3 Gas Champion : MBB : IBM BCS PO : 2 BBc : : /45

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2 3 Gas Champion : MBB : IBM BCS PO : 2 BBc : : /45

3 Define ~ Analyze Define VOB KBI R 250 O 2 2.2% CBR Gas Dome 1290 CTQ KCI VOC Measure Process Data USL Target LSL Mean Sample N StDev (Within) StDev (Overall) * * Process Capability Analysis for heat_p LSL Within Overall Potential (Within) Capability Z.Bench 1.59 Z.USL * Z.LSL 1.59 Cpk 0.53 Cpm * Overall Capability Z.Bench 1.02 Z.USL * Z.LSL 1.02 Ppk Observed Performance PPM < LSL PPM > USL * PPM Total Exp. "Within" Performance PPM < LSL PPM > USL * PPM Total Exp. "Overall" Performance PPM < LSL PPM > USL * PPM Total /45

4 2/45 I I Others STS Defect Count Percent Cum % Percent Count ('02) Others Defect Count Percent Cum % Percent Count ('02) Defect Count Percent Cum % Percent Count ('02) 65.1% 83.1% 86.3% 13.7%

5 Define ~ Analyze Analyze Vital Few AHP Analysis Insight Variable Selection Regression Decision Tree 1. Mgas_cal 2. Cog_p 3. A_g 4. Mgas_w Vital Few ( ) Process (Regression, Multi Variate Analysis) Machine Learning (Decision Tree/Neural Network) Data 3/45

6 Process Overview Process HBV GAS BAV BGV (1100~1300) Process BF., M-Gas ( Parallel ), CBV CHV 4/45

7 Model Regression Model (1) Model (2 ) Input (, ) Decision Tree (1) (2 ). DT Neural Network (1) Data Input Layer. (2) signal Multilayer Perceptron Hidden Layers Output Layer Hidden Unit 5/45

8 P_heat_p( )Vital Few Contour 6/45

9 Data Mining Vital Few V.Few (heat_p >83%) DM Mgas_cal (kcal/n) COGMgas_w (%) (%) (N/D) ,668, ,634,000? Problem : Data Mininer Local Solution OK, But Gobal Solution? Data Mining Pilot Test. Crytal Ball Based Simulator,. 7/45

10 Quick Win Pilot Test Quick Win 1 ( 1,4 ) EV Open #1 : Feedforward Guidance Simulator ( 1,2,4 ) #2 : ( 1,4 ) ~ ~ 8.4 ~ Quick Win 2 ( 4 ) Steam & N2 Line 9.4 ~ #3 : RV Open Program ( 4 ) #4 : & ( 5 ) #5 : MCV ( 4 ) #6 : BFG ( 1,2 ) 10.9 ~ ~ 9.4 ~ ~ Pilot Test 10.14~ Quick Win 3 ( 3,4 ) #7 : Pattern ( 1,2,3,4 ) Calorie #8 : Mgas PID Gain Tunning( 2,3,4 ) ~ ~ 9.4 ~ Vital Fews 1:COG, 2:, 3:Mgas Calorie, 4:Mgas, 5: 8/45

11 (1) #1 : BFG & COGFeedforward Guidance Simulator Feedforward Guidance Simulator3 Meter Room - Neural Network Model : Guidance ( )COG Mgas. 9/45

12 (2) #2 : 1. BFG M-GAS 2. COG MIX-GAS M-GAS 3. : Program & Logic DOME DOME DOME 4. Gas Chro Calorie (H2%, CO%) : Over 10/45

13 (3) #4 : - No.34 No.34 Max : 189 Max : 187 Max : 187 T/C Max : ~10.1, 11/45

14 (4) #7 : Pattern 3.. Pilot Test checker Pattern Conventional Method Proposed Method 99,588N/Hr 91,853N/Hr ,687N/Hr (9) 5% 2Pattern : Mix Gas Calorie 12/45

15 (5) #8 : Mgas PID Gain Tunning PID( Proportional-Integral-Derivative) Controller. 3 (PV:Process Value) (SV :Set Value). P Gain Rising Time I Gain Setting Time. PID Gain Tunning Matlab Simulation. Tunning Tunning P Gain- Mgas_w I Gain- Mgas_w D Gain- Mgas_w ??? 3 Mix Gas Gain Mix Gas Hunting : Mix Gas & COG 13/45

CTQ ( ) & Vital Few : 36.9 / : ( 81 87%) Gas Total : 13.

46/Mcal x 365 x 0.98 = 36.9 / Gas Gas : : 396.9 396.9 359.7 359.

16 CTQ ( ) & Vital Few : 36.9 / : ( 81 87%) Gas Total : / {( Gas Gas ) x } x Mixed Gas = {( Mcal/T-P)} x 8,587Ton/D x 32.46/Mcal x 365 x 0.98 = 36.9 / Gas Gas : : Mcal/T-P Mcal/T-P :37.2 :37.2 Mcal/T-P Mcal/T-P (10% (10% ) ) CTQ CTQ ( ( ): ): % 87.1% 87.1% : : % % 14/45

Manufacturing6

Manufacturing6 σ6 Six Sigma, it makes Better & Competitive - - 200138 : KOREA SiGMA MANAGEMENT C G Page 2 Function Method Measurement ( / Input Input : Man / Machine Man Machine Machine Man / Measurement Man Measurement