Mg-modified biochar shows high adsorption performance under weakly acidic and neutral water conditions. However, its phosphate removal efficiency markedly decreases in naturally alkaline wastewater, such as that released in livestock farming (anaerob...
Assessing the mutual benefits of artificial intelligence (AI) and bioenergy systems, to promote efficient and sustainable energy production. By addressing issues with conventional bioenergy techniques, it highlights how AI is revolutionising optimisa...
Wastewater sludges (WSs) are major reservoirs and emission sources of antibiotic resistance genes (ARGs) in cities. Identifying antimicrobial resistance (AMR) host bacteria in WSs is crucial for understanding AMR formation and mitigating biological a...
The development of high-performance strains and the continuous breakthrough of strain screening technology also pose challenges to downstream fermentation optimization and scale-up. Therefore, neural network models are utilized to optimize the fermen...
Interpretable causal machine learning (ICML) was used to predict the performance of denitrification and clarify the relationships between influencing factors and denitrification. Multiple models were examined, and XG-Boost model provided the best pre...
A graph learning [Binarized Attributed Network Embedding (BANE)] model enhances the single-target and multi-target prediction performances of random forest and eXtreme Gradient Boosting (XGBoost) by learning complex interrelationships between cultiva...
Biogas yield in anaerobic digestion (AD) involves continuous and complex biological reactions. The traditional linear models failed to quantitatively assess the interactive effects of these factors on AD performance. To further explore the internal r...
Accurate modeling of methane (CH) and sulfide (HS) production in sewer systems was constrained by insufficient consideration of microbial processes under dynamic environmental conditions. This study introduces a microbial-guided machine learning (ML)...
Causal inference-assisted machine learning was used to predict photosynthetic bacterial (PSB) protein production capacity and identify key factors. The extreme gradient boosting algorithm effectively predicted protein content, while the gradient boos...
This review explores the critical role of machine learning (ML) in enhancing microalgae bioprocesses for sustainable biofuel production. It addresses both technical and economic challenges in commercializing microalgal biofuels and examines how ML ca...