Global optimization of absorption chiller system by genetic algorithm and neural network

Chow, Tommy W. S.; Zhang, G.Q.; Lin, Zhang; Song, Chuankai

doi:10.1016/s0378-7788(01)00085-8

Cited by 279 publications

(121 citation statements)

References 3 publications

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“…It is quite difficult to accurately describe these processes using classical modelling techniques. Thus, an ANN model, which can be used to resolve complicated and non-linear problems of internal heat transfer, was adopted in this paper [15,25,[29][30][31]. The model with one hidden layer can meet the simulative requirements [32].…”

Section: Three-layer Bp Networkmentioning

confidence: 99%

Analysis of the Effect of the CaCl2 Mass Fraction on the Efficiency of a Heat Pump Integrated Heat-Source Tower Using an Artificial Neural Network Model

Wei

Peng

et al. 2016

Sustainability

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An existing idle cooling tower can be reversibly used as a heat-source tower (HST) to drive a heat pump (HP) in cold seasons, with calcium chloride (CaCl 2 ) aqueous solution commonly selected as the secondary working fluid in an indirect system due to its good thermo-physical properties. This study analyzed the effect of CaCl 2 mass fraction on the effectiveness (ε) of a closed HST and the coefficient of performance (COP) of a HP heating system using an artificial neural network (ANN) technique. CaCl 2 aqueous solutions with five different mass fractions, viz. 3%, 9%, 15%, 21%, and 27%, were chosen as the secondary working fluids for the HSTHP heating system. In order to collect enough measured data, extensive field tests were conducted on an experimental test rig in Changsha, China which experiences hot summer and cold winter weather. After back-propagation (BP) training, the three-layer (4-9-2) ANN model with a tangent sigmoid transfer function at the hidden layer and a linear transfer function at the output layer was developed for predicting the tower effectiveness and the COP of the HP under different inlet air dry-/wet-bulb temperatures, hot water inlet temperatures and CaCl 2 mass fractions. The correlation coefficient (R), mean relative error (MRE) and root mean squared error (RMSE) were adopted to evaluate the prediction accuracy of the ANN model. The results showed that the R, MRE, and RMSE between the training values and the experimental values of ε (COP) were 0.995 (0.996), 2.09% (1.89%), and 0.005 (0.060), respectively, which indicated that the ANN model was reliable and robust in predicting the performance of the HP. The findings of this paper indicated that in order to guarantee normal operation of the system, the freezing point temperature of the CaCl 2 aqueous solution should be sufficiently (3-5 K) below its lowest operating temperature or lower than the normal operating temperature by about 10 K. The tower effectiveness increased with increasing CaCl 2 mass fraction from 0 to 27%, while the COP of the HP decreased. A tradeoff between the tower effectiveness and the COP of the HP should be considered to further determine the suitable mass fraction of CaCl 2 aqueous solution for the HSTHP heating system. The outputs of this study are expected to provide guidelines for selecting brine with an appropriate mass fraction for a closed HSTHP heating system for actual applications, which would be a reasonable solution to improve the system performance.

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Section: Three-layer Bp Networkmentioning

confidence: 99%

Analysis of the Effect of the CaCl2 Mass Fraction on the Efficiency of a Heat Pump Integrated Heat-Source Tower Using an Artificial Neural Network Model

Wei

Peng

et al. 2016

Sustainability

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“…Inspired from Darwin's theory of natural selection, this method has demonstrated its capability to handle discontinuous variables and also noisy objective functions (Wright et al 2002). In addition it can find near-optimal solution using less computing time compared to other methods such as mixed-integer programming (Sakamoto et al 1999), and can be used in conjunction with some non-differentiable RSM methods (Chow et al 2002;Lu et al 2005). Furthermore, GA being a stochastic method has a better chance to explore the entire design space and reach global optimum.…”

Section: Numerical Optimizationmentioning

confidence: 99%

Development of a numerical optimization approach to ventilation system design to control airborne contaminant dispersion and occupant comfort

2012

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“…Artificial Neural Network has been used for different types of modeling in various disciplines including Medicine, Mathematics, Economics, Engineering, Meteorology, Psychology, Hydrology and Neurology [3][4][5]. Neural Networks have become popular since their first inception in 1943 [6].…”

Section: Introductionmentioning

confidence: 99%

Statistical Features Based Approach (SFBA) for Hourly Energy Consumption Prediction Using Neural Network

Wahid¹,

Ghazali²,

Fayaz³

et al. 2017

IJITCS

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Abstract-In this paper, new statistical features based approach (SFBA) for hourly energy consumption prediction using Multi-Layer Perceptron is presented. The model consists of four stages: data retrieval, data preprocessing, feature extraction and prediction. In the data retrieval stage, historical hourly consumed energy data has been retrieved from the database. During data preprocessing, filters have been applied to make the data more suitable for further processing. In the feature extraction stage, mean, variance, skewness, and kurtosis are extracted. Finally, Multi-Layer Perceptron has been used for prediction. For experimentation with MultiLayer Perceptron with different training algorithms, a final model of the network was designed in which the scaled conjugate gradient (trainscg) was used as a network training function, tangent sigmoid (Tansig) as a hidden layer transfer function and linear function as an output layer transfer function. For hourly energy consumption prediction, a total of six weeks data of ten residential buildings has been used. To evaluate the performance of the proposed approach, Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), evaluation measurements were applied.

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Global optimization of absorption chiller system by genetic algorithm and neural network

Cited by 279 publications

References 3 publications

Analysis of the Effect of the CaCl2 Mass Fraction on the Efficiency of a Heat Pump Integrated Heat-Source Tower Using an Artificial Neural Network Model

Analysis of the Effect of the CaCl2 Mass Fraction on the Efficiency of a Heat Pump Integrated Heat-Source Tower Using an Artificial Neural Network Model

Development of a numerical optimization approach to ventilation system design to control airborne contaminant dispersion and occupant comfort

Statistical Features Based Approach (SFBA) for Hourly Energy Consumption Prediction Using Neural Network

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