합성가스 조성 예측을 위한 데이터마이닝 모델 비교에 관한 연구 = A Study on the Comparison of Data Mining Models for Syngas Composition Prediction

다국어 초록 (Multilingual Abstract)

The purpose of this study is to use data mining methods to assess effective operation methods by developing prediction models for gas composition, an indicator of the effectiveness of waste gasification. We have examined an algorithm that can predict the correlation between gasifier temperature and syngas composition. We found that H2 concentrations are highly negatively correlated with the temperature of the drying zone and pyrolysis zone in the gasifier. However, CO concentrations were not correlated with the gasifier temperature. Results of analyses using PLS, MLR, PCR, and SVR, indicated that SVR showed the highest prediction accuracy result at 86.5%.

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