Phytoremediation of nitrogen and phosphorus pollutants from glass industry effluent by using water hyacinth (Eichhornia crassipes (Mart.) Solms): Application of RSM and ANN techniques for experimental optimization

The present study aimed to assess the efficiency of the water hyacinth ( Eichhornia crassipes (Mart.) Solms) plant for the reduction of nitrogen and phosphorus pollutants from glass industry effluent (GIE) as batch mode phytoremediation experiments. For this, response surface methodology (RSM) and a...

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Veröffentlicht in:Environmental science and pollution research international Jg. 30; H. 8; S. 20590 - 20600
Hauptverfasser: Singh, Jogendra, Kumar, Pankaj, Eid, Ebrahem M., Taher, Mostafa A., El-Morsy, Mohamed H. E., Osman, Hanan E. M., Al-Bakre, Dhafer A., Kumar, Vinod
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2023
Schlagworte:
Aquatic Pollution
Atmospheric Protection/Air Quality Control/Air Pollution
Biodegradation, Environmental
Earth and Environmental Science
Ecotoxicology
Eichhornia
Eichhornia crassipes
Environment
Environmental Chemistry
Environmental Health
Environmental Pollutants
glass
industry
logit analysis
Neural Networks, Computer
Nitrogen
Phosphorus
phytoremediation
plant density
Plants
prediction
Research Article
response surface methodology
total phosphorus
Waste Water Technology
Water Management
Water Pollutants, Chemical - analysis
Water Pollution Control
Artificial neural network
Response surface method
Glass industry effluent
Phytoremediation
Eichhornia crassipes
ISSN:1614-7499, 1614-7499
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Zusammenfassung:The present study aimed to assess the efficiency of the water hyacinth ( Eichhornia crassipes (Mart.) Solms) plant for the reduction of nitrogen and phosphorus pollutants from glass industry effluent (GIE) as batch mode phytoremediation experiments. For this, response surface methodology (RSM) and artificial neural networks (ANN) methods were adopted to evidence the optimization and prediction performances of E . crassipes for total Kjeldahl’s nitrogen (TKN) and total phosphorus (TP) removal. The control parameters, i.e., GIE concentration (0, 50, and 100%) and plant density (1, 3, and 5 numbers) were used to optimize the best reduction conditions of TKN and TP. A quadratic model of RSM and feed-forward backpropagation algorithm-based logistic model (input layer: 2 neurons, hidden layer: 10 neurons, and output layer: 1 neuron) of ANN showed good fitness results for experimental optimization. Optimization results showed that maximum reduction of TKN (93.86%) and TP (87.43%) was achieved by using 60% of GIE concentration and nearly five plants. However, coefficient of determination (R 2 ) values showed that ANN models (TKN: 0.9980; TP: 0.9899) were superior in terms of prediction performance as compared to RSM (TKN: 0.9888; TP: 0.9868). Therefore, the findings of this study concluded that E . crassipes can be effectively used to remediate nitrogen and phosphorus loads of GIE and minimize environmental hazards caused by its unsafe disposal.
Bibliographie:ObjectType-Article-1
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ISSN:1614-7499
1614-7499
DOI:10.1007/s11356-022-23601-9