uv/tio /h o prediction of removal efficiency in … · tae-min kim, jong-in choi*, ... .-/ `b tio2...

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361 HWAHAK KONGHAK Vol. 39, No. 3, June, 2001, pp. 361-367 (Journal of the Korean Institute of Chemical Engineers) UV/TiO 2 /H 2 O 2 * ** *( ) ** (2001 2 9 , 2001 3 28 ) Prediction of Removal Efficiency in UV/TiO 2 /H 2 O 2 System using Error Back-propagation Neural Network Tae-Min Kim, Jong-In Choi*, Min Oh** and Tae-Hee Lee Dept. of Chemical Engineering, Yonsei University, Seoul 120-749, Korea *HYOSUNG CORPORATION R&D CENTER, Kyonggi 430-080, Korea **Division of Chemical Engineering and Technology, Hanbat National University, Daejeon 308-710, Korea (Received 9 February 2001; accepted 28 March 2001) UV/TiO 2 /H 2 O 2 , , . UV/TiO 2 /H 2 O 2 . 4 (η) (α) 0.9 0.7 , , . UV/TiO 2 /H 2 O 2 . Abstract - A prediction model of removal efficiency in UV/TiO 2 /H 2 O 2 system was constructed using the error back propa- gation neural network with generalized delta rule. The network was trained to produce removal efficiency according to initial concentrations of formic acid, dosages of oxidant and concentrations of catalyst. The reliability of removal efficiency predic- tion using the constructed neural network was verified by comparing the experimental data of the system for various condi- tions. The error back propagation network with four layer learned exactly the removal efficiency curve of the system when step size coefficient(η) was 0.9 and momentum(α) was 0.7. It offered reasonable prediction of removal efficiency curve for the var- ious initial concentrations of formic acid, dosages of oxidant and concentrations of catalyst. It was found that the error back propagation neural network is the proper model to predict the removal efficiency of UV/TiO 2 /H 2 O 2 system. Key words: UV/TiO 2 /H 2 O 2 , Photocatalyst, Neural Network, Formic Acid E-mail: [email protected] 1. UV/TiO 2 /H 2 O 2 TiO 2 UV, H 2 O 2 OH radical . CO 2 H 2 O 2 , . TiO 2 TiO 2 1976 , phenol , , [1-6]. , TiO 2 Sabate [7] TiO 2 , TiO 2 [8-11]. , , .

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HWAHAK KONGHAK Vol. 39, No. 3, June, 2001, pp. 361-367(Journal of the Korean Institute of Chemical Engineers)

����� ��� � � UV/TiO 2/H2O2 ���� �� �� ��

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Prediction of Removal Efficiency in UV/TiO2/H2O2 System usingError Back-propagation Neural Network

Tae-Min Kim, Jong-In Choi*, Min Oh** and Tae-Hee Lee†

Dept. of Chemical Engineering, Yonsei University, Seoul 120-749, Korea*HYOSUNG CORPORATION R&D CENTER, Kyonggi 430-080, Korea

**Division of Chemical Engineering and Technology, Hanbat National University, Daejeon 308-710, Korea(Received 9 February 2001; accepted 28 March 2001)

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Abstract − A prediction model of removal efficiency in UV/TiO2/H2O2 system was constructed using the error back propa-

gation neural network with generalized delta rule. The network was trained to produce removal efficiency according to initial

concentrations of formic acid, dosages of oxidant and concentrations of catalyst. The reliability of removal efficiency predic-

tion using the constructed neural network was verified by comparing the experimental data of the system for various condi-

tions. The error back propagation network with four layer learned exactly the removal efficiency curve of the system when stepsize coefficient(η) was 0.9 and momentum(α) was 0.7. It offered reasonable prediction of removal efficiency curve for the var-

ious initial concentrations of formic acid, dosages of oxidant and concentrations of catalyst. It was found that the error back

propagation neural network is the proper model to predict the removal efficiency of UV/TiO2/H2O2 system.

Key words: UV/TiO2/H2O2, Photocatalyst, Neural Network, Formic Acid

†E-mail: [email protected]

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Fig. 1. Generation of primary radicals at surface of irradiated TiO2 par-ticles.

Fig. 2. The reaction scheme of TiO2.

Fig. 3. Artificial neuron with activation function.

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Fig. 4. Three-layer back propagation network.

Fig. 5. Schematic diagram of UV/TiO2/H2O2 system.1. Tank 6. H2O2 input system2. Pump 7. 2-way on/off valve3. Flow meter 8. Check valve4, 10. 3-way valve 9. TiO2 separation unit5. Reactor

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Table 1. Specifications of UV/TiO2/H2O2 system

Total sample batch volume 2.0 LEffective reactor volume 0.126 LVolumetric flow-rate 0.85-2.85 L/minTiO2 Degussa P-25UV wavelength UV-C(254 nm)UV lamp type StraightLamp Power 30 WUV output 10 W

Fig. 6. Electrical conductivity vs. formic acid concentration in water.

Fig. 7. Training data recall for various number of layers.

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Û+ ÞoC ��-|HI, ¾üU"� ï�� ,� 100 mg/L, 1� 1.9

Table 2. LMS errors for various momentums and training steps

Momentum(α)

0.1 0.3 0.7 0.9

Training step (η)

0.1 1.2001E2 1.1832E2 1.1004E2 5.2710E30.3 1.1088E2 1.0329E2 5.3730E3 3.3580E30.7 6.1510E3 4.8640E3 2.5830E3 1.6590E30.9 5.4220E3 4.0540E3 6.8300E4 4.2425E2

Fig. 8. Training curves for several momentums training step pairs.

Fig. 9. Performance curve regeneration during the training(αααα: 0.7, ηηηη: 0.9).

Fig. 10. Training data recall for various catalyst concentrations(formicacid 100 mg/L).

Fig. 11. Estimation of removal efficiency curve for catalyst concentra-tion(formic acid 100 mg/L).

HWAHAK KONGHAK Vol. 39, No. 3, June, 2001

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Fig. 14. Training data recalls for various H2O2 concentrations(formicacid 100 mg/L, catalyst 2.0 g/L).

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HWAHAK KONGHAK Vol. 39, No. 3, June, 2001