Estimation of electronic waste using optimized multivariate grey models

Waste Management - Tập 95 - Trang 241-249 - 2019
Gazi Murat Duman1, Elif Kongar2, Surendra M. Gupta3
1Department of Technology Management, University of Bridgeport, 221 University Avenue, School of Engineering, 141 Technology Building, Bridgeport, CT 06604, USA
2Departments of Mechanical Engineering and Technology Management, University of Bridgeport, 221 University Avenue, School of Engineering, 141 Technology Building, Bridgeport, CT 06604, USA
3Department of Mechanical and Industrial Engineering, Northeastern University, 334 Snell Engineering Center, 360 Huntington Avenue, Boston, MA 02115, USA

Tài liệu tham khảo

Albuquerque, 2019, 127 Araújo, 2012, A model for estimation of potential generation of waste electrical and electronic equipment in Brazil, Waste Manage., 32, 335, 10.1016/j.wasman.2011.09.020 Barba-Gutiérrez, 2008, An analysis of some environmental consequences of European electrical and electronic waste regulation, Resour., Conserv. Recycling, 52, 481, 10.1016/j.resconrec.2007.06.002 Brunner, 2016 Chen, 2008, Application of the novel nonlinear grey Bernoulli model for forecasting unemployment rate, Chaos, Solitons Fractals, 37, 278, 10.1016/j.chaos.2006.08.024 Chen, 2008, Forecasting of foreign exchange rates of Taiwan’s major trading partners by novel nonlinear Grey Bernoulli model NGBM(1,1), Commun. Nonlinear Sci. Numer. Simul., 13, 1194, 10.1016/j.cnsns.2006.08.008 Chen, 2010, Forecasting Taiwan’s major stock indices by the Nash nonlinear grey Bernoulli model, Expert Syst. Appl., 37, 7557, 10.1016/j.eswa.2010.04.088 Chung, 2011, Generation of and control measures for, e-waste in Hong Kong, Waste Manage., 31, 544, 10.1016/j.wasman.2010.10.003 Diaz, 2018, Economic evaluation of an electrochemical process for the recovery of metals from electronic waste, Waste Manage., 74, 384, 10.1016/j.wasman.2017.11.050 Deng, 1989, Introduction to grey system theory, The Journal of Grey System., 1, 1 Eberhart, 1995, A new optimizer using particle swarm theory, MHS'95, 39 ecology.wa.gov, 2018. Electronics Recycling, Progress Reports. ecology.wa.gov. ElSayed, 2012, A robotic-driven disassembly sequence generator for end-of-life electronic products, J. Intelligent Robotic Syst., 68, 43, 10.1007/s10846-012-9667-8 EPA, 2007. Management of Electronic Waste in the United States Approach Two, EPA530-R-07-004b ed. U.S. Environmental Protection Agency. EPA, 2008. Electronics Waste Management in the United States: Approach I, EPA530-R-08-009 ed. U.S. Environmental Protection Agency. EPA, 2016. Electronic Products Generation and Recycling in the United States, 2013 and 2014, U.S. Environmental Protection Agency. Gupta, 2016 Hong, 2015, Life cycle assessment of electronic waste treatment, Waste Manage., 38, 357, 10.1016/j.wasman.2014.12.022 Hsu, 2009, Forecasting the output of integrated circuit industry using genetic algorithm based multivariable grey optimization models, Expert Syst. Appl., 36, 7898, 10.1016/j.eswa.2008.11.004 Hsu, 2010, A genetic algorithm based nonlinear grey Bernoulli model for output forecasting in integrated circuit industry, Expert Syst. Appl., 37, 4318, 10.1016/j.eswa.2009.11.068 Hsu, 2009, Forecasting integrated circuit output using multivariate grey model and grey relational analysis, Expert Syst. Appl., 36, 1403, 10.1016/j.eswa.2007.11.015 Intharathirat, 2015, Forecasting of municipal solid waste quantity in a developing country using multivariate grey models, Waste Manage., 39, 3, 10.1016/j.wasman.2015.01.026 Jain, 2006, E-waste assessment methodology and validation in India, J. Mater. Cycles Waste Manage., 8, 40, 10.1007/s10163-005-0145-2 Kiddee, 2013, Electronic waste management approaches: an overview, Waste Manage., 33, 1237, 10.1016/j.wasman.2013.01.006 Kim, 2013, Estimating the amount of WEEE generated in South Korea by using the population balance model, Waste Manage., 33, 474, 10.1016/j.wasman.2012.07.011 Kinnaman, 2011, The environmental consequences of global reuse, Am. Econ. Rev., 101, 71, 10.1257/aer.101.3.71 Kumar, 2017, E-waste: An overview on generation, collection, legislation and recycling practices, Resour., Conserv. Recycling, 122, 32, 10.1016/j.resconrec.2017.01.018 Lau, 2013, A material flow analysis on current electrical and electronic waste disposal from Hong Kong households, Waste Manage., 33, 714, 10.1016/j.wasman.2012.09.007 Li, 2016, The improved grey model based on particle swarm optimization algorithm for time series prediction, Eng. Appl. Artificial Intellig., 55, 285, 10.1016/j.engappai.2016.07.005 Liu, 2007, Gray correlation analysis and prediction models of living refuse generation in Shanghai city, Waste Manage., 27, 345, 10.1016/j.wasman.2006.03.010 Liu, 2016, A rolling grey model optimized by particle swarm optimization in economic prediction, Comput. Intelligence, 32, 391, 10.1111/coin.12059 Ma, 2016, Research on the novel recursive discrete multivariate grey prediction model and its applications, Appl. Math. Model., 40, 4876, 10.1016/j.apm.2015.12.021 Ma, 2015, Predicting the oil field production using the novel discrete GM (1, N) model, Journal of Grey System, 27, 63 Ma, 2017, The GMC (1, n) model with optimized parameters and its application, J. Grey Syst., 29, 122 Ma, 2018, The kernel-based nonlinear multivariate grey model, Appl. Math. Model., 56, 217, 10.1016/j.apm.2017.12.010 Ma, 2018, Predicting the oil production using the novel multivariate nonlinear model based on Arps decline model and kernel method, Neural Comput. Appl., 29, 579, 10.1007/s00521-016-2721-x Ma, 2019, Application of a novel nonlinear multivariate grey Bernoulli model to predict the tourist income of China, J. Comput. Appl. Math., 347, 84, 10.1016/j.cam.2018.07.044 Matthews, H.S., McMichael, F.C., Hendrickson, C.T., Hart, D.J., 1997. Disposition and end-of-life options for personal computers. Carnegie Mellon University Green Design Initiative Technical Report. Nakamura, 2006, A waste input–output life-cycle cost analysis of the recycling of end-of-life electrical home appliances, Ecol. Econ., 57, 494, 10.1016/j.ecolecon.2005.05.002 ofm.wa.gov, 2018a. Washington State Office of Financial Management Median household income estimates. ofm.wa.gov, 2018b. Washington State Office of Financial Management Population Estimates. ofm.wa.gov, 2018c. Washington State Office of Financial Management State Population Forecast. Oguchi, 2008, Product flow analysis of various consumer durables in Japan, Resour., Conserv. Recycling, 52, 463, 10.1016/j.resconrec.2007.06.001 Ongondo, 2011, How are WEEE doing? A global review of the management of electrical and electronic wastes, Waste Manage., 31, 714, 10.1016/j.wasman.2010.10.023 Öztürk, 2015, Generation and management of electrical–electronic waste (e-waste) in Turkey, J. Mater. Cycles Waste Manage., 17, 411, 10.1007/s10163-014-0258-6 Pao, 2012, Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model, Energy, 40, 400, 10.1016/j.energy.2012.01.037 Petridis, 2016, Estimation of computer waste quantities using forecasting techniques, J. Clean. Prod., 112, 3072, 10.1016/j.jclepro.2015.09.119 Srivastava, 2006, Grey modelling of solid waste volumes in developing countries, 145 Steubing, 2010, Assessing computer waste generation in Chile using material flow analysis, Waste Manage., 30, 473, 10.1016/j.wasman.2009.09.007 Tien, 2005, The indirect measurement of tensile strength of material by the grey prediction model GMC (1, n), Measur. Sci. Technol., 16, 1322, 10.1088/0957-0233/16/6/013 Tien, 2009, The deterministic grey dynamic model with convolution integral DGDMC (1, n), Appl. Math. Model., 33, 3498, 10.1016/j.apm.2008.11.012 Tien, 2011, The indirect measurement of tensile strength by the new model FGMC (1, n), Measurement, 44, 1884, 10.1016/j.measurement.2011.08.029 Tien, 2012, A research on the grey prediction model GM (1, n), Appl. Math. Comput., 218, 4903 Vivekanand, 2019, Application of deterministic, stochastic and fuzzy linear programming models in solid waste management studies: literature review, J. Solid Waste Technol. Manage., 45, 68, 10.5276/JSWTM.2019.68 Wäger, 2011, Environmental impacts of the Swiss collection and recovery systems for Waste Electrical and Electronic Equipment (WEEE): a follow-up, Sci. Total Environ., 409, 1746, 10.1016/j.scitotenv.2011.01.050 Wang, 2008, Using genetic algorithms grey theory to forecast high technology industrial output, Appl. Math. Comput., 195, 256 Wang, 2009, Combination gray forecast model based on the ant colony algorithm, Math. Practice Theory, 14, 017 Wang, 2014, Nonlinear grey prediction model with convolution integral NGMC and its application to the forecasting of China’s industrial emissions, J. Appl. Math., 2014, 9 Wang, 2016, An improved grey multivariable model for predicting industrial energy consumption in China, Appl. Math. Model., 40, 5745, 10.1016/j.apm.2016.01.012 Wang, 2013, An optimized Nash nonlinear grey Bernoulli model for forecasting the main economic indices of high technology enterprises in China, Comput. Indust. Eng., 64, 780, 10.1016/j.cie.2012.12.010 Wang, 2011, An optimized NGBM(1,1) model for forecasting the qualified discharge rate of industrial wastewater in China, Appl. Math. Model., 35, 5524, 10.1016/j.apm.2011.05.022 Widmer, 2005, Global perspectives on e-waste, Environ. Impact Assess. Rev., 25, 436, 10.1016/j.eiar.2005.04.001 Xie, Q., Xie, Y., 2009. Forecast of Regional Gross National Product Based on Grey Modelling Optimized by Genetic Algorithm, E-Learning, E-Business, Enterprise Information Systems, and E-Government, 2009. In: International Conference on EEEE'09. IEEE, pp. 3–5. Xie, 2009, Research on gray prediction modeling optimized by genetic algorithm for energy consumption demand, Industrial Mechatronics and Automation, 2009, 289 Xu, 2013, A hybrid procedure for MSW generation forecasting at multiple time scales in Xiamen City, China, Waste Manage., 33, 1324, 10.1016/j.wasman.2013.02.012 Yang, 2009, Logistic model-based forecast of sales and generation of obsolete computers in the U.S, Technol. Forecast. Social Change, 76, 1105, 10.1016/j.techfore.2009.03.004 Zeng, 2018, Improved multi-variable grey forecasting model with a dynamic background-value coefficient and its application, Comput. Indust. Eng., 118, 278, 10.1016/j.cie.2018.02.042 Zeng, 2016, Development of an optimization method for the GM(1, N) model, Eng. Appl. Artificial Intellig., 55, 353, 10.1016/j.engappai.2016.08.007 Zeng, 2016, A novel multi-variable grey forecasting model and its application in forecasting the amount of motor vehicles in Beijing, Comput. Indust. Eng., 101, 479, 10.1016/j.cie.2016.10.009 Zhang, 2014, An optimized Nash nonlinear grey Bernoulli model based on particle swarm optimization and its application in prediction for the incidence of Hepatitis B in Xinjiang, China, Comput. Biol. Med., 49, 67, 10.1016/j.compbiomed.2014.02.008 Zhao, 2016, An optimized grey model for annual power load forecasting, Energy, 107, 272, 10.1016/j.energy.2016.04.009 Zhao, 2016, Using GM (1, 1) optimized by MFO with rolling mechanism to forecast the electricity consumption of inner mongolia, Appl. Sci., 6, 20, 10.3390/app6010020 Zhao, 2016, Prediction and analysis of WEEE in China based on the gray model, Proc. Environ. Sci., 31, 925, 10.1016/j.proenv.2016.02.113 Zhou, 2009, Parameter optimization of nonlinear grey Bernoulli model using particle swarm optimization, Appl. Math. Comput., 207, 292 Zhou, 2019, Robust linear programming and its application to water and environmental decision-making under uncertainty, Sustainability, 11, 33, 10.3390/su11010033