AIMC Topic: Nitrogen

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An artificial intelligence modeling framework based on microbial community structure prediction enhances the pollutant removal efficiency of the algae-bacteria granular sludge system.

Journal of environmental management
Algae-bacteria granular sludge (ABGS) technology is a new energy-saving and low-carbon water treatment technology based on the algae-bacteria symbiotic system. However, due to its complex internal microbial system, the regulation mechanism of ABGS is...

A neural network-shaped composite of α-MnO with N-doped graphene for electrocatalytic reduction of hydrogen peroxide in human urine samples.

The Analyst
A neural network-shaped composite fusing α-MnO and nitrogen-doped graphene (N@Gr/α-MnO) was synthesized a hydrothermal method. The resulting composite demonstrates enhanced electrocatalytic activity for hydrogen peroxide (HO) compared with each sing...

Urbanization intensifies deterministic selection of pathogenic bacteria in river networks: Nitrogen-driven niche partitioning and cross-scale risk forecasting through DOM-bacteria interplay.

Environmental research
Urbanization modifies the composition of dissolved organic matter (DOM) and nitrogen nutrients, profoundly affecting river microbial communities. However, the mechanisms driving pathogenic and non-pathogenic bacteria remain unclear. In this study, we...

AI-driven wastewater management through comparative analysis of feature selection techniques and predictive models.

Scientific reports
The integration of artificial intelligence (AI) in wastewater treatment management offers a promising approach to optimizing effluent quality predictions and enhancing operational efficiency. This study evaluates the performance of machine learning m...

Prediction of water quality parameters and pollution exceedance analysis in typical rivers of semi-arid regions based on interpretable deep learning models.

Environmental pollution (Barking, Essex : 1987)
Deep learning models that integrate environmental characteristics provide a powerful means for high-precision water quality prediction; however, their black-box nature can limit interpretability and reliability. We proposed an interpretable Attention...

Responses of Microbial Community to Heterogeneous Dissolved Organic Nitrogen Constituents in the Hyporheic Zones of Treated Sewage-Dominated Rivers.

Microbial ecology
The hyporheic zone (HZ) of treated sewage-dominated rivers serves as a critical biogeochemical hotspot for dissolved organic nitrogen (DON) transformation, yet the mechanisms linking DON chemodiversity to microbial community dynamics remain poorly re...

Toward explicit learning frameworks for predicting the solubility of CO - N gas mixtures in brine: Implication for impure CO storage in saline aquifers.

Journal of contaminant hydrology
Carbon capture and storage (CCS) is a crucial technology for reducing industrial CO emissions and mitigating climate change. However, its large-scale deployment faces significant financial challenges, with CO capture and compression accounting for th...

Machine learning approaches for predicting antibiotic resistance genes abundance changes during biological nitrogen removal process.

Journal of environmental management
Wastewater treatment plants (WWTPs) serve as reservoirs for multiple antimicrobial agents (AAs), thereby promoting the risk of antibiotic resistance genes (ARGs) transmission in sewage and sludge during biological nitrogen removal (BNR) processes. An...

Optimization of nitrogen removal through an intelligent automated operational strategy based on real-time process simulation in an A2O membrane bioreactor.

Journal of environmental management
In this study, an intelligent automated operational strategy (IAOS) was developed and evaluated to enhance nitrogen removal efficiency in an anaerobic-anoxic-oxic (A2O) membrane bioreactor (MBR). To effectively respond to fluctuations in inflow load,...

Developing Pharmaceutically Relevant Pd-Catalyzed C-N Coupling Reactivity Models Leveraging High-Throughput Experimentation.

Journal of the American Chemical Society
This manuscript presents machine learning models for Pd-catalyzed C-N couplings constructed using a large, pharmaceutically relevant, structurally diverse dataset (4204 unique products) generated using high-throughput experimentation. The dataset ge...