| Title: | Prediction for odor gas generation from domestic waste based on machine learning |
| Author(s): | Jiang Y; Huang J; Luo W; Chen K; Yu W; Zhang W; Huang C; Yang J; Huang Y; |
| Address: | "State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China; College of Environment and Ecology, Chongqing University, Chongqing 400044, China. CITIC Environmental Technology Investment (China) Co., Ltd, Guangzhou 510000, China. State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China; College of Environment and Ecology, Chongqing University, Chongqing 400044, China. Electronic address: hclsjb@163.com. College of Physics, Chongqing University, Chongqing, 400044, China. College of Physics, Chongqing University, Chongqing, 400044, China. Electronic address: yzhuang@cqu.edu.cn" |
| DOI: | 10.1016/j.wasman.2022.12.006 |
| ISSN/ISBN: | 1879-2456 (Electronic) 0956-053X (Linking) |
| Abstract: | "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 R(2) 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" |
| Keywords: | *Odorants Gases *Volatile Organic Compounds Fermentation Machine Learning Domestic waste Odor gases Prediction Random forest; |
| Notes: | "MedlineJiang, Yuanyan Huang, Jiawei Luo, Wei Chen, Kejin Yu, Wenrou Zhang, Wenjun Huang, Chuan Yang, Junjun Huang, Yingzhou eng 2022/12/13 Waste Manag. 2023 Feb 1; 156:264-271. doi: 10.1016/j.wasman.2022.12.006. Epub 2022 Dec 10" |
