AIMC Topic: Wastewater
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Sequential dynamic artificial neural network modeling of a full-scale coking wastewater treatment plant with fluidized bed reactors.
Environmental science and pollution research international
Jun 7, 2015
This study proposed a sequential modeling approach using an artificial neural network (ANN) to develop four independent models which were able to predict biotreatment effluent variables of a full-scale coking wastewater treatment plant (CWWTP). Suita...
Prediction of effluent concentration in a wastewater treatment plant using machine learning models.
Journal of environmental sciences (China)
Apr 20, 2015
Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentratio...
Artificial neural network modelling of pharmaceutical residue retention times in wastewater extracts using gradient liquid chromatography-high resolution mass spectrometry data.
Journal of chromatography. A
Mar 30, 2015
The modelling and prediction of reversed-phase chromatographic retention time (tR) under gradient elution conditions for 166 pharmaceuticals in wastewater extracts is presented using artificial neural networks for the first time. Radial basis functio...
Generative deep learning model assisted multi-objective optimization for wastewater nitrogen to protein conversion by photosynthetic bacteria.
Bioresource technology
Sep 1, 2025
For decades, the photosynthetic bacteria (PSB)-based nitrogen treatment and valorization from wastewater have been explored. However, balancing nitrogen removal performance and resource recovery potential in PSB has remained a key unresolved issue fo...
Advances in sulfate-reducing bacteria-driven bioelectrolysis: mechanisms and applications in microbial electrolysis cell technology.
Environmental research
Aug 15, 2025
The discharge of sulfate-rich wastewater from chemical and pharmaceutical and food processing industries results in serious environmental problems that impact both the natural environment and human health. The conventional sulfate removal process usi...
DFT-assisted machine learning for polyester membrane design in textile wastewater recovery applications.
Water research
Jul 1, 2025
Resource recovery from textile wastewater has attracted increasing interest because it simultaneously addresses wastewater treatment and maximizes the utilization of the residual dyes. Although polyester membranes have demonstrated great potential fo...
Machine Learning Reveals Key Adsorption Mechanisms for Oxyanions Based on Combination of Experimental and Published Literature Data.
Environmental science & technology
Jun 10, 2025
The development of new adsorbents for water treatment often involves complex adsorption mechanisms, whose individual contributions are unclear, thereby limiting the understanding of adsorption driving forces, making it difficult to achieve precise de...
Federated Machine Learning Enables Risk Management and Privacy Protection in Water Quality.
Environmental science & technology
Jun 3, 2025
Real-time water quality risk management in wastewater treatment plants (WWTPs) requires extensive data, and data sharing is still just a slogan due to data privacy issues. Here we show an adaptive water system federated averaging (AWSFA) framework ba...
Ceasing sampling at wastewater treatment plants where viral dynamics are most predictable.
Epidemics
Jun 1, 2025
Wastewater sampling has been shown to be an effective tool for monitoring the dynamics of an infectious disease. During the COVID-19 pandemic, many sampling sites were opened in order to capture as much information as possible. However, with the pand...
Enhancing photocatalytic degradation of hazardous pollutants with green-synthesized catalysts: A machine learning approach.
Journal of environmental management
Jun 1, 2025
The effective removal of organic pollutants from wastewater necessitates the development of advanced photocatalytic materials. This study explores the application of machine learning algorithms to predict the degradation efficiency of PRM using green...
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