experimental, computational and numerical analysis

Experimental, Computational and Numerical Analysis of

Oxygen Enrichment Process in Energy Conservation in

Rotary Furnace Foundry Operation

Dr. R.K. Jain Professor & Head, Department of Mechanical Engineering, ITM University, Gwalior, India

ABSTRACT

This paper deals with Experimental Investigations computational and numerical analysis of oxygen

enrichment of combustion volume in LDO-fired rotary furnace, for specific fuel and energy conservation.

Energy consumption is major problem being faced by the Indian ferrous foundries. Bureau of Energy

Efficiency, The Energy and resources Institute, Govt. of India New Delhi & other International agencies

has reported that energy consumption in Indian ferrous foundries is much more above the required limits

and has to be drastically reduced.

The author conducted experimental investigation on oxygen enrichment of preheated air in a self

designed and developed 200 kg rotary furnace in an industry The specific fuel and energy consumption of

furnace, (when operated under existing conditions, without oxygen enrichment of preheated air,) in

melting only was 0.460liter/kg or 4110.45 Kwh/tone and total 4172.00 Kwh/tone. When operated with

oxygen enrichment of preheated air, the specific fuel and energy consumption in melting only reduced to

0.260 liter/kg or 2667.00 Kwh/tone and in total to2711.00 Kwh/tone. The energy consumption in melting

only is reduced by 35.12% and in total by 35.01%.

The L.M. modeling method of artificial neural networks contained in Mat Lab software is used for

modeling and optimization. The average percentage variation between actual experimental and modeled

results is +8.905% which is within acceptable limits of10%. The numerical techniques of initially

developing equations and then solving them has been applied. The result so obtained is compared with

Experimental, and computational results. The variation is -3.5678% which is well within the acceptable

limits.

Keywords: Rotary Furnace, Specific Fuel, Oxygen Enrichment, Energy Consumption.

1. INTRODUCTION

The rotary furnace is very simple melting unit consisting mainly of a drum of required size having a cone

on each side lined with refractory, fire bricks or ramming mortar generally having alumina as a

constituent. This drum is placed on rollers so that they may be either locked or slowly rotated about their

central axis. The rollers are driven by a small electric motor. At one end of the drum, a suitable burner is

placed with appropriate blower system and combustion gases exit from other end. This drum or horizontal

cylinder is flanked by two conical portions on both sides. One of the cones accommodates the burner

whereas from the other cone, hot flue gases exit. Charging of the iron for melting is also done from this

side. The cone on one side can accommodate different types of burners using the light diesel oil (LDO).

The tap hole is located in the cylindrical wall halfway between the ends. This tap hole is used to take out

the molten metal, but it is kept closed during the melting of metal. Figure-1 shows the layout and

accessories of a rotary furnace.

International Journal of Research in Mechanical Engineering

Volume-2, Issue-2, March-April, 2014, pp. 01-12, IASTER 2014

www.iaster.com, ISSN Online:2347-5188 Print: 2347-8772


Volume-2, Issue-21, March-April, 2014, www.iaster.com ISSN

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Fig 1. Layout of a Rotary Furnace Plant

2. LITERATURE REVIEW

A number of investigations had been conducted in the past on a rotary furnace. Baker EHW [1] explained

the working of Rotary furnace. Jain R.K, Singh R,[2].applied regression modeling and excel solver

technique for mathematical modeling and optimization of critical parameters of rotary furnace viz. rpm,

melting rate, specific fuel consumption etc. Jain RK, Singh R, Gupta B.D. [3] presented an overview of

energy consumption in ferrous foundry and stressed upon the need of an energy efficient furnace for

foundries. Baijayanath, Pal Prosanto Panigrahy K.C. [4] explained that most of the units are crippled with

usage of rudimentary techniques. The Indian foundry industry needs optimization of energy consumption.

Singh Kamlesh Kumar [5] advocates the use of newer and cleaner technology for energy conservation.

Arjunwadkar S.H, Pal Prosanto [6] stressed upon to use energy efficient melting techniques. Pandey

G.N., Singh Rajesh, Sinha A.K [7] emphasized upon to supply oxygen at 8kg/cm2 pressure as it reduces

melting time and emission levels.

W.W.Levi [8] was the first person to develop a mathematical model between carbon content in the charge

and that of tapped metal. Pehlke [9] developed the first thermo chemical model for predicting cupola

performance under various operating conditions. Landefeld and Katz [10] developed a kinetic model for

carbon pick up in cupola based on carbon activity .Sahajwala[11] et al have estimated the extend of

carburization and re carburization of the solid charge in stack of cupola and found it to be negligible.

Sahajwala and Pehlke[12] pointed out accurate control of carbon content depend upon identifying the

phenomena which controls it. Stanik et al [13] has developed similar mathematical models .Karunakar

and Datta[14] has successfully applied artificial neural networks in the control of cupola furnace. Bishop

Christopher M.[15] explained the working and importance of neural networks in modeling and

optimization .Haykins Symon [16] successfully applied the single layer and multilayer network

architecture for neural networks in modeling and optimization.

Grewal B.S [17]. explained numerical methods to solve the linear, transcendental and polynomial

equations. Rao S.S [18] described the procedures of optimization using mathematical techniques. Shastri

S.S.[19] applied numerical techniques to solve the engineering problems. Das H.K and Verma Rama [20]

used statistical techniques for optimization of objectives.



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2. MATERIALS AND METHODS

2.1Melting Operation-The process of melting the charge in rotary furnace is carried out in the

following steps:

(I) Preheating of oil and furnace

(II) Charging After pre heating, the furnace is charged.

(III) Rotation-After sufficient pre heating and charging, the furnace is rotated at desired speed.

(IV) Melting- The flame starts coming out of the exit end, which is initially yellowish in color. After

approximately 1 hour, the colour of flame changes to white indicating that metal has been

thoroughly melted. The temperature of the molten metal is measured using pyrometer. If it is

approximately 1250 to 1300C, the rotation of furnace is stopped.

(V) Tapping-The tape hole is slightly lowered and opened and metal is transferred into ladles, which

are pre heated prior to the transfer of molten metal to avoid heat losses.

(VI) Inoculation-The Ferro silicon and Ferro manganese approx. 600 grams per heat are added in

molten metal contained in the ladles.

(VII) Pouring The ladles are then carried to moulds and pouring is completed. The furnace is shown in

fig 1.

3. EXPERIMENTAL INVESTIGATIONS

3.1 Operating Furnace Under Existing Conditions of Operation without Oxygen Enrichment Specific Fuel and Energy Consumption

The furnace was operated under existing conditions of operation without oxygen enrichment. The charge

per heat is 200.0 kg. In first heat, as furnace was started from room temperature, the melting time, fuel

and energy consumption were more. In subsequent heats, the melting time, fuel and energy consumption

were reduced. 1liter of LDO is equivalent to 9.9047kwh/kg of energy. Observations are given in table 1.

Table1- Performance and Specific Fuel Consumption of Furnace under Existing Conditions

of Operations without Oxygen Enrichment

(I) The graphical representation the graphical representation of energy consumption under existing

conditions of operation is shown in fig 2.

S

N

He

at

no

Rp

m

Time

min

Fuel

liters

Specific

Fuel

(lit/kg)

Melting

Rate

(kg/hr)

Flame

temp.0C

Preheated

air cons.

m3

Energy

consumption

oxygen enrich.

kwh/kg

1 1 2.0 50.0 92.0 0.460 240.0 1310.0 1320.0 4.556

2 2 2.0 47.0 90.0 0.450 255.3 1314.0 1290.0 4.457

3 3 2.0 46.0 87.0 0.435 260.8 1325.0 1240.0 4.308

4 4 2.0 46.0 86.0 0.430 266.0 1334.0 1220.0 4.259

5 5 2.0 45.0 83.0 0.415 266.0 1350.0 1175.0 4.110



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Fig 2- Energy Consumption under Existing Conditions of Operation

3.2 Operating Furnace with Oxygen Enrichment of Preheated Air- Specific Fuel and Energy

Consumption

It is thought to optimize the energy consumption by reducing the amount of air and supplying oxygen

externally, required for combustion. Several experiments were conducted, gradually reducing air to its

theoretical requirement and even lesser in steps of 5.0 to 10.0% and supplying oxygen externally in steps

of 1.0 to 2.0 %, and its effect on flame temperature, time, fuel, melting rate, and fuel consumption was

studied. The effect was significant only when air was reduced to 75.0% of its theoretical requirement and

approx 7.0% oxygen was supplied externally. The experimental investigations conducted are given in

following sections.

(I) Effect of 6.9%oxygen enrichment of 75.3-75.4 % of theoretically required air on flame

temperature, time, fuel, melting rate, specific fuel and energy consumption-Numbers of experiments

are conducted, rotating furnace at optimal rotational speed 1.0 rpm, with 6.9% oxygen enrichment of

75.3-75.4% of theoretically required air, preheating LDO to 700C. The effect of above on flame

temperature, time, fuel, melting rate, and specific fuel consumption are given in table 3.

Table 2- Effect of 6.9% oxygen enrichment of 75.3-75.4% of theoretically required air on performance

(flame temperature, time, fuel, melting rate), and specific fuel consumption.

The above experimental investigations reveal that by 6.9% oxygen enrichment of 75.3-75.4% of

theoretically required preheated air, the specific fuel and energy consumption are significantly reduced.

He

at

no

Rp

m

Preh

eated

air

temp 0C

Flame

temp0

C

Time

min

Fuel

liter

Melting

rate

kg/hr

Specific

fuel

cons

lit/kg

Oxy

gen

cons

m3

Oxy

gen

cons

%

Preheated

air

cons.

m3

Preheated

air

cons %

Energy

consumpti

on oxygen

enrich

kwh/kg

1 1.0 410.0 1710.0 33.0 56.0 363.0 0.280 39.0 6.9 459.0 75.3 2.773

2 1.0 418.0 1722.0 32.0 56.0 375.0 0.280 39.0 6.9 459.0 75.3 2.773

3 1.0 428.0 1730.0 32.0 55.0 375.0 0.280 38.5 6.9 451.0 75.4 2.773

4 1.0 449.0 1746.0 31.5 54.0 385.0 0.270 38.0 6.9 443.0 75.4 2.674

5 1.0 454.0 1752.0 31.0 53.0 387.0 0.265 37.0 6.9 434.5 75.3 2.624

6 1.0 458.0 1754.0 30.5 52.0 393.0 0.260 36.6 6.9 426.7 75.4 2.575

7 1.0 460.0 1755.0 30.5 52.0 393.4 0.260 36.5 6.9 426.5 75.4 2.575



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(II) The Graphical Presentations

(a) Effect of 6.9% oxygen enrichment of 75.3-75.4% of theoretically required air on specific energy

consumption- are presented graphically in figs 2.

Fig 3-Effect of 6.9% oxygen enrichment of 75.3-75.4% of theoretically required air

on specific energy consumption

4. RESULTS

The results of above experimental investigations are summarized in table 5.

4.1 Performance of furnace- The performance of furnace is compared in table 5

Table 3- Comparison of Performance of Furnace

(III) The Graphical Presentations

Comparison of flame temperature, time, fuel, melting rate, and specific fuel consumption, and specific

energy consumption under existing conditions and with 6.9% oxygen enrichment - are presented

graphically in figs 2.

Parameters

Operating furnace

without oxygen

enrichment of preheated

air

Operating furnace with 6.9%

oxygen enrichment of 75.3%-

75.4% of theoretically required

preheated air

Time minutes 45.0 30.5

Flame temperature0C 1350.0 1755.0

Melting rate kg/hr 266.0 393.4

Fuel liters 83.0 52.0

Specific fuel cons.lit/kg 0.415 0.260

Specific energy cons. kwh/kg 4.110 2.575

Preheated Air consumption m3 1175.0 426.5

Oxygen consumption ------- 182.5 m3/tone



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Fig 4- Comparison of flame temperature, time, fuel, melting rate, and specific fuel consumption, and specific

energy consumption under existing conditions and with 6.9% oxygen enrichment

5. COMPUTATIONAL TECHNIQUES

Statistical methods such as cluster analysis, pattern recognition and design of experiments, factorial analysis

and regression analysis are some of the statistical techniques which enable one to analyze the experimental

data and build empirical models to obtain the most accurate representation of physical situations. The data on

preheated air temperature, flame temperature time of heat fuel consumption/heat, oxygen.

The furnace was run with a maximum preheated air temperature of 4600C, flame temperature 1750C,

Time / heat 30.5 minutes, fuel/heat 52 liters, melting rate 393.44 kg/hr, oxygen Consumption/heat

36.5m3, and air consumption/heat 426.5m3. The specific fuel consumption was 0.260 liters/kg. The

specific fuel consumption has been taken as output parameter Y and all other parameters viz. preheated

air temperature, flame temp. fuel, oxygen & air consumption /heat, melting rate etc. has been taken as

input parameters X [x1, x2, x3, x4, x5, x6, x7 etc].

5.1 Modeling of specific fuel consumption ---- Table 2 is being reproduced with specific fuel

consumption as output for mathematical modeling.

Table 4- Table 2 Reproduced with Specific Fuel Consumption as Output and all other

Parameters as Input for Mathematical Modeling

OUTPUT



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Regression modeling is used as given in matrices of MAT LAB 7.0.The steps followed are same as

mentioned in model 1.

The specific fuel consumption (lit/kg) is function of

= [(PA)(FT)(TIME)(FUEL)(OXYGEN)(AIR)(MELTING RATE)]

and is given by following equation

SFC=C0(PA)C1

(FT)C2

(TIME)C3

(FUEL)C4

(OXYGEN)C5

(AIR)C6

(MELTING RATE)C7

-- (1)

Where C0 C1C2C3C4C5C6C7 are constants to be evaluated using mat lab. The programme run is shown

below clc;

clear all;

close all;

y=[-1.27296;-1.27296;-1.27296;-1.30933;-1.32802;-

1.34707;-1.34707];

x1=[1 1 1 1 1 1 1];

x2=[6.01615 6.03548 6.05912 6.10031 6.11809 6.12686

6.13122];

x3=[7.44424 7.45124 7.45587 7.46508 7.46851 7.46965

7.47022];

x4=[3.49650 3.46573 3.46573 3.44998 3.43398 3.41772

3.41772];

x5=[4.02535 4.02535 4.00733 3.98898 3.97029 3.95124

3.95124];

x6=[3.66356 3.66356 3.65065 3.63758 3.61091 3.60004

3.59731];

x7=[6.12905 6.12905 6.11146 6.09356 6.07419 6.05608

6.05561];

x8=[5.89440 5.92692 5.92692 5.95324 5.95842 5.98492

5.97492];

x=[x1;x2;x3;x4;x5;x6;x7;x8]';

xt=x';

st3=xt*x;

st4=inv(st3);

st5=xt*y;

st6=st4*st5

v=st6(1)

c0=exp(v)

c1=st6(2)

c2=st6(3)

c3=st6(4)

c4=st6(5)

c5=st6(6)

c6=st6(7)

c7=st6(8)

pa=input('\n enter value of pre-heated air:= ');

ft=input('\n enter flame temperature:= ');

time=input('\n enter time:= ');

fc=input('\n enter fuel consumption:= ');



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oe=input('\n enter oxygen enrichment:= ');

as=input('\n enter air supplied:= ');

mr=input('\n enter melting rate:= ');

logsfc1=log(c0)+c1*log(pa)+c2*log(ft)+c3*log(time)+c4*log(fc)+c5*log(oe)+c6*log(as)+c7*log(mr)

sfc=exp(sfc1)

RESULT

c0 = 6.5300* 10-056

c1 = -4.4722

c2 = 17.3755

c3 = -1.0635

c4 = -21.6445

c5 = 0.6346

c6 = 20.2754

c7 = -2.0810

The equation 1 becomes

logsfc1=log(6.5300*1056

)-4.4722log(pa)+17.3755*log(ft)-1.0635*log(time)-21.6445*log(fuel)

+0.6346*log(oxygen)+20.2754log(air)-2.0810*log(mr)

Or SFC = (6.5300* 10

-056) (PA)

-4.4722 (FT)

17.3755 (TIME)

-1.0635 (FUEL)

-21.6445 (OXYGEN)

0.6346 (AIR)

20.2754

(MELTING RATE) -2.0810

------------------------- ()

5.2 Calculation of modeled specific fuel consumption

1: sfc1= -1.1440, sfc =0.3185. 2: sfc1 =-1.1439, sfc =0.3186 3: sfc1 = -1.1438 sfc = 0.3186

4: sfc1 = -1.1801 sfc = 0.3072, 5: sfc = -1.1989, sfc = 0.3015, 6: sfc1 =-1.1973, sfc = 0.3020

7: sfc1 = -1.2181, sfc = 0.2958

5.3 Comparison of modeled and actual specific fuel consumption----The comparison of modeled and

actual specific fuel consumptions is shown in following table5

Table5 -The Comparison of Modeled and Actual Specific Fuel Consumptions

(I) The Graphical Representation -

The comparison of modeled and

actual specific fuel consumptions is

shown in fig 5.

Fig 5 -The Comparison of Modeled and

Actual Specific Fuel Consumptions.

S.No. Modeled

output

Experimental

output

Absolute

variation

% Absolute

variation

Mean average

%variation

1 0.3185 0.280 +0.0385 +12.087 +8.905%

2 0.3186 0.280 +0.0386 +12.115

3 0.3186 0.280 +0.0386 +12.115

4 0.3072 0.270 +0.0372 +12.109

5 0.3015 0.265 +0.0365 +12.106

6 0.3020 0.260 +0.042 +13.907

7 0.2958 0.260 -0.0358 -12.102



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6. Mathematical Techniques

(I) Formation and Development of Equation

As per mathematical analysis the equation has been considered as

Y=a0x4+a1x

3+a2x

2+a3x+a4,

Where a0-----a4 are constants and x is variable= oxygen consumption/heat.

The mathematical equations are of polynomial curve and given in table 6.

Table 6- The Mathematical Equations

(IV) Evaluation of Constants -Mat lab

The values of constants a1-----a5 are evaluated using Mat lab. The programme run is shown below and values are given in table 7.

M-file script

A= [(39.0)4 (39.0)

3 (39.0)

2 (39.0) 1; (38.5)

4 (38.5)

3 (38.5)

2 (38.5) 1; (38.0)

4 (38.0)

3 (38.0)

2 (38.0) 1;

(37.0)4 (37.0)

3 (37.0)

2 (37.0) 1; (36.5)

4 (36.5)

3 (36.5)

2 (36.5) 1];

b= [56.0; 55.0; 54.0; 53.0; 52.0];

X=A\b

Solution:

X = -0.4

60.667

-3449.5

87150.83

-825446.6

Where, X= [a0; a1; a2; a3; a4] Table 7 -The Values of Constants A0-----A4 Using Mat Lab

(V) Comparison of Calculated and Experimental Outputs

Putting values of constants as per table 7 in equations 1 to 5, (table 6) and comparison of calculated and

experimental outputs is given in table 8.

Table 8- Comparison of Calculated and Experimental Outputs

S

No Equation

Calcula

ted

output

Experi

mental

output

Absolute

variation

%

Absolute

variation

Mean

average

%

variation

1 a0(39.0)4+a1(39.0)

3 +a2(39.0)

2 +a3(39.0)+a4= 55.843 56.00 -0.157 -0.280%- -3.5678%

2 a0(38.5)4+a1(38.5)

3+a2(38.5)

2+a3(38.5)+a4 54.093 55.00 -0.907 -1.649%

3 a0(38.0)4+a1(38.0)

3+a2(38.0)

2+a3(38.0)+a4 52.364 54.00 -1.636 -3.029%

4 a0(37.0)4+a1(37.0)

3+a2(37.0)

2+a3(37.0)+a4 49.961 53.00 -3.039 -5.733

5 a0(36.5.0)4+a1(36.5.0)

3+a2(36.5)

2+a3(36.5)+a4 48.283 52.00 -3.717 -7.148%

S.No. Equations

1 a0(39.0)4+a1(39.0)

3 +a2(39.0)

2 +a3(39.0)+a4= 56.0

2 a0(38.5)4+a1(38.5)

3+a2(38.5)

2+a3(38.5)+a4= 55.0

3 a0(38.0)4+a1(38.0)

3+a2(38.0)

2+a3(38.0)+a4=54.0

4 a0(37.0)4+a1(37.0)

3+a2(37.0)

2+a3(37.0)+a4= 53.0

5 a0(36.5.0)4+a1(36.5.0)

3+a2(36.5)

2+a3(36.5)+a4=52.0

SN Constants Value

1 a0 -0.4

2 a1 60.667

3 a2 -3449.5

4 a3 87150.83

5 a4 -825446.6



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(VI) Presentation of variations

The variations between experimental and calculated output y (specific fuel consumption) is presented

graphically in fig 4.

Fig 6 -The Variations between Experimental and Calculated Output Y (Specific Fuel Consumption)

6. RESULTS (I) Experimental Analysis The percentage reductions in operating parameters under two different conditions (under existing

conditions of operation and with oxygen enrichment process) are given in table 9.

Table 9- The Comparison of Energy Consumptions under Existing Conditions of Operation

and with 6.9% Oxygen Enrichment

The comparison of energy consumptions based on Experimental analysis is also presented graphically in

figure 5.

Fig 7- The Comparison of Energy Consumptions Based on Experimental Analysis

Parameters Percentage reductions/increments

Time minutes -32.22%

Flame temperature0C +30%

Melting rate kg/hr +47.89%

Fuel liters -37.34%

Specific fuel cons.lit/kg -37.34%

Specific energy cons.kwh/kg 37.34

Preheated Air consumption m3 -63.70%

Oxygen consumption -



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(II) Computational Analysis - The mean average percentage variations is+8.905%, it lies within the

acceptable limits of 10%.

(III) Numerical Analysis - The variations between actual and modeled specific fuel consumption is from

-0.280% to-7.148%.The mean average percentage variations is -3.5678% It lies within the acceptable

limits of 10%.

7. CONCLUSIONS

The above experimental investigations reveal that by 6.9% oxygen enrichment of 75.3-75.4% of

theoretically required preheated air, not only the specific fuel and energy consumption are significantly

reduced but performance of furnace is also significantly improved.

The computational analysis reveals that mean average percentage variation between modeled and

experimental specific fuel consumption is+8.905%.it is acceptable.

The numerical analysis reveals that mean average percentage variation between calculated and

experimental fuel consumption is-3.5678 % It lies within the acceptable limits of 10%.

It is concluded that computational analysis and numerical analysis both are capable of analyzing the

experimentally investigated results of specific fuel/energy consumption with sufficient accuracy and

further can be utilized for analysis of energy conservation analysis in any sector.

REFERENCES

[1] Baker EHW 1976Rotary furnace Modern Workshop Technology, part -1, Clever Hummer Press

Ltd., London- 2nd

Edition. Chap 4.

[2] Jain R.K., Singh R, 2008- Modeling and Optimization of Rotary Furnace Parameters using Regression & Numerical Techniques proc. 68th World Foundry Congress, Feb7-10, 2008, Chennai. Pp 178-185.

[3] Jain R.K., Singh R., Gupta B.D2008.- Energy Considerations in Indian Ferrous Foundries. Indian Foundry Journal, 54(8), p.p. 32-34.

[4] Baijya Nath, Pal Prosanto, Panigrahi K. C. 2007 Energy Conservation Options among Indian Foundries-A Broad Overview . Indian Foundry Journal 53(8) pp. 27-30.

[5] Singh Kamlesh Kumar, 2007 - Energy Efficiency in Foundry Process and Casting Rejection Control Indian Foundry Journal 53(11) pp. 43-48.

[6] Arjunwadkar S.H. Pal Prosanto, et al 2008 -Energy Savings and Carbon Credits- Opportunities and Challenges For Indian Foundries J. Indian Foundry Journal, 54(10) p.p.33-37.

[7] Pandey G.N., Singh Rajesh & SinhaA.K,2007.- Efficient Energy Measures in Steel Foundry Indian Foundry Journal, 53(10) pp.49-54.



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[8] Levis W.W. Variables Affecting Carbon Control in Cupola Transactions of AFS 1947,vol 55,pp 626-632.

[9] Pehlke R.D. Thermo Chemical Model of Computer Prediction of Cupola Performance Transactions of AFS 1963, l7(1),pp580-587.

[10] Landefeld C. F., Katz S. A Dual Stream Model of Carbon Pick Up Based on Carbon Activity Cast Metals, 1976, 3(4), pp163-17

[11] Sahajiwala V., Pehlke R.D., Landefeld C. F Modeling Key Cupola Reactions Behavior of Carbon, Silicon, And Manganese Transactions of AFS 1991,vol 99, pp 269-276.

[12] Sahajiwala V., Pehlke R.D Experimental Investigations and Mathematical Modeling of Carbon Transport in a Cupola Transactions of AFS 1992,vol 100, pp 343-352.

[13] Stanek V., Katz S., Landefeld C. F., Bauer M.E The AFS Cupola Process Model- A Computer Tool for Foundries Modern Casting, June 1999, pp 41-43

[14] Karunakar D.B., Dutta G.L Modeling of Cupola Furnace Parameters Using Artificial Neural Networks Indian Foundry Journal 48, (5), May 2002 pp 29-39.

[15] Christopher M. Bishop Neural Networks for Pattern Recognition Oxford University Press Sixth Indian Edition, pp116.

[16] Symon Haykins Neural Networks Pearson fifth Indian edition pp156-169.

[17] Grewal B.S.- Numerical Methods in Engineering and Science Text Book, Khanna publishers, New Delhi,pp132-168.

[18] Jain R.K., Experimental and Mathematical Analysis of Energy Conservation in Iron Foundries with Critical Input Parameters of Rotary Furnace, International Journal of Research in Mechanical Engineering, Vol.1, Issue 2, Oct.-Dec. 2013, pp.108-115.

[19] Singiresu S.Rao- Engineering Optimization Theory and Practice Text Book, New Age International (P) Limited Publishers, New Delhi, pp 65-105.

[20] S.S.Shastry- Introductory Methods of Numerical Analysis Third edition, Text Book, Prentice Hall of India. New Delhi, pp 12-47.

[21] H.K. Das ,Rama Verma-Introduction to Engineering Mathematics Volume II, Text Book, S. Chand &Co. ltd, New Delhi pp121-147.

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