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A Novel Approach for Energy Efficiency Prediction of Various Natural Draft Wet Cooling Towers Using ANN

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Abstract

Cooling tower is crucial equipment in the cool-end system of power plant and the natural draft counter-flow wet cooling tower (NDWCT) gets wide application. The artificial neural network (ANN) technique is becoming an effective method for the thermal performance investigation of cooling towers. However, the neural network research on the energy efficiency performance of NDWCTs is not sufficient. In this paper, a novel approach was proposed to predict energy efficiency of various NDWCTs by using Back Propagation (BP) neural network: Firstly, based on 638 sets of field test data within 36 diverse NDWCTs in power plant, a three-layer BP neural network model with structure of 8-14-2 was developed. Then the cooling number and evaporation loss of water of different NDWCTs were predicted adopting the BP model. The results show that the established BP neural network has preferable prediction accuracy for the heat and mass transfer performance of NDWCT with various scales. The predicted cooling number and evaporative loss proportion of the testing cooling towers are in good agreement with experimental values with the mean relative error in the range of 2.11%–4.45% and 1.04%–4.52%, respectively. Furthermore, the energy efficiency of different NDWCTs can also be predicted by the proposed BP model with consideration of evaporation loss of water in cooling tower. At last, a novel method for energy efficiency prediction of various NDWCTs using the developed ANN model was proposed. The energy efficiency index (EEI) of different NDWCTs can be achieved readily without measuring the temperature as well as velocity of the outlet air.

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Abbreviations

a :

actual output (experimental output)

b :

predicted output (network output)

c :

constant

c w :

specific heat capacity of water/kJ·kg−1·°C−

EEI :

energy efficiency index

G a :

mass flow rate of inlet dry air/t·h−1

H :

tower height/m

H w :

central shaft water level/m

h 1 :

specific enthalpy of inlet wet air/kJ·kg−1

h 2 :

specific enthalpy of outlet wet air/kJ·kg−1

h m :

average specific enthalpy of inlet and outlet air/kJ·kg−1

h″ 1 :

saturated air enthalpy when temperature is equal to t1/kJ·kg−1

h″ 2 :

saturated air enthalpy when temperature is equal to t2/kJ·kg−1

k :

number of samples

M :

number of neurons in the hidden layer

MSE :

mean square error

MRE :

mean relative error/%

m :

number of neurons in the output layer

n :

number of neurons in the input layer

pA:

atmospheric pressure/Pa

P e :

evaporative loss proportion/%

Q L :

evaporation loss of water/t·h−1

Q t :

circulating water mass flow rate/t·h−1

R :

correlation coefficient

S :

water-spraying area/m2

t 1 :

inlet water temperature/°C

t 2 :

outlet water temperature/°C

Δt :

temperature difference of cooling water/°

x :

moisture content of air/kg·kg−1

x 1 :

moisture content of inlet air/kg·kg−1

x 2 :

moisture content of outlet air/kg·kg−1

α :

weight coefficient of EEI

β :

weight coefficient of EEI

γ :

weight coefficient of EEI

Δε :

thermal balance error/%

η :

cooling capacity/%

θ :

dry bulb temperature of air/°C

θ 1 :

dry bulb temperature of inlet air/°C

λ :

air/water ratio

μa :

mean value of a set

μb :

mean value of b set

τ :

wet bulb temperature of air/°C

τ 1 :

wet bulb temperature of inlet air/°C

ϕ :

relative humidity of inlet air/%

Ω :

cooling number

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Acknowledgements

This work is supported by the National Key R&D Program of China (Grant No. 2017YFF0209803).

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Authors and Affiliations

  1. Research Institute of Heat Transfer Technology and Equipment, Hefei General Machinery Research Institute Co., LTD (HGMRI), Hefei, 230031, China

    Jialiang Song, Yongdong Chen, Xiaohong Wu & Zhongqing Zhang

  2. East China Electric Power Test & Researcher Institute, China Datang Corporation Science and Technology Research Institute Co., LTD, Hefei, 230088, China

    Shengqi Ruan

Authors
  1. Jialiang Song
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  2. Yongdong Chen
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  3. Xiaohong Wu
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  4. Shengqi Ruan
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  5. Zhongqing Zhang
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Correspondence to Yongdong Chen.

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Song, J., Chen, Y., Wu, X. et al. A Novel Approach for Energy Efficiency Prediction of Various Natural Draft Wet Cooling Towers Using ANN. J. Therm. Sci. 30, 859–868 (2021). https://doi.org/10.1007/s11630-020-1296-0

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  • DOI: https://doi.org/10.1007/s11630-020-1296-0

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