AIMC Topic: Waste Disposal, Fluid

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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...

Recognizing the state of aerobic granular sludge over its life-cycle in a continuous-flow membrane bioreactor with an artificial intelligence approach.

Journal of environmental management
The continuous-flow aerobic granular sludge-membrane bioreactor (AGS-MBR) system represents an efficient and sustainable technology for wastewater treatment. AGS, a spherical or ellipsoidal granular sludge formed through microbial self-aggregation un...

Predicting Membrane Fouling of Submerged Membrane Bioreactor Wastewater Treatment Plants Using Machine Learning.

Environmental science & technology
Membrane fouling remains a significant challenge in the operation of membrane bioreactors (MBRs). Plant operators rely heavily on observations of filtration performance from noisy sensor data to assess membrane fouling conditions and lab-based protoc...

Machine learning-assisted prediction and identification of key factors affecting nitrogen metabolism for aerobic granular sludge.

Environmental research
To achieve higher denitrification efficiency with reduced energy consumption in aerobic granular sludge (AGS) system, a systematic evaluation of the carbon and nitrogen metabolism process for AGS under different stage is essential. Herein, this study...

Prediction of total phosphorus removal in hybrid constructed wetlands: a machine learning approach for rice mill wastewater treatment.

Water environment research : a research publication of the Water Environment Federation
Efficient prediction of pollutant concentrations in constructed wetlands is critical for optimizing treatment performance, yet existing methodologies often fail to account for the influence of meteorological conditions and flow rate variations in rea...

Comparative analysis of supervised learning models for effluent quality prediction in wastewater treatment plants.

PloS one
Effluent quality prediction is critical for optimizing Wastewater Treatment Plant (WWTP) operations, ensuring regulatory compliance, and promoting environmental sustainability. This study evaluates the performance of five supervised learning models-A...

Energy saving for air supply in a real WWTP: application of a fuzzy logic controller.

Water science and technology : a journal of the International Association on Water Pollution Research
An unconventional cascade control system, for the regulation of air supply in activated sludge wastewater treatment plants (WWTPs), was tested. The dissolved oxygen (DO) set point in the aeration tank was dynamically calculated based on effluent ammo...

Prediction of effluent quality in ICEAS-sequential batch reactor using feedforward artificial neural network.

Water science and technology : a journal of the International Association on Water Pollution Research
It is highly essential that municipal wastewater is treated before its discharge and reuse in order to meet the standard requirements for safe marine life and for farming and industries. It is beneficial to use reclaimed water, since availability of ...

Decision tree (DT), generalized regression neural network (GR) and multivariate adaptive regression splines (MARS) models for sediment transport in sewer pipes.

Water science and technology : a journal of the International Association on Water Pollution Research
Sediment deposition in sewers and urban drainage systems has great effect on the hydraulic capacity of the channel. In this respect, the self-cleansing concept has been widely used for sewers and urban drainage systems design. This study investigates...

Wastewater treatment plant performance analysis using artificial intelligence - an ensemble approach.

Water science and technology : a journal of the International Association on Water Pollution Research
In the present study, three different artificial intelligence based non-linear models, i.e. feed forward neural network (FFNN), adaptive neuro fuzzy inference system (ANFIS), support vector machine (SVM) approaches and a classical multi-linear regres...