. . 2556 16-18 2556

Relative Air / Fuel ratio analysis of LPG Engine

1* 2 3 1,2

3

*E-mail: wisut_sueparn@hotmail.com

Wisut Sueparn1* Yanphinit Wachirasurong2 Phusit Chotswasd 3

1,2 Faculty of Engineering, Thonburi University, Bangkok 3 Faculty of Engineering and Architecture, Rajamangala University of Technology Suvarnabhumi,

Nonthaburi

*E-mail: wisut_sueparn@hotmail.com

(Back Fire)

Abstract

This research study analyzes the air to fuel ratio of the engine relatively gas LPG. Adjust air to fuel ratio

relatively incorrectly can cause more waste than gasoline and may cause danger of fire back (Back Fire) is a full installation of the garage in general tend to feel. refinement ratio of air to fuel the engine makes

relatively no certainty. The garage standard gas installation. LPG is no research data that indicates the

air fuel ratio relatively appropriate. Keywords: air to fuel ratio, Back Fire, gas LPG

1.

3 (

36.88 12.7 )

(Back Fire)

. . 2556 16-18 2556

2.

2.1

2.2

3.

3.1

4.

4.1

4.2

4.3 (Back Fire)

4.4

4.5

4.6

5.

1

5.1

2

LPG

LPG

LPG

. . 2556 16-18 2556

3 ,

4

5

5.2

( )

(Stoichiometric Combustion, chemically correct combustion theoretical combustion)

/ ( fuel/air

equivalence ratio, )

/ (relative air/fuel ratio, )

5.3

(warm up)

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5.4

(Fuel lean)

(Fuel rich)

91 (36.88 / ) 2.672 /

(12.7 / ) 0.92 /

1.752 /

*** ( 1500 . )***

1

( )

LPG

LPG

20,000

40,000

1.752

1.752

11415.52

22831.05

(Back Fire)

5.5

6

7

8

. . 2556 16-18 2556

9 2000 /

10 3000 /

11 4000 /

12 5000 /

13 5500 /

6.

1.

2 1.

2.

2

2.

3.

4.

. . 2556 16-18 2556

7.

8.

1.

2.

3.

2550

[1] . .

2550. http://lpg-trader.com. 2 2551

[2] . 2546.

. :

. [3] . ( ). 2546.

. http://www.pttplc.com. 4 2551

[4] . 2527. .

15G-IE-2525. :

[5] John B. Heywood. 1988. Internal Combustion Engine Fundamental. Singapore :

McGraw-Hill Book Company.

. . 2556 16-18 2556

Multiple Regression of Factors Affecting Water Distribution

1* 2 3 4

E-mail:j_pen2000@hotmail.com

Chanpen Anurattananon1* Paisan Sinsamruam2 Komson Dechklai3 Thiwarat Sawatdee4

1*,2,3,4Department of Industrial Engineering and Management, Faculty of Engineering and Industrial

Technology, Silpakorn University, Nakhon Pathom

E-mail:j_pen2000@hotmail.com*

4

2

97.8%

( )

Abstract

According to our research about water process, we found that pressure can be used to determine the

amount of water .So the objective of this study was to find the optimum pressure. The study was initiated by finding the influencing factors .There were four factors of particular interest, these were temperature,

pressure, Time and Humidity. These factors were studied in detail by using quadratic regression model to find the mathematical equation. We then find the optimum pressure by solver. The result was shown that

the regression model had coefficient of determination (R-squared) 97.8 percent and we concluded that

temperature has the main affect. The pressure used for calculation pressure corresponding was different from the old pressure. In the water distribution process we had to use temperature included with

. . 2556 16-18 2556

pressure trend curve too for reducing blasting in the system and increasing efficiency for water distribution.

Keywords: Multiple regression, Water distribution

1.

()

( )

3

2.

2.1 (Muliple

Regression)

2.2 (Anova; Analysis

of Variance)

2.3 Microsoft Excel

solver

3.

3.1

3.2

3.3 3.4 ( 3 1

-31 2555)

3.5 2 (Multiple linear regression)

MINITAB solver Microsoft Excel

3.6 3.7

3.8

4.

1

4.1 (Analysis of Variance;

ANOVA)

1

- Normal Probability Plot

- Residual versus Fits

- Residual versus Orders ei

. . 2556 16-18 2556

1 ANOVA

Source DF SS MS F P

Regression 17 28429408456 1672318144 13326.71 0.000

Residual

Error

5072 636466129 125486

Total 5089 29065874586

1 P-Value 0.05

(Least Square Error) 2 ANOVA Predictor Coef SE Coef T P

Constant 26447 6125 4.32 0.000

X4 (

)

22296 2985 7.47 0.000

X5()

20058 3074 6.53 0.000

X8 ( ) -23082 4666 -4.95 0.000

X9 ( ) -847.1 273.9 -3.09 0.000

X10( ) -198.21 52.16 -3.80 0.000

X4xX8(

)

19705 1793 10.99 0.000

X4xX9(

)

-903.13 62.47 -14.46 0.000

X4xX10(

)

-154.86 12.65 -12.24 0.000

X5xX8(

)

23455 1891 12.40 0.000

X5xX9(

)

-955.96 65.08 -14.69 0.000

X5xX10(

)

-168.11 13.23 -12.71 0.000

X8xX9(

)

1455 105.4 13.80 0.000

X8xX10( 257.84 21.38 12.06 0.000

)

X9xX10(

)

0.749 1.150 0.65 0.515

X82(

)

-23240 1578 -14.73 0.000

X92(

)

0.540 3.335 0.16 0.871

X102(

)

0.2458 0.1298 1.89 0.058

S = 354.240 R-Sq = 97.8% R-Sq(adj) = 97.8%

97.8% 97.8%

97.8%

4.2

2 (Quadratic regression model)

MINITAB

Y = 26447 + 22296 X4 + 20058 X5 - 23082 X8 - 847 X9

- 198 X10 + 19705 X4xX8 - 903 X4xX9 - 155 X4xX10 + 23455 X5xX8- 956 X5xX9 - 168 X5xX10 + 1455 X8xX9 + 258 X8xX10 -

23240 X82

3

4

. . 2556 16-18 2556

5.

5.1 (Analysis of

Variance; ANOVA)

P-Value

(Least

Square Error)

5.2

(Coefficient of Determination:R2)

(R-Square= 97.8%) (R-Square(adj) =97.8%

5.3

(Linea rProgramming)

(OptimalValue) (objective

function) (constraints) (Maximum

value) 5

6

2

[1] . 1 . :

, 2536. [2]

. . : , 2554.

[3] . . 3.: , 2551.

[4] .

. 2. :

, 2550. [5]

. . : , 2551.

. . 2556 16-18 2556

[6] . .

: , 2531. [7] .

. : , 2539.

[8] . . :

, 2555.

[9] Chowdhury K. K. and E. V. Gopal., Quality Improvement Through Design of Experiments,

A case Study, Quality Engineering, 2000, pp.54-82.

[10] Gopal Rao K. and Joseph. V. Roshan., Reduction of Testing of Chemical Parameter

Using Design of Experiment. Quality Engineering,

2001, pp.42-65. [11] .

: .

, 2550.

[12] .

: .

, 2548.

[13] .

Solvent Recovery

- .

, 2550.

[14] .

.

, 2550.

[15] .

2 ( 4)

, 2549. [16] Tekasakul, P. 2003. Characteristics of soot

particles produced from rubberwood

combustion. Proceedings of the Second Asian Particle Technology Symposium, Penang,

Malaysia, Dec. 17-19, 2003: 103-108.