期刊
WASTE MANAGEMENT
卷 156, 期 -, 页码 264-271出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.wasman.2022.12.006
关键词
Domestic waste; Odor gases; Machine learning; Random forest; Prediction
In this study, machine learning models (Random Forest, XGBoost, LightGBM) were established to predict the production of odors from domestic waste based on four factors (weight, wet composition, temperature, and fermentation time). The Random Forest model showed the highest accuracy with a R2 value of 0.8958. Furthermore, the impact of microbial fermentation on odor production from domestic waste was discussed.
Domestic waste is prone to produce a variety of volatile organic compounds (VOCs), which often has unpleasant odors. A key process in treating odor gases is predicting the production of odors from domestic waste. In this study, four factors of domestic waste (weight, wet composition, temperature, and fermentation time) were adopted to be the prediction indicators in the prediction for domestic waste odor gases. Machine learning models (Random Forest, XGBoost, LightGBM) were established using the odor intensity values of 512 odor gases from domestic waste. Based on these data, the regression prediction with supervised machine learning was achieved, in which three different algorithmic models were evaluated for prediction performance. A Random Forest model with a R2 value of 0.8958 demonstrated the most accurate prediction of the production of domestic waste odor gas based on our data. Furthermore, the prediction results in the Random Forest model were further discussed based on the microbial fermentation of domestic waste. In addition to enhancing our knowledge of the production of odor from domestic waste, we also explore the application of machine learning to odor pollution in our study.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据