실규모 하수처리공정에서 동력학적 동특성에 기반한 인공지능 모델링 및 예측기법

다국어 초록 (Multilingual Abstract)

The established mathematical modeling methods have limitation to know the hydraulic characteristics at the wastewater treatment plant which are complex and nonlinear systems. So, an artificial neural network (ANN) model based on hydraulic characteristics is applied for modeling wastewater quality of a full-scale wastewater treatment plant using DNR (Daewoo nutrient removal) process. ANN was trained using data which are influents (TSS, BOD, COD, TN, TP) and effluents (COD, TN, TP) components in a year, and predicted the effluent results based on the training. To raise the efficiency of prediction, inputs of ANN are added the influent and effluent information that are in yesterday and the day before yesterday. The results of training data tend to have high accuracy between real value and predicted value, but test data tend to have lower accuracy. However, the more hydraulic characteristics are considered, the results become more accuracy.

목차 (Table of Contents)

• Abstract
• Ⅰ. 서론
• Ⅱ. 인공신경망
• Ⅲ. 재료 및 방법
• Ⅳ. 결과 및 고찰
• Ⅴ. 결론
• 참고문헌

참고문헌 (Reference)

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