Ele engineering HEAT AND MASS TRANSFER .pptx

Electrical Engineering

1. 1 Study and Analysis of Experimental and Mathematical Models for Heat and Mass Transfer Process in Counter Flow Cooling Tower THESIS Submitted to Shri Venkateshwara University, Gajraula , U.P. for the registration of the topic for Doctor of Philosophy in Mechanical Engineering SUPERVISED BY: Dr.Ch . V.S. Parameshware Rao Professor, Department of Mechanical Engineering S.V . University Gajraula (UP). SUBMITTED BY: Basavaprabhu Ayyanna Hiremath Research Scholar Dept. of Mechanical Engg. S.V . University Gajraula (UP). DEPARTMENT OF MACHNICAL ENGINEERING SHRI VENKATESHWARA UNIVERSITYGAJRAULA, DIST. J.P. NAGAR (U.P.) 2011

2. INTRODUCTION Cooling towers originated in the 19th century through the development of condensers for use with the steam engine. Condensers use relatively cool water to condense the steam coming out of the cylinders or turbines. However the condensers require an ample supply of cooling water, without which they are impractical. The consumption of cooling water by inland processing and power plants is estimated to reduce power availability for the majority of thermal power plants by 2040–2069.  By the turn of the 20th century, several evaporative methods of recycling cooling water were in use in areas lacking an established water supply. In urban locations where municipal water mains may not be of sufficient supply, in areas with limited land, such as in cities are innovated and used. 2 Dept. of Mech. Engg. S.V. University, Gajraula , UP.

3. COMPONENTS OF COOLING TOWER Frame and casing Cold water basin Drift eliminators Air inlet Louvers Nozzles Fans 3 Dept. of Mech. Engg. S.V. University, Gajraula , UP.

4. TOWER MATERIALS WOOD CONCRETE GALVANIZED STEEL STAINLESS STEEL GLASS FIBER PVC POLYPROPYLENE POLYMERS 4 Dept. of Mech. Engg. S.V. University, Gajraula , UP.

5. TERMINOLOGY Blow-out   Plume   Draw-off  or  Blow-down   Zero bleed for cooling towers Make-up   Noise Range Approach Full-Flow Filtration   5 Dept. of Mech. Engg. S.V. University, Gajraula , UP. Blow-out   Side-Stream Filtration Cycle of concentration  Treated timber   Leaching   Pultruded FRP   

6. CLASSIFICATION OF COOLING TOWERS Classification on Type of Air Induced Natural draft Mechanical draught   Induced draught   Fan assisted natural draught   Forced draught   6 Dept. of Mech. Engg. S.V. University, Gajraula , UP.

7. CLASSIFICATION OF COOLING TOWERS Classification on Type of Heat Transfer Mechanism Dry cooling towers   Wet cooling towers or open circuit cooling towers Fluid coolers  or  closed circuit cooling towers 7 Dept. of Mech. Engg. S.V. University, Gajraula , UP.

8. CLASSIFICATION OF COOLING TOWERS Categorization by air-to-water flow Cross flow 8 Dept. of Mech. Engg. S.V. University, Gajraula , UP. Figure 1.2: A cross flow design

9. CLASSIFICATION OF COOLING TOWERS Categorization by air-to-water flow Counter flow 9 Dept. of Mech. Engg. S.V. University, Gajraula , UP. Figure 1.3: A counter flow design

10. CLASSIFICATION OF COOLING TOWERS Classification by Use HVAC Industrial cooling towers Classification by Built Package cooling tower Field erection type 10 Dept. of Mech. Engg. S.V. University, Gajraula , UP.

11. WORKING PRINCIPLE 11 Dept. of Mech. Engg. S.V. University, Gajraula , UP. COOLING TOWER Warm condenser water Ambient Air Warmer, More Humid Cold condenser water Figure 1.4: Working principle of cooling tower

12. THEORY OF COOLING TOWER The theory behind the operation of the cooling tower is the First Law of Thermodynamics, which is the conservation of energy. In simpler terms, the energy that enters the system must exit the system; energy can neither be created nor destroyed, just transformed from one form to another 12 Dept. of Mech. Engg. S.V. University, Gajraula , UP.

13. WET COOLING TOWER MATERIAL BALANCE S.V. University, Gajraula , UP. 13 Dept. of Mech. Engg. Figure 1.5: Fan-induced draft, counter-flow cooling tower

14. COOLING TOWER PERFORMANCE S.V. University, Gajraula , UP. 14 Dept. of Mech. Engg. Cooling tower effectiveness Cooling capacity Evaporation loss Blow down losses Liquid/Gas (L/G) ratio

15. FACTORS AFFECTING COOLING TOWER PERFORMANCE S.V. University, Gajraula , UP. 15 Dept. of Mech. Engg. Capacity Range Heat Load Wet Bulb Temperature Fill Media Effects

16. EFFICIENT SYSTEM OPERATION S.V. University, Gajraula , UP. 16 Dept. of Mech. Engg. Cooling Water Treatment Drift Loss in the Cooling Towers Cooling Tower Fans Operation in freezing weather Fire hazard Structural stability

17. LITERATURE REVIEW S.V. University, Gajraula , UP. 17 Dept. of Mech. Engg. The cooling tower is a steady flow device that uses a combination of mass and energy transfer to cool water by exposing it as an extended surface to the atmosphere. To predict and/or evaluate the cooling tower performance, many numerical and analytical models have been developed on the basis of heat and mass transfer theory with suitable assumptions.

18. NUMERICAL AND ANALYTICAL MODELS S.V. University, Gajraula , UP. 18 Dept. of Mech. Engg. Merkel’s model Baker and Shryock Braun’s model Model of Klimanek&Bia_lecky

19. Problem Formulation S.V. University, Gajraula , UP. 19 Dept. of Mech. Engg. There are many factors which affect the performance of counter flow cooling tower like water flow rate, air flow rate, fill porosity, water to air ratio, fouling, heat leakage in the atmosphere. This study presents an experimental investigation of effectiveness of the cooling tower with the effect of fill porosity. We have used three different fill porosity models in ANSYS to find out outlet temperature of hot water. Also we use TAGUCHI method for optimization of cooling tower performance affected parameters with the help of portable Minitab software.

20. Objectives S.V. University, Gajraula , UP. 20 Dept. of Mech. Engg. To observe the effects of process variables on the outlet temperature of the water. Modeling of cooling tower Effectiveness calculation CFD analysis in ANSYS Experimental reading for validation of ANSYS result Optimization of effectiveness by Taguchi method. The effects of range and approach temperatures will be established from CFD analysis. To identify the conditions that make alternative capacity control methods for cooling towers cost effective. The optimization of tower based free cooling systems for energy consumption.

21. ANALYSIS OF COUNTER COOLING TOWER MODEL S.V. University, Gajraula , UP. 21 Dept. of Mech. Engg. Simplified Calculation Method for Performance Analysis As one of the most widely used units in water cooling systems, the closed wet cooling towers (CWCTs) have two typical counter-flow constructions, in which the spray water flows from the top to the bottom, and the moist air and cooling water flow in the opposite direction vertically (parallel) or horizontally (cross), respectively. A simplified calculation method for conveniently and accurately analyzing the thermal performance of the two types of counter-flow CWCTs, viz. the parallel counter-flow CWCT (PCFCWCT) and the cross counter flow CWCT (CCFCWCT).

22. Parallel counter-flow (PCFCWCT) S.V. University, Gajraula , UP. 22 Dept. of Mech. Engg. Spray water inlet Spray water outlet Process water inlet Circulating water pump Air inlet Process water outlet

23. Cross counter-flow (CCFCWCT). S.V. University, Gajraula , UP. 23 Dept. of Mech. Engg. Spray water inlet Process water inlet Pump Air inlet Process water outlet Spray water outlet

24. ASSUMPTIONS AND SIMPLIFICATIONS S.V. University, Gajraula , UP. 24 Dept. of Mech. Engg. The major assumptions to derive the simplified modeling equations are The heat and mass transfer processes occur under steady-state conditions Water and air specific heats are constant Heat transfer from the tower fans to air or water streams is negligible, The spray water flow rate is sufficient to wet all surfaces of the tubes Process water and air are in counter-flow

25. Air enthalpy vs. wet-bulb temperature S.V. University, Gajraula , UP. 25 Dept. of Mech. Engg.

26. Water dynamic viscosity coefficient vs. water temperature. S.V. University, Gajraula , UP. 26 Dept. of Mech. Engg.

27. HEAT AND MASS TRANSFER IN WET-COOLING TOWERS S.V. University, Gajraula , UP. 27 Dept. of Mech. Engg. Control Volume for Derivation of Governing Equations for Counter flow Fill

28. COOLING TOWER COEFFICIENTS S.V. University, Gajraula , UP. 28 Dept. of Mech. Engg. Required coefficient Enthalpy Per Dry air BTU 140 120 100 80 60 40 20 20 40 60 80 120 140 Temperature °F D0 D1 D2 D3 A B C T2 T1 Counterflow cooling graph for constant conditions, variable L/G rates

29. Cooling tower characteristic, KaV /L versus L /G. S.V. University, Gajraula , UP. 29 Dept. of Mech. Engg. Enthalpy Per Dry air BTU 1.0 0.6 Temperature °F B C (Available Co-efficient) 0.8 0.6 0.8 1.0 2.0 (Required Co-efficient) Normal Air rate +25 -25 Correlation Line

30. Cooling tower characteristic, KaV /L versus gpm /sq ft. S.V. University, Gajraula , UP. 30 Dept. of Mech. Engg. Enthalpy Per Dry air BTU 1.0 0.6 Temperature °F Correlation Line (Available Co-efficient) 0.8 0.6 0.8 1.0 2.0 (Required Co-efficient) Normal Air rate +25 -25

31. Heat Balance Corrections S.V. University, Gajraula , UP. 31 Dept. of Mech. Engg. Range Example I No Modification NTU Example II Equation (3.17), Constant L/GNTU Example IIEquation (16a), Variable L/G NTU L/G 1 0.1048 0.1051 0.1046 1.1633 2 0.2106 0.2115 0.2105 1.1641 3 0.3171 0.3192 0.3170 1.1649 4 0.4246 0.4279 0.4245 1.1658 5 0.5317 0.5372 0.5317 1.1666 10 1.0531 1.0762 1.0564 1.1710 15 1.5294 1.5770 1.5387 1.1759 20 1.9350 2.0080 1.9523 1.1802 25 2.2631 2.9577 2.2886 1.1850 30 2.5203 2.6315 2.5533 1.1899 35 2.7244 2.8422 2.7581 1.1949 40 2.8775 3.0037 2.9159 1.2000

32. Example of counterflow calculation of NTU for 80°F cold-water temperature, 70°F entering wet-bulb temperature and L/G 1.20. S.V. University, Gajraula , UP. 32 Dept. of Mech. Engg. 1 2 3 4 5 6 7 8 9 Water Temperaturet Enthalpyat th ' Enthalpyof airh Enthalphydifference (h' – h) I (h' – h) I (h' – h) dt (h' – h) Dt (h' – h) Range°F 80 43.69 34.09 9.60 .1043 .1049 .1049 .1049 1 81 44.78 35.29 9.49 .1055 .1059 .1059 .2108 2 82 45.90 36.49 9.41 .1067 .1067 .1067 .3175 3 83 47.04 37.69 9.35 .1070 .1071 .1071 .4246 4 84 48.20 38.83 9.33 .1072 .1072 .1072 .5318 5 85 49.43 40.09 9.34 .1071 .1043 .5215 1.0533 10 90 55.93 46.09 9.84 .1016 .0996 .4980 1.5513 15 95 63.32 52.09 10.23 .0977 .0856 .4280 1.9793 20 100 71.73 58.09 13.64 .0734 .0657 .3285 2.3078 25 105 81.34 64.09 17.25 .0580

33. True versus Apparent Potential S.V. University, Gajraula , UP. 33 Dept. of Mech. Engg. Enthalpy Per Dry air BTU 140 120 100 80 60 40 20 20 40 60 80 120 140 Temperature °F D0 h”-h Bt@h T’-t B’ T”@h h”-h

34. True versus Apparent Potential S.V. University, Gajraula , UP. 34 Dept. of Mech. Engg. Enthalpy Per Dry air BTU 140 120 100 80 60 40 20 20 40 60 80 120 140 Temperature °F D0 h”-h Bt@h T’-t B’ T”@h

35. NUMERICAL STUDY OF PERFORMANCE OF CFCT S.V. University, Gajraula , UP. 35 Dept. of Mech. Engg. In the numerical investigation, the two dimensional CFD model with finite volume scheme has utilized the standard (k-e) turbulence model to computes the air properties, while one-dimensional model is used to get the water properties. A forced draft counter water flow cooling tower will be employed. A theoretical and experimental study will be carried out on this tower. In theoretical part a thermal solution (heat and mass balance) will be used to solve the governing equations using finite volume method based on computational fluid dynamics (CFD)

36. MATHEMATICAL MODEL S.V. University, Gajraula , UP. 36 Dept. of Mech. Engg. The mathematical model of transport processes of cooling tower is steady state, two dimension, turbulent and incompressible flows. The mass, momentum and energy conservation equations of air are given in rectangular coordinate system ( x,y ) and in two dimensions, while the mass and energy conservation equations of water are given only in water stream direction (y-direction). The cooling tower is a forced draft counter flow type and square cross section area, in which air passes upward through a falling spray of water.

37. GEOMETRIC SHAPE OF THE WATER COOLING TOWER S.V. University, Gajraula , UP. 37 Dept. of Mech. Engg.

38. VARIOUS EMPIRICAL CONSTANTS APPEARING IN THE (K-Ɛ) EQUATIONS S.V. University, Gajraula , UP. 38 Dept. of Mech. Engg. Equation f T f S f The continuity equation of air 1 m v ”’ The continuity equation of water 1 - m v ”’ The momentum equation of air in X-direction U u eff ∂( u eff ∂u ) + ∂( u eff ∂u ) - ∂p - f x ∂x ∂ x∂y ∂x ∂x The momentum equation of air in Y-direction V u eff ∂( u eff ∂u ) + ∂( u eff ∂u ) - ∂p - ∂y ∂ y∂x ∂y ∂x f y - (p- p amb ) g The energy equation of air h a T eff q’’’ The energy equation of water h w - q’’’ The moisture equation of air w a T eff m v ”’ The turbulent kinetic energy equation K T k G- p e The rate of dissipation equation e T e C 1 e e G – C 2 e p e k k

39. EXPERIMENTAL COMPUTATION PROCEDURE S.V. University, Gajraula , UP. 39 Dept. of Mech. Engg. Ka.V = C w ∫ tw2 dt w (3.47) L tw1 h sw - h a Ka.V = t w2 – t w1 L h m   Ka.V = l L -n L G Ka.V = 0.183 L -0.624 L G Ka.V = 0.3807 L -0.762 L G

40. NUMERICAL COMPUTATION PROCEDURE S.V. University, Gajraula , UP. 40 Dept. of Mech. Engg. Ka.V = C w ∫ tw2 dt w (3.47) L tw1 h sw - h a Ka.V = t w2 – t w1 L h m   Ka.V = l L -n L G Ka.V = 0.183 L -0.624 L G Ka.V = 0.3807 L -0.762 L G

41. NUMERICAL COMPUTATION PROCEDURE S.V. University, Gajraula , UP. 41 Dept. of Mech. Engg. A staggered grid system was followed. In this system the scalar quantities are located at the intersection of grid nodes and velocities are located at the boundaries of the control volumes of scalar quantities and the domain of tower is divided into main nodes which are (21 x21). The resulting equation after discretizationis : f p = a w f w + a E f E + a N f N + a S f S + S SC a w + a E + a N +a S -S fp   Where stands for any depended variable such as u ,v , h a , h w ,w a ,k , ɛ ; and link a w , a E , a N and a S coefficients express the effects of convection and diffusion between the grid point, P, and its neighboring grid nodes in East, West, North and South directions, respectively.S SC and S fp are components of source term, S f , which is linearized as: S f= S fp + S SC.  

42. CONCLUSIONS S.V. University, Gajraula , UP. 42 Dept. of Mech. Engg. The objective of this paper is to describe methods that will give a satisfactory answer, without regard to the effort needed. A method that does not provide an acceptable degree of accuracy is all but worthless, regardless of how easy it may be. The limits of acceptability and the effort to be expended will be up to the individual, and each will obviously seek the easiest means of attaining the desired end .

43. METHODOLOGY OF NUMERICAL SIMULATIONS S.V. University, Gajraula , UP. 43 Dept. of Mech. Engg. For effectiveness parameters like inlet water flow rate, inlet air rate and fill porosity are considering in this research. Optimization of heat transfer is done by using Taguchi method. For this model of cooling tower is made in solid works then convert this model in STEP file and imported in ANSYS for CFD analysis. In this paper present the CFD analysis of cooling tower and comparing this result to practical reading. For taking practical reading temperature sensor and thermocouple is used.

44. CFD ANALYSIS OF COOLING TOWER S.V. University, Gajraula , UP. 44 Dept. of Mech. Engg. Here ANSYS workbench is used for CFD analysis of cooling tower. Application of the CFD to analyze a fluid problem requires the following steps. First, the mathematical equations describing the fluid flow are written. These are usually a set of partial differential equations. These equations are then discredited to produce a numerical analogue of the equations. The domain is then divided into small grids or elements. Finally, the initial conditions and the boundary conditions of the specific problem are used to solve these equations. All CFD codes contain three main elements: (1) A preprocessor (2) A flow solver (3) A post-processor,

45. PRE PROCESSOR S.V. University, Gajraula , UP. 45 Dept. of Mech. Engg. A Preprocessor is used to input the problem geometry, generate the grid, and define the flow parameter and the boundary conditions to the code.Here current work pre-processor is divided in three parts 1) Prepare modal of cooling tower. 2) Meshing 3) Material Property and Boundary condition.

46. PRE PROCESSOR S.V. University, Gajraula , UP. 46 Dept. of Mech. Engg. A Preprocessor is used to input the problem geometry, generate the grid, and define the flow parameter and the boundary conditions to the code.Here current work pre-processor is divided in three parts 1) Prepare modal of cooling tower. 2) Meshing 3) Material Property and Boundary condition.

47. PREPARE SOLID MODEL COOLING TOWER S.V. University, Gajraula , UP. 47 Dept. of Mech. Engg. Here Cavity model of cooling tower made in Creo is converting in to STEP file and imported in ANSYS workbench. Cavity model of cooling tower in ANSYS workbench is imported.

48. FOLLOWING MODELING EXPERIMENTS ON ANSYS CARRIED OUT. S.V. University, Gajraula , UP. 48 Dept. of Mech. Engg. 1 . Meshing of cooling tower. 2. Domain for water and Air Mixture. 3. Domain for water. 4. Porous Domain. 5. Porosity. 6. Water and Air domain. 7. Inlet for water. 8. Outlet for water. 9. Inlet for Air. 10. Outlet for Air

49. COOLING TOWER WIRE FRAME CAVITY MODEL S.V. University, Gajraula , UP. 49 Dept. of Mech. Engg.

50. ASSEMBLY OF COOLING TOWER   S.V. University, Gajraula , UP. 50 Dept. of Mech. Engg.

51. Meshing modal of cooling Tower   S.V. University, Gajraula , UP. 51 Dept. of Mech. Engg.

52. Short representation of the structured grid   S.V. University, Gajraula , UP. 52 Dept. of Mech. Engg. Numbers of node and element use for meshing Domain Nodes Element All domain 243832 727631

53. WATER AND AIR MIXTURE DOMAIN   S.V. University, Gajraula , UP. 53 Dept. of Mech. Engg.

54. Domain for water   S.V. University, Gajraula , UP. 54 Dept. of Mech. Engg.

55. Define Porous Domain   S.V. University, Gajraula , UP. 55 Dept. of Mech. Engg.

56. Define Porosity   S.V. University, Gajraula , UP. 56 Dept. of Mech. Engg.

57. Domain for inlet water   S.V. University, Gajraula , UP. 57 Dept. of Mech. Engg.

58. Define outlet for Water   S.V. University, Gajraula , UP. 58 Dept. of Mech. Engg.

59. DEFINE INLET FOR AIR  S.V. University, Gajraula , UP. 59 Dept. of Mech. Engg.

60. DEFINE OUTLET FOR AIR S.V. University, Gajraula , UP. 60 Dept. of Mech. Engg.

61. THE PROPERTIES OF MATERIALS USE FOR THIS WORK S.V. University, Gajraula , UP. 61 Dept. of Mech. Engg. S. No. Input Conditions Values 1 Inlet temperature of hot water 315 K 2 Inlet velocity of air 3.2 m/s 3 Inlet air pressure 1.013 bar 4 Inlet air pressure 303 K 5 Fills Porosity 55 % 6 Hot water flow rate 1.37 kg/s

62. COMPARISON BETWEEN PRACTICAL READING AND CFD ANALYSIS   S.V. University, Gajraula , UP. 62 Dept. of Mech. Engg. Inlet Hot Water Temperature Outlet Hot Water Temperature Practical Reading 315 k 308K ANSYS Result 315k 307.4K

63. SOLVER   S.V. University, Gajraula , UP. 63 Dept. of Mech. Engg. A flow solver, which is used to solve the governing equations of the flow subject to the conditions provided. There are four different methods used as a flow solver: ( i ) finite difference method (ii) finite element method (iii) finite volume method, and (iv) spectral method. This phase of FEA is automatically chosen by ANSYS no user interfere is needed. Here 100 iteration is run by ANSYS for solve current problem.

64. POST PROCESSOR   S.V. University, Gajraula , UP. 64 Dept. of Mech. Engg. A select set of results is available for the user to view and interrogate. The set of results include fundamental stress, strain, deformation, thermal, shape optimization, mode shape, and fatigue. INTERROGATION INVOLVES SEVERAL OPTIONS : Scoping Slicing Deformed Shape

65. Design and analysis of experiments   S.V. University, Gajraula , UP. 65 Dept. of Mech. Engg. Design of experiment is a powerful statistical method for determining the unknown properties of the operating parameters in the experiment process and for analyzing and modeling the interaction among the factors . The classical experimental design methods are too complex and not easy to use. Additionally, a large numbers of experiments have to be carried out when the number of operating parameters increases. Therefore, the factors causing variations should be determined and checked under laboratory conditions. These studies are considered under the scope of off-line quality improvement.

66. TAGUCHI’S METHOD   S.V. University, Gajraula , UP. 66 Dept. of Mech. Engg. In early 1950’s, Dr. Genichi Taguchi , “The Father of Quality Engineering,” introduced the concept of off-line quality control techniques known as Taguchi parameter design. Offline quality control are those activities which were performed during the Product (or Process) Design and Development phase. In this research work, Taguchi’s method is used for improving the effectiveness in the cooling tower

67. PROCEDURE OF TAGUCHI DESIGN METHODOLOGY   S.V. University, Gajraula , UP. 67 Dept. of Mech. Engg. 1.Problem Recognition and Formulation 2. Select quality characteristics 3. Select design or process parameters 4. Classify design parameters 5. Determine level 6. Identify Interactions 7. Choose appropriate orthogonal array 8. Conduct experiments 9. Perform statistical analysis 10. Perform a confirmatory experiment and Implement results

68. Process parameter with their ranges and values at three levels   S.V. University, Gajraula , UP. Dept. of Mech. Engg. Sl No Parameters Designation Process Parameters Range Level Low Medium high 1 A Water Flow (kg/hr) 100-200 100 150 200 2 B Air Flow (kg/hr) 100-200 100 150 200 3 C Water Temp (°C) 40-48 40 44 48 68

69. SELECTION OF ORTHOGONAL ARRAY (OA) S.V. University, Gajraula , UP. Dept. of Mech. Engg. The methodology minimizes performance or quality problems arising due to non-identical operating or environmental conditions using a simplified method known as orthogonal array experiment that helps to conduct a multifactor experiment towards establishing the best product or process design. 69

70. THE MAJOR CHARACTERISTICS OF ORTHOGONAL ARRAYS S.V. University, Gajraula , UP. Dept. of Mech. Engg. 1. Orthogonal arrays (OA) are special matrix experiments that allow the experimenter to study the main factor effects of several design parameters at once and efficiently.  2. OA is a valid representation of the cause-effect relationship of the process under study.  3. The crux lies in choosing the level combinations of the input design variable for each experiment.  4. The total number of rows in an OA determines the total number of experiments to be run in the investigation.  5. In any pair of columns in an OA, all combinations of the treatments occur in equal number of times.  70

71. THE MAJOR CHARACTERISTICS OF ORTHOGONAL ARRAYS…………….continued S.V. University, Gajraula , UP. Dept. of Mech. Engg. 6. Any treatment pair occurs once and only once between the two column, a property known as balancing property.  7. Any two columns of OA are mutually orthogonal.  8. The experiments guided by an OA may not use all columns but it must use every one of the array.  9. Orthogonality implies that the entries in the array satisfy a special mathematical condition. 10. Within the OA, further a sub-set can be analysed for a particular combination.   11. Use of OAs to plan matrix experiments also ensures that if errors in each experiment are independent and have zero mean and equal variance and the estimated factor effects are mutually uncorrelated. 71

72. TAGUCHI ORTHOGONAL ARRAY S.V. University, Gajraula , UP. Dept. of Mech. Engg. 72 Run Factors Response A B C (A) (B) Curved(20mm) Packing Curved(100mm) Packing Triangular Packing Run 1 Run 2 Run 3 Run 1 Run 2 Run 3 Run 1 Run 2 Run 3 WF AF WT WF AF WT 1 1 1 1 100 100 40 64.10 61.23 60.27 59.02 58.25 57.34 55.24 53.45 57.43 2 1 2 2 100 150 44 73.15 69.45 74.62 68.18 69.27 67.25 62.43 65.52 67.26 3 1 3 3 100 200 48 74.25 70.24 76.21 69.24 70.38 68.24 68.45 65.87 67.15 4 2 1 2 150 100 44 55.05 53.34 57.32 50.07 52.15 51.76 45.66 49.41 48.47 5 2 2 3 150 150 48 62.30 58.65 62.21 57.29 59.47 58.44 51.27 49.67 54.64 6 2 3 1 150 200 40 63.00 57.76 61.54 58.03 57.25 59.25 56.96 52.74 54.89 7 3 1 3 200 100 48 51.00 47.56 50.43 46.34 45.53 47.23 42.44 44.67 43.02 8 3 2 1 200 150 40 54.00 50.84 52.87 49.28 47.58 48.56 47.68 46.54 48.84 9 3 3 2 200 200 44 61.00 55.54 60.69 56.48 55.68 54.23 51.52 53.23 52.89

73. A linear graph of L27 - an Orthogonal array [OA] S.V. University, Gajraula , UP. Dept. of Mech. Engg. 73 3.4 6.7 8.11 WF AFTW

74. EXPERIMENTAL SETUP FORCED DRAFT COOLING TOWER S.V. University, Gajraula , UP. Dept. of Mech. Engg. 74

75. Line diagram of the Experimental Setup of Forced Draft cooling tower S.V. University, Gajraula , UP. Dept. of Mech. Engg. 75 3 4 113 1 5 133 15 6 7 9 8 103 143 12

76. Bottle Type Cooling Tower S.V. University, Gajraula , UP. Dept. of Mech. Engg. 76

77. S.V. University, Gajraula , UP. Dept. of Mech. Engg. 77 Steps of Making Bottle

78. S.V. University, Gajraula , UP. Dept. of Mech. Engg. 78 Packing Material Photographic picture of the ceramic pacing

79. S.V. University, Gajraula , UP. Dept. of Mech. Engg. 79 Blower A blower is used to supply outside air to the cooling tower. An orifice meter is provided for air flow measurement. U-tube manometer is used which is filled with mercury. The pressure drop at fill zone is measured by U-tube manometer. A (1 Hp) controlled centrifugal blower with plastic vane is used to supply variable flow air to cool the tower.

80. S.V. University, Gajraula , UP. Dept. of Mech. Engg. 80 Measuring Tank At the bottom a cylindrical measuring tank is provided so that water flow rate at the outlet can be calculated. Hence evaporation loss can also be calculated by just subtracting the outlet water flow rate from inlet water flow rate.

81. S.V. University, Gajraula , UP. Dept. of Mech. Engg. 81 Control Panel The control panel is consisted of a) A rotameter for water flow measurement, b) U tube manometer for air flow measurement

82. S.V. University, Gajraula , UP. Dept. of Mech. Engg. 82 Psychrometer This instrument was used to measure the amount of moisture in the air. It consists of two thermometers. One thermometer measures the dry air temperature while the other one measures the wet-bulb temperature. After the wick of the wet-bulb thermometer is dipped in water, the psychrometer is whirled around using the handle.

83. S.V. University, Gajraula , UP. Dept. of Mech. Engg. 83 Anemometer This instrument was used to measure the velocity of the air exiting the cooling tower fans. The Anemometer (smart sensor AR826) is used to measure the air inlet velocity in the cooling tower with range0-45m/ secandan accuracy ± 3%. A make up water tank is provided to measure the water level losses due to evaporation in the cooling tower.

84. S.V. University, Gajraula , UP. Dept. of Mech. Engg. 84 Psychrometeric Chart Enthalpy of air at inlet temperature (Ha1) 94 KJ/Kg Enthalpy of air at outlet temperature (Ha2) 118 KJ/Kg Specific Humidity of air at inlet temperature (W1) 0.024 Kg/Kg of air Specific Humidity of air at outlet temperature (W2) 0.03 Kg/Kg of air Specific Volume of air at inlet temperature (Vs1) 0.908 m3/Kg Specific Volume of air at outlet temperature (Vs2) 0.94 m3/Kg Enthalpy of water at inlet temperature (Hw1) 163.3 KJ/Kg Enthalpy of water at outlet temperature (Hw2) 138.2 KJ/Kg

85. S.V. University, Gajraula , UP. Dept. of Mech. Engg. 85 Cooling Tower Operating Parameters and Range Parameter Range Water flow(kg/hr) 100-200 Air flow(kg/hr) 100-200 Inlet water temp (˚C) 40-48

86. S.V. University, Gajraula , UP. Dept. of Mech. Engg. 86 Experimental procedure and observation: Water is allowed to circulate through the cooling tower when the heaters switch on until the temperature reaches a specified value. Different water temperatures are maintained by increasing or decreasing the heat input to the tank. After reaching this specified value of temperature, the air allowed to force through the tower by forced draft fan. The air flow rate is maintained at different level by adjusting the control vane. When steady state condition reach, the inlet and outlet dry and wet bulb temperatures of air, and water temperature at five station along tower height were measured under variable operation parameters of liquid flow rate (L/min), and air flow rate (m 3 /sec) for types of packs.

87. S.V. University, Gajraula , UP. Dept. of Mech. Engg. 87 RESULTS AND ANALYSIS Model Validation Model validation was carried out for predicting the performance of a PCFCWCT and a CCFCWCT. Moreover, the predicted outlet temperatures of process water were compared with the measurements for both towers.

88. S.V. University, Gajraula , UP. Dept. of Mech. Engg. 88 Validation with a Parallel Counter-Flow CWCT No Air Supply Process Water T w,o,pre ( o C ) T w,o - T w,o , pre m a kg/s) T wb,i ( o C ) m w (kg/s) T w,i ( o C ) T w,o ( o C ) c psat = 2.3516 b ext = 0.803 b int = 0.565 cpsat = 3.587 b ext = 0.452 b int = 0.640 c psat = 5.2759 b ext = 0.283 b int = 0.697 Max ( o C ) 1 1.33 13.52 0.4 18.15 15.19 15.39 15.39 15.39 0.20 2 0.59 12.13 0.4 19.74 16.85 16.87 16.86 16.86 0.02 3 0.59 10.55 0.4 18.54 15.67 15.54 15.53 15.53 0.14 4 0.59 11.68 0.8 18.53 17.02 17.05 17.05 17.05 0.03 5 1.33 11.78 0.8 15.86 14.35 14.34 14.35 14.35 0.01 6 0.59 10.34 0.8 17.24 15.76 15.76 15.76 15.76 0.00 7 0.58 13.59 0.8 20.38 18.9 18.94 18.94 18.94 0.04 8 1.30 13.27 0.8 17.97 16.37 16.24 16.25 16.26 0.13

89. Dept. of Mech. Engg. 89 Validation with a Cross Counter-Flow CWCT No Air Supply Process Water T w,o,pre ( o C ) T w,o - T w,o , pre m a kg/s) T wb,i ( o C ) m w (kg/s) T w,i ( o C ) T w,o ( o C ) c psat = 2.3516 b ext = 0.803 b int = 0.565 cpsat = 3.587 b ext = 0.452 b int = 0.640 c psat = 5.2759 b ext = 0.283 b int = 0.697 Max ( o C ) 1 0.35 20.1 0.32 30.3 26.7 26.67 26.68 26.67 0.03 2 0.35 20.9 0.32 32.9 28.8 28.58 28.59 28.59 0.22 3 0.35 22 0.32 36.6 31.2 31.27 31.29 31.29 0.09 4 0.19 21.1 0.32 30.2 27.7 27.76 27.75 27.74 0.06 5 0.27 20.6 0.32 30.8 27.6 27.54 27.54 27.54 0.06 6 0.35 20.8 0.31 30.2 27.5 27.56 27.56 27.56 0.06 7 0.35 13.59 0.26 30.4 26.6 26.62 26.61 26.60 0.02 8 0.35 21.1 0.2 30.2 26.1 26.18 26.16 16.26 0.08 9 0.35 22.6 0.29 30.2 27.6 27.36 27.36 27.36 0.24 10 0.35 22.8 0.3 30.2 27.4 27.48 27.48 27.48 0.8

90. Dept. of Mech. Engg. 90 Effects of Different Factors on Cooling Capacity and Effectiveness Effects of Spray Water Flow Rate and Inlet Water Temp. Effect of Ambient Wet-Bulb Temperature Effect of Air Flow Rate Effects of Inlet Water Temperature Effects of Process Water Flow Rate Effects of the Mass Flow Rates of Air and Process Water

91. Dept. of Mech. Engg. 91 Effect of Ambient Wet-Bulb Temperature

92. Dept. of Mech. Engg. 92 Effect of Air Flow Rate

93. Dept. of Mech. Engg. 93 Effects of Inlet Water Temperature

94. Dept. of Mech. Engg. 94 Effects of Process Water Flow Rate

95. Dept. of Mech. Engg. 95 Effects of the Mass Flow Rates of Air and Process Water

96. Dept. of Mech. Engg. 96 Effects of the Mass Flow Rates of Air and Process Water

97. Dept. of Mech. Engg. 97 Uncertainty Analysis Variables unit A-Type B-type u(y) Temperature(T) °C 1.51e-2 1.00e-2 1.01e-2 Water Flow meter( Fw ) lts /hr 3.522e-2 1.00e-2 2.25e-2 Air Velocity(v) m/s 1.583-1 1.00e-1 2.01e-1 Air pressure drop mm of water 1.325 1.00 2.25 Orifice diameter(d) m -- 1.33e-5 1.33e-5 Pipe diameter(D) 1.33e-5 1.33e-5 Cd - - 3.633e-3 3.63e-3 Uncertainty estimation of variables

98. Dept. of Mech. Engg. 98 Combined standard uncertainty Input Sensitivity coefficient Uncertainty Percentage Input Water flow 4.29e-4 2.101 0.0721 Water flow Air flow 5.25e-5 4.325 0.0653 Air flow Uncertainty estimation of variables

99. Dept. of Mech. Engg. 99 CALCULATION 1.Cooling tower approach = T2 – WBT = 33 – 29 = 4 C 2. Cooling tower range = T1 – T2= 39 – 33 3. Mass of water circulated in cooling tower Mw1=Volume of water x density of water Mw1 = 30 x 1000= 30000 Kg / hr 4.Heat loss by water (HL) = Mw1 x Cpw x (T1 – T2) = 30000 x 4.186 x (39 - 33) = 753480 KJ / hr

100. Dept. of Mech. Engg. 100 CALCULATION............continued 5.Volume of air required = (HL x Vs1) / [(Ha2 – Ha1) - (W2 –W1) x Cpw x T2] = (753480 x 0.908) / [(118 - 94) – (0.03 – 0.024) x 4.186x 33] = 29526.337 m3 / hr 6.Heat gain by air = V x [(Ha2 – Ha1) - (W2 – W1) x Cpw xT2]/ Vs1 = 29526.337 x [(118 - 94) – (0.03 – 0.024) x4.186 x 33] /0.908 = 684159.833 /0.908= 753480 KJ / hr 7.Mass of air required = V / Vs1= 29526.337 /0.908 = 32517.992 Kg / hr

101. Dept. of Mech. Engg. 101 Statistical ANALYSIS Run WF(A) AF(A) WT(C) Mean S/ N Ratio Curved 20mm Packing Curved 100mm Packing Triangular Packing Curved 20mm Packing Curved 100mm Packing Triangular Packing 1 100 100 40 61.70 59.10 55.00 35.80 35.43 34.80 2 100 150 44 72.05 68.15 64.67 37.14 36.67 36.20 3 100 200 48 73.42 69.25 66.67 37.30 36.81 36.47 4 150 100 44 55.02 50.05 47.33 34.80 33.99 33.49 5 150 150 48 60.77 57.30 51.33 35.66 35.16 34.19 6 150 200 40 60.33 58.10 54.00 35.69 35.28 34.64 7 200 100 48 49.33 46.40 43.00 33.85 33.33 32.66 8 200 150 40 52.00 49.35 47.00 34.31 33.87 33.44 9 200 200 44 58.67 56.50 52.00 35.34 35.04 34.32 Experimental results for effectiveness and S/N Ratio

102. Dept. of Mech. Engg. 102 Average values of cooling tower effectiveness for each parameters at different levels.

103. Dept. of Mech. Engg. 103 Average values of cooling tower effectiveness for each parameters at different levels..............continued

104. Dept. of Mech. Engg. 104 Average values of cooling tower effectiveness for each parameters at different levels...........continued.

105. Dept. of Mech. Engg. 105 Average value of S/N ratios for each parameter at different levels.

106. Dept. of Mech. Engg. 106 Average value of S/N ratios for each parameter at different levels…………. cintinued .

107. Dept. of Mech. Engg. 107 Average value of S/N ratios for each parameter at different levels…………. cintinued .

108. Dept. of Mech. Engg. 108 S/N ANALYSIS OF OPTIMIZATION OF PERFORMANCE The experimental results and corresponding S/N ratios are given in Table .The uncontrolled or the noise parameters are the outside air DBT and WBT. As these cannot be kept at a particular level during experimentation, experiments are conducted at each experimental setting from 7.00am to 9.00am.Data measured at two hour interval are used at noise parameters(N1 to N8).

109. Dept. of Mech. Engg. 109 S/N ANALYSIS OF OPTIMIZATION OF PERFORMANCE Expt No Output Response Cooling tower effectiveness Cooling tower Effectiveness( ) S/N N1 N2 N3 N4 N5 N6 N7 N8 Mean Predicted 1 63 63 64 64 65 64 63 63 63.6 61.4 36.0710 2 64 64 64 65 65 64 64 64 64.2 62.6 36.1569 3 65 65 65 66 66 65 64 64 65.1 63.8 36.2738 4 69 69 69 70 70 69 68 68 69.1 67.5 36.7917 5 70 70 70 71 71 70 69 69 70.1 68.7 36.9165 6 71 71 71 72 72 71 70 70 71.1 69.9 37.0395 7 73 73 73 74 74 73 72 72 73.0 73.7 37.2652 8 74 74 74 75 75 74 73 73 74.0 74.9 37.3834 9 75 75 75 74 75 74 73 73 74.40 75.1 37.5143 10 50 50 50 51 51 51 49 49 50.2 53.2 34.0205 11 51 51 51 52 52 51 50 50 51.1 54.4 34.1709 12 52 52 52 53 53 52 51 51 52.1 55.6 34.3392 13 60 60 60 61 61 60 59 59 60.1 59.3 35.5798 14 61 61 61 62 62 61 60 60 61.1 60.5 35.7231

110. Dept. of Mech. Engg. 110 S/N ANALYSIS OF OPTIMIZATION OF PERFORMANCE .........................continued . 15 62 62 62 62 63 62 61 61 62.0 61.7 35.8470 16 66 66 66 67 66 66 65 65 66.0 65.5 36.3901 17 67 67 67 68 68 67 66 66 67.1 66.7 36.5366 18 68 68 68 69 68 68 67 67 68.0 67.9 36.6495 19 44 44 44 45 44 44 43 43 44.0 45.0 32.8674 20 47 47 47 48 48 47 46 46 47.1 46.2 33.4629 21 49 49 49 50 50 49 48 48 49.1 47.4 33.8241 22 53 53 53 54 54 53 52 52 53.1 51.1 34.5043 23 54 54 54 55 54 54 54 53 54.0 52.3 34.6468 24 56 56 56 57 57 56 56 55 56.1 53.5 34.9816 25 57 57 57 58 58 57 57 56 57.1 57.3 35.1351 26 58 58 58 59 59 58 58 57 58.1 58.5 35.2859 27 59 59 59 60 60 59 59 58 59.1 59.7 35.4341

111. Dept. of Mech. Engg. 111 ANALYSIS OF VARIANCE (ANVOA) ANVOA is a method most widely used for determining significant parameters on response and measuring their effects. In the cooling tower performance, the major factor of the non-reproducibility is the controls the test facility and the cooling tower operating condition.

112. Dept. of Mech. Engg. 112 Analysis of Ceramic Packing Cooling Tower

113. Dept. of Mech. Engg. 113 Analysis of Ceramic Packing Cooling Tower

114. Dept. of Mech. Engg. 114 ANVOA results for Optimization of Performance Source DF Seq SS Adj SS Adj MS F % of contribution WF 2 25.3060 25.3060 12.6530 1132.05 59.33 AF 2 15.4723 15.4723 7.7362 692.15 36.27 WT 2 0.5970 0.5970 0.2985 26.71 1.40 WF*AF 4 1.0400 1.0400 0.2600 23.26 2.43 WF*WT 4 0.1045 0.1045 0.0261 2.34 0.25 AF*WT 4 0.0436 0.0436 0.0109 0.97 0.10 Residual Error 8 0.0894 0.0894 0.0112 0.21 Total 26 42.6529 100 Analysis of Variance for S/N ratios

115. Dept. of Mech. Engg. 115 ANVOA results for Optimization of Performance Analysis of Variance for means Source DF Seq SS Adj SS Adj MS F % of contribution WF 2 1224.86 1224.86 612.431 1916.31 61.65 AF 2 698.94 698.94 349.469 1093.50 35.20 WT 2 25.68 25.68 12.841 40.18 1.30 WF*AF 4 31.97 31.97 7.994 25.01 1.61 WF*WT 4 2.20 2.20 0.550 1.72 0.11 AF*WT 4 0.59 0.59 0.148 0.46 0.03 Residual Error 8 2.56 2.56 0.320 0.13 Total 26 1986.81 100

116. Dept. of Mech. Engg. 116 REGRESSION ANALYSIS Regression analysis of Performance of Ceramic Packing Cooling Tower By mean of regression and correlation analysis, the effect of process parameter on the quality characteristics of cooling tower effectiveness ( e ) was obtained as follows. 20mm Packing Mean=53.4-0.157WF+0.0879AF+0.395WT (5.3) 100mm Packing Mean=54.4-0.148WF+0.0943AF+0.267WT (5.4) Triangular Packing Mean= 52.8-0.148WF+0.09119AF+0.208WT (5.5)

117. Dept. of Mech. Engg. 117 REGRESSION ANALYSIS Run Cooling Tower Effectiveness Taguchi method Cooling Tower Effectiveness Regression Equation Curved 20mm Curved 100mm Triangular Curved 20mm Curved 100mm Triangular 1 61.70 59.10 55.00 62.29 58.71 55.43 2 72.05 68.15 64.67 68.27 64.49 60.82 3 73.42 69.25 66.68 74.24 70.28 66.20 4 55.02 50.05 47.33 56.02 52.39 48.86 5 60.77 57.30 51.33 62.00 58.16 54.25 6 60.33 58.10 54.00 63.23 60.74 57.14 7 49.33 46.40 43.00 49.75 46.05 42.29 8 52.00 49.35 47.00 50.99 48.63 45.19 9 58.67 56.50 52.00 56.96 54.41 50.57

118. Dept. of Mech. Engg. 118 Regression Analysis for optimization of performance By mean of regression and correlation analysis, the effect of process parameter on the quality characteristics of cooling tower effectiveness ( e ) was obtained as follows. e (predicted) = 53.8 - 0.164 WF + 0.123 AF + 0.299 WT (7) where e , WF, AF and WT are cooling tower effectiveness,

119. Dept. of Mech. Engg. 119 Determination of optimal factor levels WF AF 100 150 200 100 150 200 36.5 36.0 35.5 35.0 34.5 WT 40 44 48 36.5 36.0 35.5 35.0 34.5 Main Effects Plot for SN ratios Data Means

120. Dept. of Mech. Engg. 120 Interaction plot for S/N ratios AF 100 150 200 40 44 48 50 55 60 50 55 60 100 150 200 WF WT

121. Dept. of Mech. Engg. 121 CONFIRMATION TESTS

122. Dept. of Mech. Engg. 122 Comparative results of cooling tower effectiveness

123. Dept. of Mech. Engg. 123 Experimental condition including optimal factors setting Experimental condition for confirmation test Test Water Flow-kg/hr (Factor-WF) Air Flow-kg/hr (Factor-AF) Water Temperature-˚C (Factor-WT) 1 100 200 48 2 150 150 44 3 200 100 40

124. Dept. of Mech. Engg. 124 Comparative test results for cooling tower effectiveness Confirmation results for confirmation tests Test Experimental Results Results as per Developed model Eq (5 ) Error % 1 76.5 75.1 1.83 2 62.9 60.5 3.82 3 44.2 45.0 1.81 Average % error 2.49

125. Dept. of Mech. Engg. 125 Normal probability plot for means

126. Dept. of Mech. Engg. 126 Normal probability plot for means

127. Dept. of Mech. Engg. 127 Normal probability plot for means

128. Dept. of Mech. Engg. 128 Histogram for experimental run of Means

129. Dept. of Mech. Engg. 129 Histogram for experimental run of Means

130. Dept. of Mech. Engg. 130 Histogram for experimental run of Means

131. Dept. of Mech. Engg. 131 Vitiation of mass transfer coefficient with L / G ratios

132. Dept. of Mech. Engg. 132 Variation of mass transfer coefficient with hot water temperture

133. Dept. of Mech. Engg. 133 Variation of mass transfer coefficient with inlet air dry bulb temperature

134. Dept. of Mech. Engg. 134 Variation of cold water temperature with packing height

135. Dept. of Mech. Engg. 135 Relation between experimental and predicted cold water temperature

136. Dept. of Mech. Engg. 136 Relation between experimental and predicted outlet air dry bulb temperature

137. Dept. of Mech. Engg. 137 Variation of cooling tower effectiveness with L/G ratio

138. Dept. of Mech. Engg. 138 CONCLUSIONS The performance of the cooling tower is dominated by wind speed, ambient air temperatures and humidity in the atmospheric conditions. . In ideal condition, the heat loss by water must be equal to heat gain by air. But in actual practice it is not possible because of some type of losses. Cooling tower performance increases with increase in air flow rate, increase in air-water contact and characteristic decreases with increase in water to air mass ratio.

139. Dept. of Mech. Engg. 139 Performance Analyses of Counter-Flow Closed Wet Cooling Towers Two typical closed water cooling towers (CWCTs) with different counter-flow constructions, viz. one with the parallel counter-flow and the other with the cross counter-flow, were selected as the study objects in this work. The cooling capacity and cooling tower effectiveness of both towers were investigated and compared, respectively, under given operating conditions. The key findings are summarized .

140. Dept. of Mech. Engg. 140 The key findings Performance Analyses of CWCTs. (1) A simplified cooling capacity model with two characteristic parameters inputting was developed and the two parameters were determined by curve fitting of real-time experimental data using the Levenberg –Marquardt method. (2) The predicted outlet temperatures of the process water were in good agreement with the experimental data. The maximum absolute errors between the predicted values and the measurements were 0.20 and 0.24 _C for CCFCWCT & PCFCWCT respectively

141. Dept. of Mech. Engg. 141 The key findings Performance Analyses of CWCTs. (3) Although the flow patterns of both types of counter-flow CWCTs were different, the effects of the main influencing factors on their performance indicators were similar. (4) The PCFCWCT is much more applicable than the CCFCWCT in a large-scale cooling water system, and the superiority would be amplified when the scale of water distribution system increases.

142. Dept. of Mech. Engg. 142 CONCLUSIONS Based on the ANOVA results, all control factors have significant effect on the quality characteristics statistically. Water flow (WF) has the most dominant effect on total variation and it is followed by air flow (AF), and water temperature(WT) The effects of control factors and their response were modelled via regression and correlation analysis with R2-value of 96.6%. The optimum experiment condition was obtained with water flow kept at first level(100kg/hr) , air flow kept at third level (200kg/hr) and water temperature kept at third level (44˚C) for all ceramic packing.

143. Dept. of Mech. Engg. 143 CONCLUSIONS.............continued The confirmation experiments at optimum experiment condition was conducted and found that the error between predicted values and confirmation test results is only 2.49%. As seen from the optimum results, maximum cooling tower effectiveness was achieved at lower water flow rate, higher air flow rate and medium water temperature.

144. Dept. of Mech. Engg. 144 CONCLUSIONS.............continued The confirmation experiments at optimum experiment condition was conducted and found that the error between predicted values and confirmation test results is only 2.49%. This result indicates that Taguchi method can be used in the optimization of counter flow cooling tower performance reliably. As seen from the optimum results, maximum cooling tower effectiveness was achieved at lower water flow rate, higher air flow rate and medium water temperature. 20mm ceramic packing having higher effectiveness was achieved compare with 100mm and triangular ceramic packing using optimization techniques.

145. Dept. of Mech. Engg. 145 CONCLUSIONS.............continued Based on the ANOVA results, all control factors and their two-way interactions have significant effect on the quality characteristics statistically. Uncertainty analysis was conducted in the experimental runs. The cooling tower performance measurements were conducted in order to ensure the measurement reliability. Validation results agree with errors less than 0.1%. Water flow (WF=59.33%) has the most dominant effect on total variation and it is followed by air flow (AF=36.27%), water flow-air flow (WF-AF=2.43%) and water temperature(WT=1.4%) The effects of control factors and their two-way interactions on response were modelled via regression and correlation analysis with R2-value of 96.6%.

146. Dept. of Mech. Engg. 146 CONCLUSIONS.............continued The optimum experiment condition was obtained with water flow kept at first level(100kg/hr) , air flow kept at third level (200kg/hr) and water temperature kept at third level (48˚C). Three confirmation experiments including one at optimum experiment condition were conducted and found that the error between predicted values and confirmation test results is only 2.49%. This result indicates that Taguchi method can be used As seen from the optimum results, maximum cooling tower effectiveness was achieved at lower water flow rate, higher air flow rate and higher water temperature.