The present work establishes an improved experimentally validated analysis to predict performance and exergy-related parameters of a mechanical draft cooling tower involving wooden splash fills. Unlike earlier studies, which accounted for the effect of at most three tower inlet parameters for the exergy analysis, the present study simultaneously considers all five inlet parameters affecting the tower exergy performance. To simultaneously predict outlet air and water conditions, an optimization algorithm involving discrete functions of dry- and wet-bulb temperatures is used in conjunction with the mathematical model derived from mass and energy conservations within the control volume involving Bosnjakovic correlation. From practical point of view, five inlet parameters such as dry-bulb temperature, relative humidity, water temperature, water, and air flow rates are selected for the exergy analysis. Thereafter, the influence of all inlet parameters on the tower performance is analyzed on various important exergy-related factors. The quantitative analysis reveals that the inlet air humidity, water inlet temperature, and the inlet water mass flow rate significantly influence the air and water exergy changes. The present study also reveals that among the five inlet parameters, the water temperature, air humidity, and air mass flow rate are primarily responsible for the exergy destruction. Furthermore, it is observed that the second law efficiency is mainly governed by the inlet air flow rate. The present study is proposed to be useful for selecting the tower inlet parameters to improve exergy performance of mechanical cooling towers.

References

1.
Khan
,
J. R.
, and
Zubair
,
S. M.
,
2001
, “
An Improved Design and Rating Analyses of Counter Flow Wet Cooling Towers
,”
ASME J. Heat Transfer
,
123
(
4
), pp.
770
778
.
2.
Elsarrag
,
E.
,
2006
, “
Experimental Study and Predictions of an Induced Draft Ceramic Tile Packing Cooling Tower
,”
Energy Convers. Manage.
,
47
(
15–16
), pp.
2034
2043
.
3.
Picardo
,
J. R.
, and
Variyar
,
J. E.
,
2012
, “
The Merkel Equation Revisited: A Novel Method to Compute the Packed Height of a Cooling Tower
,”
Energy Convers. Manage.
,
57
, pp.
167
172
.
4.
Majumdar
,
A. K.
,
Singhal
,
A. K.
,
Reilly
,
H. E.
, and
Bartz
,
J. A.
,
1983
, “
Numerical Modeling of Wet Cooling Towers—Part 2: Application to Natural and Mechanical Draft Towers
,”
ASME J. Heat Transfer
,
105
(
4
), pp.
736
743
.
5.
Haseli
,
Y.
,
Dincer
,
I.
, and
Naterer
,
G. F.
,
2008
, “
Exergy Analysis of Condensation of a Binary Mixture With One Noncondensable Component in a Shell and Tube Condenser
,”
ASME J. Heat Transfer
,
130
(
8
), p.
084504
.
6.
Mmohammadiun
,
M.
,
Dashtestani
,
F.
, and
Alizadeh
,
M.
,
2015
, “
Exergy Prediction Model of a Double Pipe Heat Exchanger Using Metal Oxide Nanofluids and Twisted Tape Based on the Artificial Neural Network Approach and Experimental Results
,”
ASME J. Heat Transfer
,
138
(
1
), p.
011801
.
7.
Lee
,
S.
, and
Kim
,
K.
,
2015
, “
Optimization of Printed Circuit Heat Exchanger Using Exergy Analysis
,”
ASME J. Heat Transfer
,
137
(6), p.
064501
.
8.
Alizadeh
,
M.
, and
Sadrameli
,
S. M.
,
2016
, “
Modeling of Thermal Cracking Furnaces Via Exergy Analysis Using Hybrid Artificial Neural Network–Genetic Algorithm
,”
ASME J. Heat Transfer
,
138
(
4
), p.
042801
.
9.
Qureshi
,
B. A.
, and
Zubair
,
S. M.
,
2007
, “
Second-Law-Based Performance Evaluation of Cooling Towers and Evaporative Heat Exchangers
,”
Int. J. Therm. Sci.
,
46
(2), pp.
188
198
.
10.
Muangnoi
,
T.
,
Asvapoositkul
,
W.
, and
Wongwises
,
S.
,
2007
, “
An Exergy Analysis on the Performance of a Counterflow Wet Cooling Tower
,”
Appl. Therm. Eng.
,
27
(5–6), pp.
910
917
.
11.
Muangnoi
,
T.
,
Asvapoositkul
,
W.
, and
Wongwises
,
S.
,
2008
, “
Effects of Inlet Relative Humidity and Inlet Temperature on the Performance of Counterflow Wet Cooling Tower Based on Exergy Analysis
,”
Energy Convers. Manage.
,
49
(
10
), pp.
2795
2800
.
12.
Saravanan
,
M.
,
Saravanan
,
R.
, and
Renganarayanan
,
S.
,
2008
, “
Energy and Exergy Analysis of Counter Flow Wet Cooling Towers
,”
Therm. Sci.
,
12
(
2
), pp.
69
78
.
13.
Wang
,
L.
, and
Li
,
N.
,
2011
, “
Exergy Transfer and Parametric Study of Counter Flow Wet Cooling Towers
,”
Appl. Therm. Eng.
,
31
(
5
), pp.
954
960
.
14.
Merkel
,
F.
,
1925
,
Verdunstungskühlung
,
VDI-Zeitchrift
,
70
, pp. 123–128.
15.
Jaber
,
H.
, and
Webb
,
R. L.
,
1989
, “
Design of Cooling Towers by the Effectiveness-NTU Method
,”
ASME J. Heat Transfer
,
111
(
4
), pp.
837
843
.
16.
Poppe
,
M.
, and
Rögener
,
H.
,
1991
, “
Berechnung Von Rückkühlwerken
,”
VDI-Wärmeatlas
,
Springer
,
Berlin
, pp.
Mi1
Mi15
.
17.
El-Dessouky
,
H.
,
1993
, “
Thermal and Hydraulic Performance of a Three-Phase Fluidized Bed Cooling Tower
,”
Exp. Therm. Fluid Sci.
,
6
(
4
), pp.
417
426
.
18.
Soylemez
,
M. S.
,
1999
, “
Theoretical and Experimental Analyses of Cooling Towers
,”
ASHRAE Trans.
,
105
, p.
330
.https://search.proquest.com/openview/1cee7fd50b21c5600fe1b0d75fe5c5ac/1?pq-origsite=gscholar&cbl=34619
19.
Halasz
,
B.
,
1999
, “
Application of a General Non-Dimensional Mathematical Model to Cooling Towers
,”
Int. J. Therm. Sci.
,
38
(
1
), pp.
75
88
.
20.
Stabat
,
P.
, and
Marchio
,
D.
,
2004
, “
Simplified Model for Indirect-Contact Evaporative Cooling-Tower Behaviour
,”
Appl. Energy
,
78
(
4
), pp.
433
451
.
21.
Kloppers
,
J. C.
,
2003
,
A Critical Evaluation and Refinement of the Performance Prediction of Wet-Cooling Towers
,
University of Stellenbosch
,
Stellenbosch, South Africa
.
22.
Jin
,
G. Y.
,
Cai
,
W. J.
,
Lu
,
L.
,
Lee
,
E. L.
, and
Chiang
,
A.
,
2007
, “
A Simplified Modeling of Mechanical Cooling Tower for Control and Optimization of HVAC Systems
,”
Energy Convers. Manage.
,
48
(
2
), pp.
355
365
.
23.
Hosoz
,
M.
,
Ertunc
,
H. M.
, and
Bulgurcu
,
H.
,
2007
, “
Performance Prediction of a Cooling Tower Using Artificial Neural Network
,”
Energy Convers. Manage.
,
48
(
4
), pp.
1349
1359
.
24.
Qi
,
X.
,
Liu
,
Z.
, and
Li
,
D.
,
2008
, “
Numerical Simulation of Shower Cooling Tower Based on Artificial Neural Network
,”
Energy Convers. Manage.
,
49
(
4
), pp.
724
732
.
25.
Heidarinejad
,
G.
,
Karami
,
M.
, and
Delfani
,
S.
,
2009
, “
Numerical Simulation of Counter-Flow Wet-Cooling Towers
,”
Int. J. Refrig.
,
32
(
5
), pp.
996
1002
.
26.
Pan
,
T. H.
,
Shieh
,
S. S.
,
Jang
,
S. S.
,
Tseng
,
W. H.
,
Wu
,
C. W.
, and
Ou
,
J. J.
,
2011
, “
Statistical Multi-Model Approach for Performance Assessment of Cooling Tower
,”
Energy Convers. Manage.
,
52
(
2
), pp.
1377
1385
.
27.
Asvapoositkul
,
W.
, and
Treeutok
,
S.
,
2012
, “
A Simplified Method on Thermal Performance Capacity Evaluation of Counter Flow Cooling Tower
,”
Appl. Therm. Eng.
,
38
, pp.
160
167
.
28.
Wang
,
J.
,
Shieh
,
S.
,
Jang
,
S.
, and
Wu
,
C.
,
2013
, “
Discrete Model-Based Operation of Cooling Tower Based on Statistical Analysis
,”
Energy Convers. Manage.
,
73
, pp.
226
233
.
29.
Mansour
,
M. K.
, and
Hassab
,
M. A.
,
2014
, “
Innovative Correlation for Calculating Thermal Performance of Counter Flow Wet-Cooling Tower
,”
Energy
,
74
, pp.
855
862
.
30.
Chang
,
C.
,
Shieh
,
S.
,
Jang
,
S.
,
Wu
,
C.
, and
Tsou
,
Y.
,
2015
, “
Energy Conservation Improvement and On–Off Switch Times Reduction for an Existing VFD-Fan-Based Cooling Tower
,”
Appl. Energy
,
154
, pp.
491
499
.
31.
Deb
,
K.
,
2001
,
Multi-Objective Optimization Using Evolutionary Algorithms
,
Wiley
,
New York
.
32.
Roy
,
R. K.
,
2001
,
Design of Experiments Using the Taguchi Approach: 16 Steps to Product and Process Improvement
,
Wiley
,
New York
.
33.
Kloppers
,
J. C.
, and
Kröger
,
D. G.
,
2005
, “
A Critical Investigation Into the Heat and Mass Transfer Analysis of Counterflow Wet-Cooling Towers
,”
Int. J. Heat Mass Transfer
,
48
(3–4), pp.
765
777
.
34.
Bosnjakovic
,
F.
,
1965
,
Technical Thermodynamics
,
Holt, Rinehart and Winston
,
New York
.
35.
Singh
,
K.
, and
Das
,
R.
,
2017
, “
Exergy Optimization of Cooling Tower for HGSHP and HVAC Applications
,”
Energy Convers. Manage.
,
136
, pp.
418
430
.
36.
MathWorks
,
2016
, “
fminunc Unconstrained Minimization
,” The MathWorks Inc., Natick, MA, accessed Sept. 5, 2016, http://in.mathworks.com/help/optim/ug/fminunc-unconstrained-minimization.html
37.
Singla
,
R. K.
,
Singh
,
K.
, and
Das
,
R.
,
2016
, “
Tower Characteristics Correlation and Parameter Retrieval in Wet-Cooling Tower With Expanded Wire Mesh Packing
,”
Appl. Therm. Eng.
,
96
, pp.
240
249
.
38.
Minitab,
2016
, “
Taguchi Designs
,” Minitab, Inc., Coventry, UK, accessed Aug. 20, 2016, http://support.minitab.com/en-us/minitab/17/topic-library/modeling-statistics/doe/taguchi-designs/taguchi-designs/
39.
Wark
,
K.
,
1995
,
Advanced Thermodynamics for Engineers
,
McGraw-Hill
,
New York
.
40.
Shukuya
,
M.
, and
Hammache
,
A.
,
2002
, “
Introduction to the Concept of Exergy for a Better Understanding of Low-Temperature Heating and High-Temperature-Cooling Systems
,” Low Exergy Systems for Heating and Cooling of Buildings, Espoo, Finland,
IEA Annex 37
, pp.
41
44.
https://www.researchgate.net/publication/286672815_Introduction_to_the_concept_of_exergy_-_For_a_better_understanding_of_low-temperature-heating_and_high-temperature-cooling_systems
41.
Moffat
,
R. J.
,
1982
, “
Contributions to the Theory of Single-Sample Uncertainty Analysis
,”
ASME J. Fluids Eng.
,
104
(
2
), pp.
250
258
.
42.
Dieck
,
R. H.
,
2007
,
Measurement Uncertainty: Methods and Applications
,
ISA, Research Triangle Park
,
NC
.
43.
Gupta
,
S. V.
,
2012
,
Measurement Uncertainties: Physical Parameters and Calibration of Instruments
,
Springer-Verlag
,
Berlin
.
You do not currently have access to this content.