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Sewage

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Sustainability prioritization of sewage sludge to energy scenarios with hybrid-data consideration: a fuzzy decision-making framework based on full consistency method and fusion ranking model.

Environmental science and pollution research international
This work proposed a novel mathematical framework for the sustainability assessment of sewage sludge to energy (SStE) scenarios, by resorting to fuzzy multi-criteria decision-making (MCMD) methods. In which, an evaluation system including twelve crit...

Optimal Selection of Sewage Treatment Technologies in Town Areas: A Coupled Multi-Criteria Decision-Making Model.

Environmental management
In recent years, the development of sewage treatment technologies has made many treatment options available in towns. Selecting the most appropriate alternative (MAA) can make the best use of existing resources to achieve the optimal effect, which ha...

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...

Machine learning-aided analyses of thousands of draft genomes reveal specific features of activated sludge processes.

Microbiome
BACKGROUND: Microorganisms in activated sludge (AS) play key roles in the wastewater treatment processes. However, their ecological behaviors and differences from microorganisms in other environments have mainly been studied using the 16S rRNA gene t...

Predicting the higher heating value of syngas pyrolyzed from sewage sludge using an artificial neural network.

Environmental science and pollution research international
Sludge pyrolysis is a complex process including complicated reaction chemistry, phase transition, and transportation phenomena. To better evaluate the use of syngas, the monitoring and prediction of a higher heating value (HHV) is necessary. This stu...

An influent responsive control strategy with machine learning: Q-learning based optimization method for a biological phosphorus removal system.

Chemosphere
Biological phosphorus removal (BPR) is an economical and sustainable processes for the removal of phosphorus (P) from wastewater, achieved by recirculating activated sludge through anaerobic and aerobic (An/Ae) processes. However, few studies have sy...

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 ...

Coupling growth kinetics modeling with machine learning reveals microbial immigration impacts and identifies key environmental parameters in a biological wastewater treatment process.

Microbiome
BACKGROUND: Ubiquitous in natural and engineered ecosystems, microbial immigration is one of the mechanisms shaping community assemblage. However, quantifying immigration impact remains challenging especially at individual population level. The activ...

Comparative study of different wavelet-based neural network models to predict sewage sludge quantity in wastewater treatment plant.

Environmental monitoring and assessment
In this study, artificial neural networks (ANNs) including feed forward back propagation neural network (FFBP-NN) and the radial basis function neural network (RBF-NN) were applied to predict daily sewage sludge quantity in wastewater treatment plant...

Evaluation and optimization of anammox baffled reactor (AnBR) by artificial neural network modeling and economic analysis.

Bioresource technology
Anammox baffled reactor (AnBR) had a moderate start-up period of 53 days. Interestingly, tangled relationships between key parameters affecting anammox performance were observed, i.e., polynomial function for nitrogen loading rate (NLR) with extracel...