AIMC Topic: Waste Management

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Artificial Intelligence for the Evaluation of Operational Parameters Influencing Nitrification and Nitrifiers in an Activated Sludge Process.

Microbial ecology
Nitrification at a full-scale activated sludge plant treating municipal wastewater was monitored over a period of 237 days. A combination of fluorescent in situ hybridization (FISH) and quantitative real-time polymerase chain reaction (qPCR) were use...

Impact of pH Management Interval on Biohydrogen Production from Organic Fraction of Municipal Solid Wastes by Mesophilic Thermophilic Anaerobic Codigestion.

BioMed research international
The biohydrogen productions from the organic fraction of municipal solid wastes (OFMSW) were studied under pH management intervals of 12 h (PM12) and 24 h (PM24) for temperature of 37 ± 0.1°C and 55 ± 0.1°C. The OFMSW or food waste (FW) along with it...

Gross parameters prediction of a granular-attached biomass reactor by means of multi-objective genetic-designed artificial neural networks: touristic pressure management case.

Environmental science and pollution research international
The Artificial Neural Networks by Multi-objective Genetic Algorithms (ANN-MOGA) model has been applied to gross parameters data of a Sequencing Batch Biofilter Granular Reactor (SBBGR) with the aim of providing an effective tool for predicting the fl...

Prediction of municipal solid waste generation using nonlinear autoregressive network.

Environmental monitoring and assessment
Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generati...

Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province, Iran.

Waste management (New York, N.Y.)
Predicting the mass of solid waste generation plays an important role in integrated solid waste management plans. In this study, the performance of two predictive models, Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) was verifi...

Managing waste for production of low-carbon concrete mix using uncertainty-aware machine learning model.

Environmental research
This study introduces an uncertainty-aware AI-driven optimization framework for designing sustainable concrete mixtures that incorporate waste-derived materials. The primary objectives are to reduce global warming potential (GWP) and promote a circul...

Enhancing waste recognition with vision-language models: A prompt engineering approach for a scalable solution.

Waste management (New York, N.Y.)
Conventional unimodal computer vision models, trained on limited bespoke waste datasets, face significant challenges in classifying waste images in material recovery facilities, where waste appears in diverse forms. Maintaining performance of these m...

Automated Electro-construction waste Sorting: Computer vision for part-level segmentation.

Waste management (New York, N.Y.)
The global generation of construction, demolition, and renovation (CDR) waste has surged, increasing the demand for efficient recycling solutions. Emerging technologies can automate the sorting of CDR waste, which is crucial for specialised categorie...

Plastics detection and sorting using hyperspectral sensing and machine learning algorithms.

Waste management (New York, N.Y.)
Plastic waste second life management requires effective detection (and sorting if necessary) techniques to tackle the environmental challenge it poses. This research explores the application of hyperspectral imaging in the spectral range 900-1700 nm ...

An analytic hierarchy process combined with artificial neural network model to evaluate sustainable sludge treatment scenarios.

Waste management (New York, N.Y.)
Sludge management in China faces critical environmental, economic, and technical challenges, necessitating urgent optimal management strategy selection. Given the limited number of comprehensive studies on sludge management, quantitative decision-mak...