Forecasting of Disassembly Waste Generation under Uncertainties Using Digital Twinning-Based Hidden Markov Model
Abstract
:1. Introduction
2. Problem Description and Modeling
2.1. DG-HMM Modeling
2.2. Performance Measures
3. Theoretical Methods
3.1. Data Pretreatment
3.2. Baum–Welch Algorithm
4. Model Verification
4.1. Parameter Setting
4.2. Disassembly Waste Generation Prediction
4.2.1. Computational Complexity Analysis
4.2.2. Prediction Reliability Analysis
4.2.3. Comparison Plots of the Forecast and Observed Value
4.3. Comparative Analysis
5. Conclusions and Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Comparison Model | Parameters Setting |
---|---|
ANN | Learning rate 0.1; the transfer function of hidden layer is tansig; the wrong target is 0.0001; the maximum number of iterations is 10,000; the hidden node is 3; the output node is 1. |
SVM | Radial basis function is used as the best kernel function. In the coarse grid search, the parameters of the gamma function and the penalty parameters are set to the range [2−8, 28]. The steps of coarse grid search and fine grid search are set to 1 and 0.2, respectively. |
ARIMA | The most suitable ARIMA model parameters are (1, 1, 1). |
ES | Using a multiplicative algorithm to predict the amount of waste generated during disassembly. |
Monday | Tuesday | Wednesday | Thursday | Friday | Saturday | |
---|---|---|---|---|---|---|
Sample size | 53 | 53 | 52 | 52 | 52 | 52 |
Normal(Mean/SD) | 3.532/2.753 | 3.364/2.517 | 3.826/2.964 | 2.963/1.857 | 4.178/3.135 | 3.584/2.822 |
Most extreme differences | 0.121 | 0.094 | 0.135 | 0.087 | 0.096 | 0.105 |
Kolmogorov–Smirnov | 0.872 | 0.944 | 0.817 | 1.016 | 1.039 | 0.936 |
Asymp. Sig.(2-tailed) | 0.535 | 0.438 | 0.324 | 0.420 | 0.238 | 0.341 |
NHS | NGMC | MLE | AIC | NHS | NGMC | MLE | AIC | NHS | NGMC | MLE | AIC |
---|---|---|---|---|---|---|---|---|---|---|---|
3 | 1 | 388 | −168 | 3 | 3 | 325 | −63 | 2 | 2 | 397 | −185 |
2 | 2 | 306 | −49 | 2 | 1 | 359 | −102 | 3 | 3 | 364 | −124 |
3 | 3 | 385 | −153 | 2 | 2 | 312 | −58 | 2 | 1 | 372 | −135 |
2 | 1 | 328 | −68 | 3 | 3 | 355 | −94 | 3 | 2 | 393 | −174 |
3 | 2 | 341 | −73 | 3 | 1 | 369 | −127 | 3 | 3 | 377 | −146 |
ARMA | ES | ANN | HMM | DG-HMM | |
---|---|---|---|---|---|
MAE | 18.34 | 19.60 | 20.47 | 16.83 | 14.35 |
R2 | 0.875 | 0.892 | 0.899 | 0.906 | 0.913 |
Ȓ2 | 0.861 | 0.890 | 0.874 | 0.898 | 0.911 |
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Yang, Y.; Yuan, G.; Cai, J.; Wei, S. Forecasting of Disassembly Waste Generation under Uncertainties Using Digital Twinning-Based Hidden Markov Model. Sustainability 2021, 13, 5391. https://doi.org/10.3390/su13105391
Yang Y, Yuan G, Cai J, Wei S. Forecasting of Disassembly Waste Generation under Uncertainties Using Digital Twinning-Based Hidden Markov Model. Sustainability. 2021; 13(10):5391. https://doi.org/10.3390/su13105391
Chicago/Turabian StyleYang, Yinsheng, Gang Yuan, Jiaxiang Cai, and Silin Wei. 2021. "Forecasting of Disassembly Waste Generation under Uncertainties Using Digital Twinning-Based Hidden Markov Model" Sustainability 13, no. 10: 5391. https://doi.org/10.3390/su13105391