Electronic Solid Waste Prediction Based on Intelligent Optimization Grey Model

doi:10.16182/j.issn1004731x.joss.20-0833

Abstract

Abstract:

Aiming at the problems of complex modeling mechanism and low modeling accuracy in the prediction of electronic solid waste production, an intelligent modeling method combining fractional order multiple gray model and neural network compensation model is proposed. Particle swarm optimization is used to optimize the accumulative order and background parameters of the gray model to maximize the performance of the gray model. BP neural network is used to compensate the error of gray modeling and improve the prediction accuracy of solid waste production. The effectiveness of the proposed method is verified by Washington state electronic solid waste data. The accurate estimation of electronic solid waste production provides reference for infrastructure planning and process optimization of electronic solid waste recovery.

Key words: electronic solid waste prediction, hybrid intelligent modeling, fractional multiple gray model, BP neural networks, particle swarm optimization

CLC Number:

TP391.9

Xiaoan Sun, Xiaoli Luan, Fei Liu. Electronic Solid Waste Prediction Based on Intelligent Optimization Grey Model[J]. Journal of System Simulation, 2022, 34(3): 536-542.

Figures/Tables 7

Fig.1

Table1

Configuration of the PSO algorithm

群体个体数目	最大迭代次数	学习因子1	学习因子2	动态惯性权重因子	粒子范围
30	300	2	2	0.75	$0 < ω i < 1 0 ≤ r ≤ 1$

Table1

Fig. 2

Table 2

Data set of electronic solid waste generation from Washington

年份	人口密度/ ( $人 ? m i - 2$ )	家庭收入中位数(美元)	固废产生量/t
2003	92.07	46 967.25	18 108.19
2004	93.30	49 585.35	27 341.56
2005	94.66	50 004.20	35 887.90
2006	96.48	53 522.31	46 126.41
2007	98.06	56 141.32	53 737.51
2008	99.31	57 857.52	62 071.46
2009	100.27	55 458.05	69 246.27
2010	101.19	54 888.45	68 777.91
2011	101.84	55 500.26	69 673.02
2012	102.59	56 443.76	73 851.24
2013	103.56	57 283.94	65 894.78
2014	104.85	60 153.00	67 822.93
2015	106.26	63 439.20	72 103.41
2016	108.10	65 500.29	N/A
2017	110.00	68 550.43	N/A

Table 2

Table 3

GMC(1,3)and the optimizedFAGMC(1,3)results

方法	MAPE/%	$w 1$	$w 2$	$w 3$	$r$
$G M C (1,3)$	2.989	0.5	0.5	0.5	N/A
优化后的 $F A G M C (1,3)$	2.792	0.394	0.580	0.534	1

Table 3

Fig. 3

Table 4

Comparison of relative error of estimated values via three models

模型	预测值/t	相对误差/%
传统 $G M C (1, n)$	77 758.017	7.842
$G M C (1, n) - P S O$	76 952.166	6.301
混合模型	73 584.563	1.777

Table 4

References 15

1	张厚明. 电子垃圾回收利用对策研究[J]. 中国国情国力, 2014(5): 50-52.
	Zhang Houming. Countermeasure Research on Recycling and Utilization of Electronic Waste[J]. China National Condtions and Strength, 2014(5): 50-52.
2	Gupta Surendra M. Reverse Supply Chains: Issues and Analysis[M]. Florida: CRC Press, 2013.
3	洪鸿加, 彭晓春, 陈志良, 等. 系统动力学模型在电子废弃物产生量预测中的应用——以广州市废旧电脑为例[J]. 环境污染与防治, 2009, 31(10): 83-86.
	Hong Hongjia, Peng Xiaochun, Chen Zhiliang, et al. Application of System Dynamics Model in the Prediction of E-Waste Production: a Case Study of Waste Computer in Guangzhou[J]. Environmental Pollution & Control, 2009, 31(10): 83-86.
4	郭晓倩, 孟伟庆, 汲奕君. 基于改进logistic模型的天津市电子废弃物产生量预测[J]. 环境科学与技术, 2014, 37(3): 188-193.
	Guo Xiaoqian, Meng Weiqing, Ji Yijun. Estimation of Electrical and Electronic Equipment Waste Generation Using a Modified Logistic Model in Tianjin City[J]. Environmental Science and Technology, 2014, 37(3): 188-193.
5	江晓健, 刘静欣, 严康, 等. 中国电子废弃物产生量预测及金属积存量特征分析[J]. 有色金属科学与工程, 2016, 7(5): 104-109.
	Jiang Xiaojian, Liu Jingxin, Yan Kang, et al. Prediction of Electronic Waste Amount and Metal Cumulative Amount Features[J]. Nonferrous Metals Science and Engineering, 2016, 7(5): 104-109.
6	Zhao Min, Zhao Chen, Yu Luling, et al. Prediction and Analysis of WEEE in China Based on the Gray Model[J]. Procedia Environmental Sciences (S1878-0296), 2016, 31: 925-934.
7	Ma Xin, Liu Zhibin. The GMC(1,n) Model with Optimized Parameters and Its Application[J]. Journal of Grey System (S1028-9488), 2017, 29: 122-138.
8	Gazi Murat Duman, Kongar Elif, Gupta Surendra M. Estimation of Electronic Waste Using Optimized Multivariate Grey Models[J]. Waste Management (S0956-053X), 2019, 95: 241-249.
9	Hsu Lichang, Wang Chaohung. Forecasting Integrated Circuit Output Using Multivariate Grey Model and Grey Relational Analysis[J]. Expert Systems With Applications (S0957-4174), 2009, 36(2): 1403-1409.
10	Wu Lifeng, Liu Sifeng, Yao Ligen, et al. Grey System Model with the Fractional Order Accumulation[J]. Communications in Nonlinear Science and Numerical Simulation (S1007-5704), 2013, 18(7): 1775-1785.
11	袁德宝, 张振超, 张军, 等. 最优化分数阶算子EGM(1, 1)模型在变形监测预报中的应用[J]. 大地测量与地球动力学, 2020, 40(4): 331-334.
	Yuan Debao, Zhang Zhenchao, Zhang Jun, et al. Application of Optimized Fractional Order EGM(1,1) Model in Deformation Monitoring and Forecasting[J]. Journal of Geodesy and Geodynamics, 2020, 40(4): 331-334.
12	张鹏. 小样本时间序列灰色预测关键技术研究[D]. 成都: 电子科技大学, 2020.
	Zhang Peng. Research on Key Techniques of Grey Prediction for Small Sample Time Series[D]. Chengdu: University of Electronic Science and Technology of China, 2020.
13	Zeng Bo, Li Chuan. Improved Multi-Variable Grey Forecasting Model with a Dynamic Background-Value Coefficient and Its Application[J]. Computers & Industrial Engineering (S0360-8352). 2018, 118: 278-290.
14	Zhang Liping, Zheng Yanling, Wang Kai, et al. 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[J]. Computers in Biology and Medicine (S0010-4825), 2014, 49: 67-73.
15	刘宇, 黎蕾蕾, 刘俊, 等. 压电陀螺漂移特性的灰色神经网络建模研究[J]. 系统仿真学报, 2007, 19(20): 4676-4679.
	Liu Yu, Li Leilei, Liu Jun, et al. Study on Network Drift Modeling for Piezoelectic Gyro. Journal of System Simulation[J]. Journal of System Simulation, 2007, 19(20): 4676-4679.

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