) and wall pressure (ω). The ANN was trained and tested using the experimental data. The experimental results found that micro-jet controls were successful in increasing the base pressure for low Mach numbers and high nozzle pressure ratios. It was also found that the wall pressure was same for with and without micro jet control. The ANN predicted values agreed well with the experimental values, with average relative errors of less than 5.02% for base pressure and 6.71% for wall pressure. Additionally, with a relative significance of 32% and 43%, the nozzle pressure ratio and duct location to length ratio had the highest influence on the base pressure and wall pressure, respectively. The results demonstrate that the ANN model is capable of accurately predicting the pressure results, enabling theoretical foundation for research into pressure distribution in aerodynamic systems.

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Section: Sensitivity Of Input Parametersmentioning

confidence: 99%

Investigation of suddenly expanded flows at subsonic Mach numbers using an artificial neural networks approach

Quadros

Nagpal²,

Khan³

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…The prediction of the output, or target, is performed if the input is within the limits of the database with which the training was carried out [13]. In the present work, a feed-forward network type multilayer neural network model was developed, which has one or more hidden layers of neurons with sigmoid function followed by a layer of linear neurons, this architecture allows establishing linear and not linear transfer functions, providing adjustments in a better way to the studied phenomena [14]. In Figure 2 the implemented architecture is shown, there you can see the number of inputs, the hidden layer with the transfer function where the weights are assigned to each of the variables and their adjustment (bias), and finally the layer linear output with an additional adjustment component corresponding to the linear function [15].…”

Section: Cutting Parameter Rangementioning

confidence: 99%

Prediction of energy consumption in the leadwell v-40 it CNC machining center through artificial neural networks

Rodriguez-Cabal¹,

Marín²,

Rudas³

2022

J Appl Eng Science

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Energy consumption in machining processes has become a problem for today's manufacturing industry. The use of neural networks and optimization algorithms for modeling and prediction of consumption as a function of the cut-off parameters in processes of this type has aroused the interest of research groups. The present work used artificial neural networks (ANN) to predict the energy consumption of a Leadwell V-40iT® five-axis CNC machining center, based on experimental data obtained through a factorial experimental design 53. ANN was programed in Matlab®. From the study was concluded that the depth per pass (Ap) is the variable that has the most influence on the prediction model of energy consumption with a 77% of relative importance, while the feed rate is the least relevant with 9% of importance.

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“…According to Hernández (2009); Labus et al (2012); Hernández et al (2012); Laidi and Hanin, (2013); Hernández (2013); Morales et al (2015); the artificial neural network can be inverted to calculated a desired input parameter. In order to apply this inverse artificial neural network (ANNi), first it is necessary to have the ANN model.…”

Section: Inverse Artificial Neural Networkmentioning

confidence: 99%

Estimación de desempeño y optimización de un sistema de absorción adiabático H2OLiBr usando redes neuronales artificiales

Gutiérrez-Urueta

Colorado

Hernández

et al. 2019

iit

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The search for alternatives to curb climate change and its devastating consequences for today's society, leads to research environmentally friendly climate systems. To optimize or control them, artificial neural networks (ANN) is considered an effective option. Adiabatic absorption is based on separate design for heat and mass transfer process in order to reduce the size of equipment. This study deals with the application of ANN on the experimental results of a single effect water-lithium bromide adiabatic absorption facility and its optimization using an inverse ANN. Transient and steady state data were used to obtain three empirical models. The models developed correspond to the coefficient of performance (COP), cooling power and generation power of the facility. Steady state statistics consists of 219 experimental points obtained at different operating conditions. These data were used to train and test the steady state and transient ANN models. For transient statistics, 1445 values were considered for a period. In the validation data set, the results showed that simulations and the experimental data were in good agreement with an R > 0.98 for both steady state and transient models. A model for COP, based on the principle of accessibility of data, was developed including temperatures for the external fluid circuits with good results. The inverse neural model applied to transient data demonstrated satisfactory results as well, making possible the optimization of the facility. These results illustrate the adequacy in using an ANN with transient data in absorption systems, making it especially attractive for solar cooling applications.

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Optimal COP prediction of a solar intermittent refrigeration system for ice production by means of direct and inverse artificial neural networks

Cited by 27 publications

References 16 publications

Investigation of suddenly expanded flows at subsonic Mach numbers using an artificial neural networks approach

Investigation of suddenly expanded flows at subsonic Mach numbers using an artificial neural networks approach

Prediction of energy consumption in the leadwell v-40 it CNC machining center through artificial neural networks

Estimación de desempeño y optimización de un sistema de absorción adiabático H2OLiBr usando redes neuronales artificiales

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