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Waste Disposal, Fluid

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Detecting the interaction between microparticles and biomass in biological wastewater treatment process with Deep Learning method.

The Science of the total environment
Investigating the interaction between influent particles and biomass is basic and important for the biological wastewater treatment. The micro-level methods allow for this, such as the microscope image analysis method with the conventional ImageJ pro...

Real-time monitoring of activated sludge flocs via enhanced mask region-based Convolutional Neural networks.

Environmental research
The functionality of activated sludge in wastewater treatment processes depends largely on the structural and microbial composition of its flocs, which are complex assemblages of microorganisms and their secretions. However, monitoring these flocs in...

Application and innovation of artificial intelligence models in wastewater treatment.

Journal of contaminant hydrology
At present, as the problem of water shortage and pollution is growing serious, it is particularly important to understand the recycling and treatment of wastewater. Artificial intelligence (AI) technology is characterized by reliable mapping of nonli...

Application of artificial intelligence tools in wastewater and waste gas treatment systems: Recent advances and prospects.

Journal of environmental management
The non-linear complex relationships among the process variables in wastewater and waste gas treatment systems possess a significant challenge for real-time systems modelling. Data driven artificial intelligence (AI) tools are increasingly being adop...

Evaluation of enhanced chemical coagulation method for a case study on colloidal liquid particle in wastewater treatment: Statistical optimization analysis and implementation of machine learning.

Journal of environmental management
Coal mines are one of the largest sources of energy supply and generate significant volumes of wastewater. Chemical coagulation is one of the most effective methods for wastewater treatment. In this research, ferric and aluminum-based coagulants, alo...

Quality evaluation parameter and classification model for effluents of wastewater treatment plant based on machine learning.

Water research
With the growing consensus of emerging pollutants and biological toxicity risks in wastewater treatment plant (WWTP) effluents, traditional water quality management based on general chemical parameters no longer meets the new challenges. Here, a firs...

Forecasting nitrous oxide emissions from a full-scale wastewater treatment plant using LSTM-based deep learning models.

Water research
Nitrous oxide (NO) emissions from wastewater treatment plants (WWTPs) exhibit significant seasonal variability, making accurate predictions with conventional biokinetic models difficult due to complex and poorly understood biochemical processes. This...

Constructing a visual detection model for floc settling velocity using machine learning.

Journal of environmental management
Optimizing the dosage of coagulant is a time-consuming process, and real-time evaluation of floc settling velocity can quickly predict the coagulation effect and optimize the dosage. This study used a convolutional neural network (CNN) model to analy...

Application of machine learning in ultrasonic pretreatment of sewage sludge: Prediction and optimization.

Environmental research
In this research, typical industrial scenarios were analyzed optimized by machine learning algorithms, which fills the gap of massive data and industrial requirements in ultrasonic sludge treatment. Principal component analysis showed that the ultras...

Machine learning-assisted source tracing in domestic-industrial wastewater: A fluorescence information-based approach.

Water research
An emergency water pollution incident poses a significant risk to the proper functioning of wastewater treatment plants, particularly in domestic-industrial integrated facilities. Source tracing is recognized as an effective method to mitigate ongoin...