Biorefinery can be promoted by building accurate machine learning models. This work proposed a strategy to enhance model's generalization ability and overcome insufficient data conditions for mixed sugar fermentation simulation. Multiple inputs single output models, using initial glucose, initial xylose, and time together as inputs, have higher generalization ability than single input single output models with time as sole input in predicting glucose, xylose, ethanol, or biomass separately. Multiple inputs multiple outputs models, integrating outputs, enhanced model accuracy and resulted in an average R² at 0.99. To overcome data insufficiency conditions, consensus yeast (CY) model, through consolidating data from 4 yeasts, obtained R² at 0.90. By adjusting the pretrained CY model, the model can save more than 50% data and get R² at 0.95 and 0.93 for yeast and bacterial fermentation simulation. The strategy can expand the application range and save costs of data curation for ANN models.

• PMID: 37352987
• DOI full text
• PubMed
• PubTator

Reference Type

Journal Article

Authors

Huang XY, Ao TJ, Zhang X, Li K, Zhao XQ, Champreda V, Runguphan W, Sakdaronnarong C, Liu CG, Bai FW

Primary Lit For

Additional Lit For

XKS1 | XYL2 | TAL1

Review For