AIMC Topic: Water Purification
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An Integrated First Principal and Deep Learning Approach for Modeling Nitrous Oxide Emissions from Wastewater Treatment Plants.
Environmental science & technology
Feb 2, 2022
Mathematical modeling plays a critical role toward the mitigation of nitrous oxide (NO) emissions from wastewater treatment plants (WWTPs). In this work, we proposed a novel hybrid modeling approach by integrating the first principal model with deep ...
Water Quality Indicator Interval Prediction in Wastewater Treatment Process Based on the Improved BES-LSSVM Algorithm.
Sensors (Basel, Switzerland)
Jan 6, 2022
This paper proposes a novel interval prediction method for effluent water quality indicators (including biochemical oxygen demand (BOD) and ammonia nitrogen (NH3-N)), which are key performance indices in the water quality monitoring and control of a ...
Occurrence and concentration of 20-100 μm sized microplastic in highway runoff and its removal in a gross pollutant trap - Bioretention and sand filter stormwater treatment train.
The Science of the total environment
Oct 21, 2021
Microplastic pollution of stormwater can be a serious threat to the environment. Gross pollutant trap (GPT) - bioretention treatment trains have been shown previously to treat (inter alia) particulate stormwater pollutants including microplastic part...
Towards better process management in wastewater treatment plants: Process analytics based on SHAP values for tree-based machine learning methods.
Journal of environmental management
Oct 15, 2021
Understanding the mechanisms of pollutant removal in Wastewater Treatment Plants (WWTPs) is crucial for controlling effluent quality efficiently. However, the numerous treatment units, operational factors, and the underlying interactions between thes...
A novel artificial intelligent model for predicting water treatment efficiency of various biochar systems based on artificial neural network and queuing search algorithm.
Chemosphere
Sep 20, 2021
This study aims at providing a robust artificial intelligent model for predicting the efficiency of heavy metal removal from aqueous solutions of biochar systems with high accuracy and reliability. Not only is it environmentally significant, but it i...
A novel two-step approach for optimal groundwater remediation by coupling extreme learning machine with evolutionary hunting strategy based metaheuristics.
Journal of contaminant hydrology
Aug 9, 2021
We propose a simulation-optimization (SO) model based on a novel two-step strategy for the optimal design of groundwater remediation systems. The SO models are developed by coupling simulation models directly or through the extreme learning machine (...
A multi-stage fuzzy decision-making framework to evaluate the appropriate wastewater treatment system: a case study.
Environmental science and pollution research international
May 25, 2021
Selection of appropriate treatment processes for wastewater treatment (WWT) plants at the design stage involves a careful examination of different economic, environmental, and social parameters. Designers and decision-makers seek a compromise among s...
Deep learning model for simulating influence of natural organic matter in nanofiltration.
Water research
Mar 20, 2021
Controlling membrane fouling in a membrane filtration system is critical to ensure high filtration performance. A forecast of membrane fouling could enable preliminary actions to relieve the development of membrane fouling. Therefore, we established ...
Real-time monitoring of forward osmosis membrane fouling in wastewater reuse process performed with a deep learning model.
Chemosphere
Feb 22, 2021
Monitoring fouling behavior for better understanding and control has recently gained increasing attention. However, there is no practical method for observing membrane fouling in real time, especially in the forward osmosis (FO) process. In this arti...
A novel two-step adaptive multioutput semisupervised soft sensor with applications in wastewater treatment.
Environmental science and pollution research international
Feb 7, 2021
To make full use of unlabeled data for soft-sensor modelling and to address the coexistence of a large number of hard-to-measure variable issues, this study proposed a novel two-step adaptive heterogeneous co-training multioutput model. First, unlabe...
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