Robust prediction for characteristics of digestion products in an industrial-scale biogas project via typical non-time series and time-series machine learning algorithms

文献类型： 外文期刊

第一作者： Shen, Ruixia

作者： Shen, Ruixia;Sun, Peihao;Liu, Jie;Luo, Juan;Yao, Zonglu;Yu, Jiadong;Zhao, Lixin;Zhang, Ruiqiang

作者机构：

关键词： Machine learning; Time-series; Biogas project; Agricultural waste; Cost

期刊名称：CHEMICAL ENGINEERING JOURNAL （ 影响因子：13.2； 五年影响因子：13.5 ）

ISSN： 1385-8947

年卷期： 2024 年 498 卷

页码：

收录情况： SCI

摘要： Anaerobic digestion (AD) is a well-established pathway for treating agricultural organic waste, and machine learning has emerged as a novel tool to predict its product performance. In prior research, the majority of studies concentrated on non-time series models for laboratory-scale fermentation data. Consequently, the generalization performance of these models was significantly constrained, particularly in the context of industrial-scale biogas projects. Thus, in this study, typical non-time series models (GBR and RF) and time-series models (LSTM, CNN-LSTM, and DA-LSTM) after hyperparameter optimization were chosen to accurately predict the characteristics of digestion products in a biogas project. The ideal GBR model for CH4 content was obtained, and the R-2 values of the test set and training set were 0.93 (R-MSE=1.11) and 0.97 (R-MSE=0.69), respectively. Temperature was the most important parameter for biogas production according to feature importance and SHAP analysis of the RF model. The DA-LSTM was superior to LSTM and CNN-LSTM for the prediction of biogas production, and the R-2 of DA-LSTM was 0.87 (R-MSE=1048.14) with a seq_len of 10 d. This study provides direction for high-efficiency biogas production in wet biogas projects with the aid of reliable machine learning models.

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