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자료유형
학술저널
저자정보
저널정보
대한환경공학회 Environmental Engineering Research Environmental Engineering Research 제26권 제6호
발행연도
2021.12
수록면
67 - 76 (10page)

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The aim of this study was to develop a model for predicting the performance of a desulfurizing bio-filter (BF), without requiring prior information about H₂S biodegradation kinetics and mechanism. A single hidden layer artificial neural network (ANN) model was developed and validated using the gradient descent backpropagation (GDBP) learning algorithm coupled with a learning rate and a momentum factor. The ANN model inputs were gas flow rate, residence time, and axial position in the BF bed. The removal efficiency of H₂S was the model output. Various structures for ANN model, differing in the number of hidden layer neurons, were trained and an early stopping validation technique, the K-fold cross-validation, was used to determine the optimal structure with the best generalization ability. The modeling results showed that there was a good agreement between the experimental data and the predicted values, with a determination coefficient (R²) of 94%. This implies that the ANN model might be an attractive and useful alternative tool for forecasting the performance of desulfurizing BFs.

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ABSTRACT
1. Introduction
2. Artificial Neural Network (ANN)
3. Methodology
4. Results and Discussion
5. Conclusions
References

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