Industrial-scale garage dry fermentation systems are
extremely
nonlinear, and traditional machine learning algorithms have low prediction
accuracy. Therefore, this study presents a novel intelligent system
that employs two automated machine learning (AutoML) algorithms (AutoGluon
and H2O) for biogas performance prediction and Shapley
additive explanation (SHAP) for interpretable analysis, along with
multiobjective particle swarm optimization (MOPSO) for early warning
guidance of industrial-scale garage dry fermentation. The stacked
ensemble models generated by AutoGluon have the highest prediction
accuracy for digester and percolate tank biogas performances. Based
on the interpretable analysis, the optimal parameter combinations
for the digester and percolate tank were determined in order to maximize
biogas production and CH4 content. The optimal conditions
for the digester involve maintaining a temperature range of 35–38
°C, implementing a daily spray time of approximately 10 min and
a pressure of 1000 Pa, and utilizing a feedstock with high total solids
content. Additionally, the percolate tank should be maintained at
a temperature range of 35–38 °C, with a liquid level of
1500 mm, a pH range of 8.0–8.1, and a total inorganic carbon
concentration greater than 13.8 g/L. The software developed based
on the intelligent system was successfully validated in production
for prediction and early warning, and MOPSO-recommended guidance was
provided. In conclusion, the novel intelligent system described in
this study could accurately predict biogas performance in industrial-scale
garage dry fermentation and guide operating condition optimization,
paving the way for the next generation of intelligent industrial systems.