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A Comprehensive Review on Performance Prediction of Solar Air Heaters Using Artificial Neural Network

  • Harish Kumar GhritlahreEmail author
  • Purvi Chandrakar
  • Ashfaque Ahmad
Article

Abstract

Solar air heater (SAH) is a most commonly used solar energy utilization system, which collects solar radiation on absorber plate and transmits absorbed thermal energy to the flowing air. Many techniques were used by various researchers for increasing the performance of SAHs by experimental examination, but analytical and experimental studies takes more time and are very costly. To avoid these types of problems soft computing techniques are used, in which artificial neural network (ANN) technique plays an important role to predict and optimize the performances of SAHs. This technique is very popular due to its fast computing speed and ability to solve complicated problems accurately which is not solved by other conventional approaches. For solving any problem programming code is not required which is the main advantage of this technique. The main purpose of present work is to review the work related to applications of neural model for performance prediction of SAHs and find out the research gap for future investigations. Various research works shown in this paper concluded that ANN is very efficient technique for performance prediction of SAHs.

Keywords

Solar air heaters Porous bed Artificial roughness Artificial neural network Learning algorithm Multi-layer perceptron 

List of Symbols

Ac

Area of collector surface (m2)

ANN

Artificial neural networks

ai

Input data

bj

Bias

Cpf

Specific heat (J/kg K)

CGP

Polak–Ribiére conjugate gradient

COV

Coefficient of variance

\( \dot{E} \)

Energy rate (W)

\( {\dot{Ex}} \)

Exergy rate (W)

\( {\dot{Ex}}_{{_{dest} }} \)

Rate of irreversibility (W)

GA

Genetic algorithm

GRNN

Generalized regression neural network

h

Enthalpy (J/kg)

I

Solar intensity (W/m2)

\( \dot{IP} \)

Rate of improvement potential (W)

LM

Levenberg–Marquardt

M

Input parameters

\( \dot{m}_{f} \)

Mass flow rate of air (kg/s)

MRE

Mean relative error

MSE

Mean square error

MAE

Mean absolute error

MLP

Multi-layered perceptron

MLR

Multivariable linear regression

MLFFNN

Multi-layer feed forward neural network

N

Output parameters

NARX

Nonlinear autoregressive exogenous model

NLR

Nonlinear regression

OSS

One step secant

P

Fluid pressure (Pa)

PSO

Particle swarm optimization

\( \dot{Q}_{c} \)

Rate of incident energy on the collector area (W)

\( \dot{Q}_{u} \)

Rate of useful energy gained by air (W)

RMSE

Root mean square error

R

Correlation coefficient

RBF

Radial basis function

Ra

Universal gas constant (J/kg K)

R2

Coefficient of multiple determination

s

Specific entropy (J/kg K)

SAH

Solar air heater

SCG

Scaled conjugate gradient

SSE

Sum square error

SVM

Surface volume method

T

Temperature (K)

Tn

Number of training data sets

wij

Weights

XA

Actual value

XP

Predicted value

WNN

Wavelet neural network

Greek Letters

ηth

Thermal efficiency

ηII

Exergy efficiency

ψ

Specific exergy (J/kg)

\( \rho_{a} \)

Density of air (kg/m3)

\( \beta \)

Ratio of orifice diameter to pipe diameter

Subscripts

a

Ambient air

f

Fluid

c

Collector

fi

Inlet air

fo

Outlet air

fm

Mean air

i

Inlet

o

Outlet

p

Plate

Notes

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Harish Kumar Ghritlahre
    • 1
    Email author
  • Purvi Chandrakar
    • 1
  • Ashfaque Ahmad
    • 1
  1. 1.Department of Energy and Environmental EngineeringChhattisgarh Swami Vivekanand Technical UniversityBhilaiIndia

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