Experimental coupled predictive modelling based recycling of waste printed circuit boards for maximum extraction of copper
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- @Article{YUN:2019:JCPa,
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author = "Liu Yun and Ankit Goyal and Vikas Pratap Singh and
Liang Gao and Xiongbin Peng and Xiaodong Niu and
Chin-Tsan Wang and Akhil Garg",
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title = "Experimental coupled predictive modelling based
recycling of waste printed circuit boards for maximum
extraction of copper",
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journal = "Journal of Cleaner Production",
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volume = "218",
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pages = "763--771",
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year = "2019",
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keywords = "genetic algorithms, genetic programming, Spent battery
mix, Metal recovery, Recycling, Bioleaching",
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ISSN = "0959-6526",
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DOI = "doi:10.1016/j.jclepro.2019.01.027",
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URL = "http://www.sciencedirect.com/science/article/pii/S0959652619300332",
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abstract = "The recycling process of materials from used and
wasted printed circuit boards plays an important role
in electronic waste management. These waste printed
circuit boards (PCBs) hold metals such as copper,
aluminium, nickel, and magnesium. The efficient
recovery process of such metals from waste PCBs is
needed for recycle and possible reuse for manufacturing
of products. The metal recovery process is complex and,
multidimensional and costly to perform. In addition,
the efficient (maximum) recovery of metals exhibit
higher dependence on determination of optimum
combination of inputs in the recovery process from
waste PCBs. Therefore, this work illustrated the
ability of four predictive modelling methods (Analysis
of Variance, Genetic Programming, Artificial Neural
Network and Generalized Neural Network) to model
complex suspension electrolysis process (recovery
process) and their comparative analysis on recovery of
copper metal from waste PCBs. Experiments were designed
based on variations of three design/input parameters
such as concentration of sulfuric acid, concentration
of copper sulphate and current density. The comparative
analysis of the four methods mentioned above reveals
that Generalized Neural Network performed the best with
coefficient of determination value at 0.92.",
- }
Genetic Programming entries for
Liu Yun
Ankit Goyal
Vikas Pratap Singh
Liang Gao
Xiongbin Peng
Xiaodong Niu
Chin-Tsan Wang
Akhil Garg
Citations