Abstract

The performance evaluation of a natural convection solar dryer is a complex one because of the transient and non-linear nature of atmospheric conditions. In this comparative study, a smart neural network -based tool was developed for estimating the performance of such a transient nature solar dryer. For this purpose, a series of experimental studies are conducted through four successive days and compared with the generalized regression neural network (GRNN) modeling. GRNN architecture proposed in this study consists of three inputs (time duration, irradiance, and ambient temperature) and four outputs (drying chamber temperature, the mass of moisture removed, drying rate, and dryer efficiency). Such generalized regression neural network architecture was trained, tested, and validated with real-time experimental variable data sets. The results of the GRNN model are in good agreement with experimental results. The overall accuracy of the proposed GRNN model in predicting the performance is 96.29%.

References

References
1.
Kabeel
,
A. E.
, and
Abdelgaied
,
M.
,
2016
, “
Performance of Novel Solar Dryer
,”
Process Saf. Environ. Prot.
,
102
, pp.
183
189
. 10.1016/j.psep.2016.03.009
2.
Prakash
,
O.
, and
Kumar
,
A.
,
2013
, “
Historical Review and Recent Trends in Solar Drying Systems
,”
Int. J. Green Energy
,
10
(
7
), pp.
690
738
. 10.1080/15435075.2012.727113
3.
Chandrasekar
,
M.
,
Senthil kumar
,
T.
,
Kumaragurubaran
,
B.
, and
Peter Fernandes
,
J.
,
2018
, “
Experimental Investigation on a Solar Dryer Integrated With Condenser Unit of Split air Conditioner (A/C) for Enhancing Drying Rate
,”
Renewable Energy
,
122
, pp.
375
381
. 10.1016/j.renene.2018.01.109
4.
Tomar
,
V.
,
Tiwari
,
G. N.
, and
Norton
,
B.
,
2017
, “
Solar Dryers for Tropical Food Preservation: Thermo Physics of Crops, Systems and Components
,”
Sol. Energy
,
154
, pp.
2
13
. 10.1016/j.solener.2017.05.066
5.
Tiwari
,
S.
, and
Tiwari
,
G. N.
,
2016
, “
Exergoeconomic Analysis of Photovoltaic-Thermal (PVT) Mixed Mode Greenhouse Solar Dryer
,”
Energy
,
114
, pp.
155
164
. 10.1016/j.energy.2016.07.132
6.
Eltawil
,
M. A.
,
Azam
,
M. M.
, and
Alghannam
,
A. O.
,
2018
, “
Energy Analysis of Hybrid Solar Tunnel Dryer With PV System and Solar Collector for Drying Mint (MenthaViridis)
,”
J. Cleaner Prod.
,
181
, pp.
352
364
. 10.1016/j.jclepro.2018.01.229
7.
Yassen
,
T. A.
, and
Al-Kayiem
,
H. H.
,
2016
, “
Experimental Investigation and Evaluation of Hybrid Solar/Thermal Dryer Combined With Supplementary Recovery Dryer
,”
Sol. Energy
,
134
, pp.
284
293
. 10.1016/j.solener.2016.05.011
8.
Farkas
,
I.
,
2013
, “
Integrated Use of Solar Energy for Crop Drying
,”
Drying Technol.
,
31
(
8
), pp.
866
871
. 10.1080/07373937.2013.790410
9.
Amer
,
B. M. A.
,
Hossain
,
M. A.
, and
Gottschalk
,
K.
,
2010
, “
Design and Performance Evaluation of a New Hybrid Solar Dryer for Banana
,”
Energy Convers. Manage.
,
51
(
4
), pp.
813
820
. 10.1016/j.enconman.2009.11.016
10.
Wang
,
W.
,
Li
,
M.
,
Hassanien
,
R. H. E.
,
Wang
,
Y.
, and
Yang
,
L.
,
2018
, “
Thermal Performance of Indirect Forced Convection Solar Dryer and Kinetics Analysis of Mango
,”
Appl. Therm. Eng.
,
134
, pp.
310
321
. 10.1016/j.applthermaleng.2018.01.115
11.
Virbhadra
,
M.
,
Swamia
,
A. T.
, and
Auteeb
,
A. T. R.
,
2018
, “
Experimental Analysis of Solar Fish Dryer Using Phase Change Material
,”
Journal of Energy Storage
,
20
, pp.
310
315
. 10.1016/j.est.2018.09.016
12.
Simate
,
I. N.
,
2001
, “
Simulation of The Mixed-Mode Natural-Convection Solar Drying of Maize
,”
Drying Technol.
,
19
(
6
), pp.
1137
1155
. 10.1081/DRT-100104810
13.
Jha
,
S. K.
,
Bilalovic
,
J.
,
Jha
,
A.
,
Patel
,
N.
, and
Zhang
,
H.
,
2017
, “
Renewable Energy: Present Research and Future Scope of Artificial Intelligence
,”
Renewable Sustainable Energy Rev.
,
77
, pp.
297
317
. 10.1016/j.rser.2017.04.018
14.
Sridharan
,
M.
,
Jayaprakash
,
G.
,
Chandrasekar
,
M.
,
Vigneshwar
,
P.
,
Paramaguru
,
S.
, and
Amarnath
,
K.
,
2018
, “
Prediction of Solar Photovoltaic/Thermal Collector Power Output Using Fuzzy Logic
,”
ASME J. Sol. Energy Eng.
,
140
(
6
), p.
061013
. 10.1115/1.4040757
15.
Sridharan
,
M.
,
2020
, “
Predicting Performance of Double-Pipe Parallel- and Counter-Flow Heat Exchanger Using Fuzzy Logic
,”
ASME J. Thermal Sci. Eng. Appl.
,
12
(
3
), p.
031006
. 10.1115/1.4044696
16.
Sridharan
,
M
,
Devi
,
R.
,
Dharshini
,
C.S.
, and
Bhavadarani
,
M.
,
2019
, “
IoT Based Performance Monitoring and Control in Counter Flow Double Pipe Heat Exchanger
,”
Internet of Things
,
5
, pp.
34
40
. 10.1016/j.iot.2018.11.002
17.
Prakash
,
O.
,
Kumar
,
A.
,
Kaviti
,
A. K.
, and
Kumar
,
P. V.
,
2015
, “
Prediction of the Rate of Moisture Evaporation From Jaggery in Greenhouse Drying Using the Fuzzy Logic
,”
Heat Transfer Res.
,
46
(
10
), pp.
1
6
. 10.1615/HeatTransRes.2014005471
18.
Prakash
,
O.
, and
Kumar
,
A.
,
2014
, “
ANFIS Modelling of a Natural Convection Greenhouse Drying System for Jaggery: An Experimental Validation
,”
Int. J. Sustain. Energy
,
33
(
2
), pp.
316
335
. 10.1080/14786451.2012.724070
19.
Ghritlahre
,
H. K.
, and
Prasad
,
R. K.
,
2018
, “
Application of ANN Technique to Predict the Performance of Solar Collector Systems—A Review
,”
Renewable Sustainable Energy Rev.
,
84
, pp.
75
88
. 10.1016/j.rser.2018.01.001
20.
Ghritlahre
,
H. K.
, and
Prasad
,
R. K.
,
2018
, “
Development of Optimal ANN Model to Estimate the Thermal Performance of Roughened Solar Air Heater Using Two Different Learning Algorithms
,”
Ann. Data Sci.
,
5
(
3
), pp.
453
467
. 10.1007/s40745-018-0146-3
21.
Ghritlahre
,
H. K.
, and
Prasad
,
R. K.
,
2018
, “
Exergetic Performance Prediction of a Roughened Solar Air Heater Using Artificial Neural Network
,”
Stroj. Vestn.-J. Mech. Eng.
,
64
(
3
), pp.
195
206
.
22.
Ghritlahre
,
H. K.
, and
Prasad
,
R. K.
,
2018
, “
Prediction of Exergetic Efficiency of Artificial Arc Shape Roughened Solar Air Heater Using ANN Model
,”
Int. J. Heat Technol.
,
36
(
3
), pp.
1107
1115
. 10.18280/ijht.360343
23.
Prakash
,
O.
, and
Kumar
,
A.
,
2014
, “
Application of Artificial Neural Network for the Prediction of Jaggery Mass During Drying Inside the Natural Convection Greenhouse Dryer
,”
Int. J. Ambient Energy
,
35
(
4
), pp.
186
192
. 10.1080/01430750.2013.793455
24.
Bendu
,
H.
,
Deepak
,
B. B. V. L.
, and
Murugan
,
S.
,
2016
, “
Application of GRNN for the Prediction of Performance and Exhaust Emissions in HCCI Engine Using Ethanol
,”
Energy Convers. Manage.
,
122
, pp.
165
173
. 10.1016/j.enconman.2016.05.061
25.
Bendu
,
H.
,
Deepak
,
B. B. V. L.
, and
Murugan
,
S.
,
2017
, “
Multi-objective Optimization of Ethanol Fuelled HCCI Engine Performance Using Hybrid GRNN–PSO
,”
Appl. Energy
,
187
, pp.
601
611
. 10.1016/j.apenergy.2016.11.072
26.
Ghritlahre
,
H. K.
, and
Prasad
,
R. K.
,
2018
, “
Investigation of Thermal Performance of Unidirectional Flow Porous Bed Solar Air Heater Using MLP, GRNN, and RBF Models of ANN Technique
,”
Thermal Sci. Eng. Prog.
,
6
, pp.
226
235
. 10.1016/j.tsep.2018.04.006
27.
Ghritlahre
,
H. K.
, and
Prasad
,
R. K.
,
2018
, “
Exergetic Performance Prediction of Solar Air Heater Using MLP, GRNN and RBF Models of Artificial Neural Network Technique
,”
J. Environ. Manage.
,
223
, pp.
566
575
. 10.1016/j.jenvman.2018.06.033
You do not currently have access to this content.