AIMC Topic: Waste Disposal, Fluid

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An intelligent system for precise management of coagulants in sludge conditioning: Inspired by the exploratory behavior in primates using senses.

Water research
Coagulation conditioning is a key step for sludge dewatering, while the control of optimal coagulant dosing is still a challenge. This study proposes an intelligent system for the precise management of coagulants (IIS) in sludge conditioning. This sy...

Surveillance of SARS-CoV-2 RNA in wastewater treatment plants in Türkiye, Istanbul: a long-term study and statistical analysis.

Environmental monitoring and assessment
Wastewater-based epidemiology (WBE) is a powerful method that allows community surveillance to identify diseases/pandemic dynamics in a city, especially in metropolitan areas with high overpopulation. This study investigated the detection and quantif...

Novel approach for AI-based NO emission reduction in biological wastewater treatment relying on genetic algorithms and neural networks.

Water science and technology : a journal of the International Association on Water Pollution Research
The potential of measurement-based control strategies for achieving lower NO emissions in biological wastewater treatment is limited due to strong temporal variations in NO emissions and a lack of measurement data regarding influencing parameters. To...

Data assimilation for prediction of ammonium in wastewater treatment plant: From physical to data driven models.

Water research
This study compares various modeling approaches to predict ammonium concentration in wastewater treatment plants (WWTPs), with a focus on integrating data assimilation techniques. It explores white-box, grey-box, and black-box models, evaluating thei...

Novel PVDF mixed matrix membranes incorporated with green synthesized magnesium oxide nanoparticles for enhanced dye removal: Optimization using RSM, SOLVER, and ANN approach.

Environmental research
The application of nanofiltration membrane technology for removing pollutant dyes from industrial wastewater represents a significant advance in environmental remediation. This research explores the development and performance evaluation of a novel P...

Machine learning for monitoring per- and polyfluoroalkyl substance (PFAS) in California's wastewater treatment plants: An assessment of occurrence and fate.

Journal of hazardous materials
Wastewater treatment plants (WWTPs) are significant sources of per- and polyfluoroalkyl substances (PFAS) pollution, but comprehensive monitoring and management are impractical and cost-prohibitive. To strengthen monitoring programs, we developed mac...

Effective evaluation of greenhouse gases (GHGs) emissions from anoxic/oxic (A/O) process of regenerated papermaking wastewater treatment through hybrid deep learning techniques: Leveraging the critical role of water quality indicators.

Journal of environmental management
Accurate accounting of greenhouse gases (GHGs) emissions from industrial wastewater treatment processes/plants with high organic concentration and fluctuating inflows is crucial for the calculation and management of carbon emissions. The impacts of w...

Treatment options of nitrogen heterocyclic compounds in industrial wastewater: From fundamental technologies to energy valorization applications and future process design strategies.

Water research
Nitrogen heterocyclic compounds (NHCs) widely exist in industrial wastewater and presented significant environmental and health risks due to their toxicity and persistence. This review addressed the challenges in treating NHCs in industrial wastewate...

Enhancing process monitoring and control in novel carbon capture and utilization biotechnology through artificial intelligence modeling: An advanced approach toward sustainable and carbon-neutral wastewater treatment.

Chemosphere
Integrating carbon capture and utilization (CCU) technologies into wastewater treatment plants (WWTPs) is essential for mitigating greenhouse gas (GHG) emissions and enhancing environmental sustainability, but further advancements in process monitori...

Machine learning algorithms for predicting membrane bioreactors performance: A review.

Journal of environmental management
Membrane bioreactors (MBR) are recognized as a sustainable technology for treating polluted effluents. Machine learning (ML) algorithms have emerged as a modeling option to predict pollutant removal and operational variables such as membrane fouling,...