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Polyethylene (PE) is one of the most multipurpose materials in plastics. It has diverse practical fundamental properties (e.g., mechanical properties, chemical stability and so on), and is thereby of diverse capability and moreover, of low cost. It is used for various purposes such as kitchen utensils, toys, general cargoes and so on. However, according to each purpose, different kinds of PE properties are required. As a result, PEs of various densities such as high-density polyethylene (HDPE), low-density polyethylene (LDPE) and linear low-density polyethylene (LLDPE) are produced. For advanced plastic recycling, a technique for discriminating various densities of PE is indispensable.

The density of plastics is determined by infrared spectroscopic analysis, differential scanning calorimetry (DSC) and nuclear magnetic resonance (NMR) spectroscopic analysis. Rapid and non-destructive analysis of the density is needed for plastic recycling. With those techniques, the density cannot be analyzed quickly and non-destructively. Williams and Everall, and Shimoyama et. al. have reported that PE density was determined by near-infrared (NIR) or Raman spectroscopy combined with chemometrics [1 - 4]. Only NIR spectra measurement is applicable for plastic recycling, because it can be done quickly and non-destructively.

The combined technique of NIR spectroscopy and chemometrics analysis has been investigated for more than two decades [1 - 14]. In plastic recycling though, it is also important to analyze more precisely. The amount of information provided by NIR spectra is difficult to detect, but it is an essential and key technique.

The neural network technique has substantial advantages for spectral discrimination. Using this technique, the little difference in information obtained from NIR spectra becomes discriminable. In our previous study, we have already succeeded in developing techniques to discriminate more than 50 different kinds of plastic patterns [15] and to separate PE grades [16]. In the present paper, we propose a calibration model which predicts precise PE density using this approach.

Table 1. Density and MFR of PE samples examined in the present study.

Sample No. | Grades | Density (g cm^{3}) | MFR (g 10min^{-1}) | Sample No. | Grades | Density (g cm^{3}) | MFR (g 10min^{-1}) | |
---|---|---|---|---|---|---|---|---|

1 | LLDPE | 0.920 | 2 | 13 | LDPE | 0.925 | 2.8 | |

2 | LLDPE | 0.920 | 2.1 | 14 | LDPE | 0.930 | 1.5 | |

3 | LLDPE | 0.929 | 100 | 15 | HDPE | 0.944 | 0.04 | |

4 | LLDPE | 0.930 | 1.5 | 16 | HDPE | 0.946 | 4 | |

5 | LLDPE | 0.935 | 5 | 17 | HDPE | 0.947 | 42 | |

6 | LLDPE | 0.935 | 5.5 | 18 | HDPE | 0.950 | 0.25 | |

7 | LDPE | 0.898 | 3.5 | 19 | HDPE | 0.950 | 1 | |

8 | LDPE | 0.905 | 3.5 | 20 | HDPE | 0.950 | 40 | |

9 | LDPE | 0.918 | 1 | 21 | HDPE | 0.952 | 10.5 | |

10 | LDPE | 0.918 | 2 | 22 | HDPE | 0.960 | 1 | |

11 | LDPE | 0.918 | 4 | 23 | HDPE | 0.962 | 36 | |

12 | LDPE | 0.918 | 7 |

Figure 1. Normalized spectra from sample of maximum density (a), it's second-derivative spectra (c), normalized spectra from sample of minimum density (b) and it's second-derivative spectra (d).

The NEUROSIM/L (version 3.2) software program (Fujitsu Ltd., Japan) was employed for training of a neural network. In comparison, principal component regression (PCR) and partial least squares (PLS) regression were attempted for making a calibration model that predicts the density. The Pirouette (version 3.0) software program (InfoMetrix Inc., USA) was applied for PCR and PLS regression. In the neural network, PCR and PLS regression analysis, a leave-one-out cross-validation was performed. That is, the test was performed using one sample (five spectra) after training using twenty-two of twenty-three samples (110 of 115 spectra).

Figure 2. A score plot of principal component analysis of factor 1 and factor 2 from the second-derivative spectra.

Figure 3. A loadings plot of factor 1 (a) and factor 2 (b) for the model shown in Figure 2.

Figure 4. A neural network analysis for predicting the polyethylene density from the second-derivative spectra.

Figure 5. A partial least-squares regression calibration model with four factors for predicting the polyethylene density from the second-derivative spectra.

Algorithm | R | RMSEP (g cm^{-3}) |
---|---|---|

Neural Network | 1.000 | 0.00026 |

PCR^{a} | 0.968 | 0.0043 |

PLS Regression^{a} | 0.983 | 0.0031 |

It is known that the PE density is determined by the number and kinds of branches attached to the main chain of PE. The larger the number of branched methyl, ethyl, or butyl groups, the lower the density. A loadings plot of regression coefficients for the model shown in Figure 5 is presented in Figure 6. This plot is similar to the loadings plot factor 2 in Figure 3(b). The plot shows one upward peak at 1.68 mm and two downward peaks at 1.70 and 1.75 mm. One upward peak can be assigned to methyl groups [2], while two downward peaks can be assigned to methylene groups [2]. Peaks at 1.19 and 1.37 mm correspond to methyl groups [2]. Other peaks at 1.16, 1.39 and 1.53 mm correspond to methylene groups [2]. As can be seen from the above results, the kinds of branches can be detected from the NIR spectra.

Figure 6. A loadings plot of regression coefficients for the model shown in Figure 5.

Shimoyama et.al [2] reported PLS calibration of NIR data of LLDPE (16 samples) with densities between 0.911 and 0.925 g cm^{-3}. They obtained the values with R of 0.965 and SEP of 0.001 g cm^{-3}. We have shown that similar results can be obtained even within a much wider range of densities (0.898-0.962) and more samples (23 samples).

On the other hand, NIR spectroscopy doesn't have stronger absorption than IR. Overtone and combination bands appear with adequate intensities in NIR spectra. So NIR spectra can be measured quickly and non-destructively without sample preparation. NIR is suited to plastic recycling.

We have shown that a little difference in NIR spectra was detected by neural network analysis. We have developed a rapid and non-destructive technique to predict PE density by combining those methods.

Figure 7 presents PE MFRs predicted for 115 NIR spectra by neural network. A good straight line could be obtained between the actual and predicted MFRs. Values with R of 1.000 and relative RMSEP of 0.038 g 10min

Figure 7. A neural network analysis for predicting the polyethylene MFR from the second-derivative spectra.

Formerly, it was thought that the difference of MFR does not appear in NIR spectra. Small differences of spectra due to the difference of MFR could be detected by neural network analysis. We have developed a technique to determine MFR quickly and non-destructively.

The neural network analysis has given good results for density prediction with R of 1.000 and RMSEP of 0.00026 g cm

The present result is quite important from the point of view of plastic recycling. Our technique can be applied rapidly and non-destructively. This combined technique using NIR spectroscopy and neural network analysis is helpful in material recycling of plastics. Moreover, the NIR spectroscopic device used in this study is small and is of low price. So this is an applicable in-situ measurement in a plastic waste disposal station. Furthermore, this technique has the possibility to predict densities of plastics other than PE. In this study, the possibility to develop a practical plastic discrimination system using the above approach has been demonstrated.

The authors are grateful to Mr. Y. Gotoh of the Center for Analytical Chemistry and Science, Inc. for the kind donation of the polyethylene samples.

[ 2] M. Shimoyama, T. Ninomiya, K. Sano, Y. Ozaki, H. Higashiyama, M. Watari, and M. Tomo,

[ 3] K. Sano, M. Shimoyama, M. Ohgane, H. Higashiyama, M. Watari, M. Tomo, T. Ninomiya, and Y. Ozaki,

[ 4] Y. Ozaki, S. Sasic, H. Sato, T. Kamiya, T. Amari, M. Shimoyama, and T. Ninomiya,

[ 5] H. W. Siesler, and K. Holland-Moriz,

[ 6] D. A. Burns, and E.W. Ciurcziak Ed.,

[ 7] C. E. Miller,

[ 8] M. K. Alam, S. L. Santon, and G. A. Hebner,

[ 9] D. Wienke, W. van den Broek, W. Melssen, L. Buydens, R. Feldhoff, T. Kantimm, T. Huth-Fehre, L. Quick, F. Winter, and K. Cammann,

[10] D. M. Scott, and R. L. Waterland,

[11] D. Wienke, W. van den Broek, L. Buydens, T. Huth-Fehre, R. Feldhoff, T. Kantimm, and K. Cammann,

[12] R. Feldhoff, D. Wienke, K. Cammann, and H. Fuchs,

[13] W. van den Broek, D. Wienke, W. Melssen, and L. Buydens,

[14] J. S. Lee, and H. Chung,

[15] T. Matsumoto, K. Tanabe, K. Saeki, T. Amano, and H. Uesaka,

[16] K. Saeki, T. Matsumoto, K. Tanabe, and T. Amano,

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