New Ways for the Advanced Quality Control of Liquefied Natural Gas
Abstract
:1. Introduction
2. Materials and Methods
2.1. Instrumentation
2.2. Gas Broadening
2.3. Peak Identification
2.4. Chemometric Predictive Models
2.4.1. Samples
2.4.2. Simplified Workflow for Model Development
- Stage 1: Preliminary assays: it is always important to visualize the spectra in order to evaluate gross differences among them, unusual signals, potential range of variables to be considered, presence of outlying samples, etc. A preliminary model can also be done to feel what its results look like and whether the samples spread through the working range of interest.
- Stage 2: spectra usually need what is called data pre-processing. This step attempts to get rid of useless information or undesired characteristics that may be detrimental for the predictions. For instance, baseline correction or noise filtering are typical steps before developing models. Very common pre-processings are the first derivative [16,64] (sometimes the 2nd derivative is also used) and mean centering [2]. Combinations of pre-processings are also common. Several options used for LNG modeling are shown in Table 3. This stage of model development is in general recommended, although some applications argued that no pre-processing was needed [4]. There is not a definitive answer to this issue, and the unique ‘true’ advice is to try different pre-processings and see which one improves the models.
- Stage 3: A critical point to develop a satisfactory PLS model (also critical for many other techniques that use abstract factors) is to ascertain the number of factors that must be included in the model (this is called model dimensionality). Although including many factors to take account of the majority of the variance (consider variance and information as synonymous for this purpose) contained into the spectra might seem to be a good idea, it is worth considering that those factors might be unrelated to the property of interest. They might be related to baseline effects or some spurious behavior of a peak, contributing to bad predictions. Of course, we also assume that too few factors will not be sufficient to extract enough relevant information to get a sound model. So, we need to equilibrate overfitting (too many factors) with underfitting (too few factors). One of the best options for this, though not perfect, consists of performing cross-validation. This empirically evaluates a cost function so that a minimum in the error is searched for [65]. Cross-validation is iterative and sometimes takes some computer time. Its conceptual idea can be resumed in a pseudo-code as follows:
- Fix a number of factors, let us say 1.
- Develop a model, and test how well it predicts those samples left out of it. The error can be stored in the computer memory.
- Reintegrate those spectra to the calibration set and extract another small subset of samples.
- Develop a model, test it with the second set of left-out spectra, and sum the error to the previous one.
- Continue the process until all samples (or possible subsets of samples) are excluded from the calibration stage and predicted afterwards. The summed errors yield the overall prediction error (=RMSECV).
- Return to 1 and increase the number of factors by one, and repeat the process again.
- Stage 4: A reason why developing a model is an iterative process is because we have to check for the existence of outlying samples. If they are present, all the previous stage is biased, and the model is not reliable. The problem is how to detect them. Likely, in the same way as you detect wrong points in a traditional calibration plot: by calculating statistics to evaluate the behavior of the samples into the model. Two of the most important and useful ones are the ‘Q residuals’ and the ‘Hotelling’s T2′ statistics [70] (another usual statistic is the leverage, although it is closely related to the T2 one). The former detects whether a spectrum has some new or different spectral characteristic(s) that could not be modeled with the present model (note: ‘different’ refers to its comparison with the residuals of the other calibration spectra in the model), whereas the second evaluates how close the spectrum is to the average spectrum of the calibration set. Clearly, we would like samples with spectra close to the average and without new spectral characteristics.
- Stage 5: The external validation set of samples is of use not only to assess that the model yields no overfitting but to evaluate how good the predictions of new samples are. A typical plot, such as that in Figure 12, is of most information as it yields insight on the closeness of the individual predictions to the true values, and samples predicted badly (possible outliers?), in addition to the overall average error.
3. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Window Material | Effective Range (cm−1) | Window Material | Effective Range (cm−1) |
---|---|---|---|
AgCl | 25,000–360 | KBr | 40,000–340 |
AMTIR (GeAsSe) | 11,000–625 | KRS-5 (TlBr + TlI) | 16,600–250 |
BaF2 | 50,000–740 | NaCl | 40,000–625 |
CaF2 | 50,000–1025 | Sapphire (Al2O3) | 50,000–1525 |
Chalcogenide (AsSeTe) | 4000–900 | Si | 8000–660 |
CsI | 33,000–200 | SiO2 | 50,000–2500 |
Diamond | 40,000–12.5 | ZnS | 17,000–690 |
Ge | 5500–475 | ZnSe | 10,000–550 |
Methane [40] | Ethane [41] | Propane [42,43] | |||||
Description | ῦ (cm−1) | Description | ῦ (cm−1) | Description | ῦ (cm−1) | Description | ῦ (cm−1) |
CH asymmetric stretch | 3019 | CH3 asymmetric stretch | 2985 | CH stretch | 2957 | CH3 & CH2 rocking | 1186 |
CH symmetric stretch | 2917 | CH3 symmetric stretch | 2895 | CH stretch | 2870 | CH3 wagging, deformation | 1155 |
CH symmetric bend | 1543 | CH3 Asymmetric deformation | 1469 | CH3 & CH2 scissoring | 1466 | C-C asymmetric stretch | 1051 |
CH asymmetric bend | 1311 | CH Symmetric deformation | 1379 | CH3 & CH wagging (in phase) | 1384 | CH3 & CCH deformation | 919 |
CH3 rocking | 821 | CH3 & CH wagging (out of phase) | 1368 | C-C symmetric stretching | 869 | ||
CH2 & CH wagging | 1331 | CH2 & CH3 twisting & rocking | 746 | ||||
n-Butane [41] | i-Butane [44] | ||||||
Description | ῦ (cm−1) | Description | ῦ (cm−1) | ||||
CH3 asymmetric stretch | 2968 | CH3 asymmetric stretch | 2968 | ||||
CH3 asymmetric stretch | 2965 | CH3 symmetric stretch | 2956 | ||||
CH3 asymmetric stretch | 2912 | CH3 symmetric stretch | 2894 | ||||
CH3 symmetric stretch | 2872 | CH asymmetric stretch | 2872 | ||||
CH2 symmetric stretch | 2853 | CH3 asymmetric stretch | 2748 | ||||
CH3 asymmetric deformation | 1460 | CH3 symmetric stretch | 2629 | ||||
CH3 scissoring | 1442 | CH3 asymmetric stretch | 1477 | ||||
CH3 twisting | 1300 | CH3 asymmetric bend | 1379 | ||||
CH3 rocking | 1151 | CH asymmetric bend | 1334 | ||||
CC stretching | 1059 | CCH3 bend | 1177 | ||||
CC stretching | 837 | CC stretch | 925 | ||||
CC bend | 797 |
Author | Pre-Processing |
---|---|
Haghi et al. [16] | 1st derivative (Savitzky–Golay algorithm), smoothing over five points plus orthogonal signal correction (OSC). |
Ferreiro et al. [2] | Iterative baseline correction (automatic weighted least squares, using a polynomial of order 2), spectral normalization (total area = 1), and mean centring. |
Barbosa et al. [4] | 2nd derivative with Savitzky-Golay smoothing using 21-point windows and a 2nd order polynomial. |
Rohwedder et al. [64] | 1st derivative (Savitzky–Golay algorithm) employing a 7-points window, 2nd order polynomial for baseline correction and smoothing. |
Nurida et al. [52] | Baseline correction and mean center (for CO2 absorption). |
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Ferreiro, B.; Andrade, J.; Paz-Quintáns, C.; López-Mahía, P.; Muniategui-Lorenzo, S. New Ways for the Advanced Quality Control of Liquefied Natural Gas. Energies 2022, 15, 359. https://doi.org/10.3390/en15010359
Ferreiro B, Andrade J, Paz-Quintáns C, López-Mahía P, Muniategui-Lorenzo S. New Ways for the Advanced Quality Control of Liquefied Natural Gas. Energies. 2022; 15(1):359. https://doi.org/10.3390/en15010359
Chicago/Turabian StyleFerreiro, Borja, Jose Andrade, Carlota Paz-Quintáns, Purificación López-Mahía, and Soledad Muniategui-Lorenzo. 2022. "New Ways for the Advanced Quality Control of Liquefied Natural Gas" Energies 15, no. 1: 359. https://doi.org/10.3390/en15010359
APA StyleFerreiro, B., Andrade, J., Paz-Quintáns, C., López-Mahía, P., & Muniategui-Lorenzo, S. (2022). New Ways for the Advanced Quality Control of Liquefied Natural Gas. Energies, 15(1), 359. https://doi.org/10.3390/en15010359