Biochar performance evaluation for heavy metals removal from industrial wastewater based on machine learning: Application for environmental protection
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- @Article{DASHTI:2023:seppur,
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author = "Amir Dashti and Mojtaba Raji and
Hossein {Riasat Harami} and John L. Zhou and Morteza Asghari",
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title = "Biochar performance evaluation for heavy metals
removal from industrial wastewater based on machine
learning: Application for environmental protection",
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journal = "Separation and Purification Technology",
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year = "2023",
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volume = "312",
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pages = "123399",
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keywords = "genetic algorithms, genetic programming, Biochar,
Heavy metals, Machine learning, Modeling, Wastewater
treatment",
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ISSN = "1383-5866",
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URL = "http://hdl.handle.net/10453/168317",
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URL = "https://www.sciencedirect.com/science/article/pii/S1383586623003076",
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DOI = "doi:10.1016/j.seppur.2023.123399",
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size = "7 pages",
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abstract = "Industrial wastewaters contaminated with heavy and
toxic metals cause serious risks to human health and
other forms of life. The performance of biochar for the
elimination of heavy metals has been acclaimed. It is
highly advantageous to develop efficient computational
methods to predict its biosorption performance. In this
research, the performance of four types of machine
learning methods including adaptive neuro fuzzy
inference system (ANFIS), coupled simulated
annealing-least squares support vector machine
(CSA-LSSVM), particle swarm optimization-ANFIS
(PSO-ANFIS) and genetic programming (GP) was evaluated.
The modeling was conducted on 44 types of biochar
reported in 353 datasets from heavy metal adsorption
experiments. All four models have demonstrated good
predictive performance, especially by LSSVM, GP and
PSO-ANFIS procedures. The correlation coefficient (R2)
values of test dataset for ANFIS, CSA-LSSVM, PSO-ANFIS,
and GP were 0.9428, 0.9832, 0.9712 and 0.9750. The
values of mean squared error (MSE) and average absolute
relative deviation (AARD) were 0.0020 and 0.36 for
CSA-LSSVM model which has the superior capability than
other models. The sensitivity analysis showed that the
key parameters in heavy metal removal by biochar were
the concentration ratio of heavy metals/biochar and
total carbon content in biochar. A MATLAB code was
developed to estimate the biosorption efficiency. Novel
equation based genetic programming assists researchers
to predict sorption yield of heavy metals by reducing
the costs and time. Analyzing the results of this
research can increase the understanding of researchers
towards the effective remediation of hazardous
chemicals in water resources",
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notes = "Separation Processes Research Group (SPRG), University
of Science and Technology of Mazandaran, Behshahr,
Iran",
- }
Genetic Programming entries for
Amir Dashti
Mojtaba Raji
Hossein Riasat Harami
John L Zhou
Morteza Asghari
Citations