TY  - JOUR
AU  - Li, Yiyang
AU  - Wei, Yongqiang
PY  - 2026
DA  - 2026/06/27
TI  - Optimizing Biogas Yield and Carbon-Nitrogen Balance in Agricultural Anaerobic Digestion via a Hybrid CNN-LSTM Attention Model: A Pathway to Circular Bioeconomy
JO  - Digital Intelligence in Agriculture
T2  - Digital Intelligence in Agriculture
JF  - Digital Intelligence in Agriculture
VL  - 2
IS  - 2
SP  - 88
EP  - 102
DO  - 10.62762/DIA.2026.512329
UR  - https://www.icck.org/article/abs/DIA.2026.512329
KW  - circular bioeconomy
KW  - agricultural anaerobic digestion
KW  - attention mechanism
KW  - biogas optimization
KW  - precision agriculture
AB  - The transition to a circular bioeconomy in agriculture demands precise, real-time optimization of organic waste valorization, with anaerobic digestion (AD) being a central process. However, the inherent non-linearity, time-varying dynamics, and complex microbial interactions in large-scale agricultural AD reactors pose significant challenges to traditional kinetic models and human operators. This study proposes a novel data-driven hybrid CNN-LSTM-attention model to predict and optimize biogas yield and carbon-nitrogen (C/N) ratios using high-frequency multi-sensor data. By integrating real-time sensor feeds of pH, volatile fatty acids (VFAs), total solids (TS), and historical biogas production, the model captures both spatial feature correlations and long-term temporal dependencies, while the attention mechanism enhances interpretability by dynamically weighting input features. Validated on a 12-month dataset from a 500 m³ commercial AD plant, the hybrid model achieved a MAPE of 4.27% for 24-hour ahead biogas prediction, significantly outperforming standalone LSTM (6.75%) and traditional ARIMA models (9.83%). Model-guided C/N ratio optimization increased cumulative methane yield by 12.4% and reduced digestate processing costs by 9.8%. This framework provides an intelligent precision management tool for agricultural waste-to-energy systems, directly supporting the technological pillars of the circular bioeconomy.
SN  - 3069-3187
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Li2026Optimizing,
  author = {Yiyang Li and Yongqiang Wei},
  title = {Optimizing Biogas Yield and Carbon-Nitrogen Balance in Agricultural Anaerobic Digestion via a Hybrid CNN-LSTM Attention Model: A Pathway to Circular Bioeconomy},
  journal = {Digital Intelligence in Agriculture},
  year = {2026},
  volume = {2},
  number = {2},
  pages = {88-102},
  doi = {10.62762/DIA.2026.512329},
  url = {https://www.icck.org/article/abs/DIA.2026.512329},
  abstract = {The transition to a circular bioeconomy in agriculture demands precise, real-time optimization of organic waste valorization, with anaerobic digestion (AD) being a central process. However, the inherent non-linearity, time-varying dynamics, and complex microbial interactions in large-scale agricultural AD reactors pose significant challenges to traditional kinetic models and human operators. This study proposes a novel data-driven hybrid CNN-LSTM-attention model to predict and optimize biogas yield and carbon-nitrogen (C/N) ratios using high-frequency multi-sensor data. By integrating real-time sensor feeds of pH, volatile fatty acids (VFAs), total solids (TS), and historical biogas production, the model captures both spatial feature correlations and long-term temporal dependencies, while the attention mechanism enhances interpretability by dynamically weighting input features. Validated on a 12-month dataset from a 500 m³ commercial AD plant, the hybrid model achieved a MAPE of 4.27\% for 24-hour ahead biogas prediction, significantly outperforming standalone LSTM (6.75\%) and traditional ARIMA models (9.83\%). Model-guided C/N ratio optimization increased cumulative methane yield by 12.4\% and reduced digestate processing costs by 9.8\%. This framework provides an intelligent precision management tool for agricultural waste-to-energy systems, directly supporting the technological pillars of the circular bioeconomy.},
  keywords = {circular bioeconomy, agricultural anaerobic digestion, attention mechanism, biogas optimization, precision agriculture},
  issn = {3069-3187},
  publisher = {Institute of Central Computation and Knowledge}
}