Experimental coupled predictive modelling based recycling of waste printed circuit boards for maximum extraction of copper

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@Article{YUN:2019:JCPa,

• 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",
• title = "Experimental coupled predictive modelling based recycling of waste printed circuit boards for maximum extraction of copper",
• journal = "Journal of Cleaner Production",
• volume = "218",
• pages = "763--771",
• year = "2019",
• keywords = "genetic algorithms, genetic programming, Spent battery mix, Metal recovery, Recycling, Bioleaching",
• ISSN = "0959-6526",
• DOI = "doi:10.1016/j.jclepro.2019.01.027",
• URL = "http://www.sciencedirect.com/science/article/pii/S0959652619300332",
• 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