AIMC Topic: Biodegradation, Environmental
Clear Filters Showing 41 to 49 of 49 articles
A New Efficient Hybrid Intelligent Model for Biodegradation Process of DMP with Fuzzy Wavelet Neural Networks.
Scientific reports
Jan 25, 2017
A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment pr...
Application of fuzzy neural networks for modeling of biodegradation and biogas production in a full-scale internal circulation anaerobic reactor.
Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering
Sep 9, 2016
This paper presents the development and evaluation of three fuzzy neural network (FNN) models for a full-scale anaerobic digestion system treating paper-mill wastewater. The aim was the investigation of feasibility of the approach-based control syste...
Application of backpropagation artificial neural network prediction model for the PAH bioremediation of polluted soil.
Chemosphere
Jul 15, 2016
The backpropagation (BP) artificial neural network (ANN) is a renowned and extensively functional mathematical tool used for time-series predictions and approximations; which also define results for non-linear functions. ANNs are vital tools in the p...
Modelling the removal of volatile pollutants under transient conditions in a two-stage bioreactor using artificial neural networks.
Journal of hazardous materials
Mar 8, 2016
A two-stage biological waste gas treatment system consisting of a first stage biotrickling filter (BTF) and second stage biofilter (BF) was tested for the removal of a gas-phase methanol (M), hydrogen sulphide (HS) and α-pinene (P) mixture. The biore...
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...
Artificial neural network-based fungal chitin production for submicron-chitosan synthesis: effects on bioremediation for heavy metal pollution.
International journal of biological macromolecules
Jun 1, 2025
This study focused on optimizing fungal chitin (CT) production from a newly identified Fusarium incarnatum (GenBank: OL314753) for subsequent synthesis of submicron chitosan (sm-CS) tailored for enhanced heavy metal removal. Initial attempts to optim...
Microbial degradation potential of microplastics in urban river sediments: Assessing and predicting the enrichment of PE/PP-degrading bacteria using SourceTracker and machine learning.
Journal of environmental management
Jun 1, 2025
Microplastic mitigation strategies that adapt to various actual aquatic environments require the ability to predict their microbial degradation potential. However, the sources and enrichment characteristics of the degrading bacteria in the plastisphe...
XenoBug: machine learning-based tool to predict pollutant-degrading enzymes from environmental metagenomes.
NAR genomics and bioinformatics
Jun 1, 2025
Application of machine learning-based methods to identify novel bacterial enzymes capable of degrading a wide range of xenobiotics offers enormous potential for bioremediation of toxic and carcinogenic recalcitrant xenobiotics such as pesticides, pla...
Phytoremediation of palm oil mill secondary effluent (POMSE) by Chrysopogon zizanioides (L.) using artificial neural networks.
International journal of phytoremediation
May 4, 2017
Artificial neural networks (ANNs) have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the nonlinear relationships between variables in complex systems. In this study, ANN was applied...
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