Department of Technology Management, University of Bridgeport, 221 University Avenue, School of Engineering, 141 Technology Building, Bridgeport, CT 06604, USA.
Departments of Mechanical Engineering and Technology Management, University of Bridgeport, 221 University Avenue, School of Engineering, 141 Technology Building, Bridgeport, CT 06604, USA.
Waste Manag. 2019 Jul 15;95:241-249. doi: 10.1016/j.wasman.2019.06.023. Epub 2019 Jun 17.
Rapid and revolutionary changes in technology and rising demand for consumer electronics have led to staggering rates of accumulation of electrical and electronic equipment waste, viz., WEEE or e-waste. Consequently, e-waste has become one of the fastest growing municipal solid waste streams in the United States making its efficient management crucial in supporting the efforts to create and sustain green cities. Accurate estimations on the amount of e-waste might help in increasing the efficiency of waste collection, recycling and disposal operations that have become more complicated and unpredictable. Early work focusing on prediction of e-waste generation includes a wide range of methodologies. Among these, grey forecasting models have drawn attention due to their capability to provide meaningful results with relatively small-sized or limited data. The performance of grey models heavily rely on their parameters. The purpose of this study is to present a novel forecasting technique for e-waste predictions with multiple inputs in presence of limited historical data. The proposed nonlinear grey Bernoulli model with convolution integral NBGMC(1,n) improved by Particle Swarm Optimization (PSO) demonstrates superior accuracy over alternative forecasting models. The proposed model and its findings are delineated with the help of a case study utilizing Washington State e-waste data. The results indicate that population density has a major impact on the generated e-waste followed by household income level. The findings also show that the e-waste generation forms a saturated distribution in Washington State. These results can help decision makers plan for more effective reverse logistics infrastructures that would ensure proper collection, recycling and disposal of e-waste.
技术的快速变革和消费者对电子产品需求的增长，导致电子电气设备废物（WEEE 或电子废物）的积累速度惊人。因此，电子废物已成为美国增长最快的城市固体废物之一，其有效管理对于支持创建和维持绿色城市的努力至关重要。准确估计电子废物的数量可能有助于提高废物收集、回收和处置作业的效率，这些作业变得更加复杂和不可预测。早期专注于预测电子废物产生量的工作包括各种方法。其中，灰色预测模型因其能够用相对较小或有限的数据提供有意义的结果而引起了关注。灰色模型的性能在很大程度上依赖于其参数。本研究旨在提出一种在有限历史数据存在下具有多输入的电子废物预测的新预测技术。通过粒子群优化（PSO）改进的具有卷积积分的非线性灰色伯努利模型 NBGMC(1,n) 表现出优于替代预测模型的准确性。借助利用华盛顿州电子废物数据的案例研究，阐述了提出的模型及其发现。结果表明，人口密度对产生的电子废物有重大影响，其次是家庭收入水平。研究结果还表明，电子废物的产生在华盛顿州形成了饱和分布。这些结果可以帮助决策者规划更有效的逆向物流基础设施，以确保电子废物的妥善收集、回收和处置。
