UV Spectroscopy for the Determination

In this paper, a new approach based on the principal component analysis technique (PCA) and UV spectroscopy was applied to the determination of low ...
2 downloads 0 Views 550KB Size
Ind. Eng. Chem. Res. 2008, 47, 4025–4028

4025

RESEARCH NOTES Principal Component Analysis/UV Spectroscopy for the Determination of 1-Ethyl-3-methylimidazolium Ethylsulfate Ionic Liquid and Toluene Concentrations in Aqueous Solutions Jose´ S. Torrecilla,* Ester Rojo, Julia´n Garcı´a, and Francisco Rodrı´guez Department of Chemical Engineering, Faculty of Chemistry, UniVersity Complutense of Madrid, 28040 Madrid, Spain

In this paper, a new approach based on the principal component analysis technique (PCA) and UV spectroscopy was applied to the determination of low concentrations of 1-ethyl-3-methylimidazolium ethylsulfate ionic liquid ([Emim][EtSO4]) and toluene in aqueous solutions. By optimizing 10 parameters, the integrated PCA/ UV spectroscopy system is able to predict [Emim][EtSO4] and toluene concentrations with a mean predictive error of 2.6% without any previous phenomenological knowledge. 1. Introduction In recent years, as a consequence of their particular characteristics, ionic liquids (ILs) are gaining importance in the chemical engineering field.1–3 One of the most important research methods is to replace nonenvironmentally friendly solvents with ionic liquids.4,5 In particular, ionic liquids have been used as volatile organic compounds replacements in metal ion extractions,6,7 the separation of ethers from ethanol,8 the separation of aliphatic and aromatic hydrocarbons,4,5 etc. Focusing on the separation of toluene from toluene/heptane mixtures, when this process is carried out by ILs, the investment and energy costs are considerably lower than when other classical solvents are used. One of the ILs successfully used in this extraction process is 1-ethyl-3-methylimidazolium ethylsulfate IL ([Emim][EtSO4]). The extraction process scheme where the toluene is separated from toluene/heptane mixtures by IL is shown in the literature.9 Given that this IL has a negligible vapor pressure, it could be recovered from the mixture by evaporation. To evaluate and control the extraction process rate, the measurement of chemicals concentrations is necessary. Recently, these measurements have been carried out by interpolation in the physicochemical properties correlation of the mixtures (density, viscosity, refractive index, etc.), 1H NMR spectroscopy,4 and gas chromatography (GC).1 None of these techniques are adequate to measure/control online any extraction process because they require a relatively long sample preparation time. Therefore, in order to control and evaluate the extraction processes online, an analytical technique with a shorter sample preparation time is necessary. Given that the imidazolium ring of the [Emim][EtSO4] is UV active and that the UV-visible spectroscopy fulfills all mentioned conditions, this analytical technique was proposed. On the other hand, as the toluene and most ILs have similar absorbance profiles in the UV-visible range, the use of powerful chemometric tools to solve signal overlapping is necessary. We have developed a model based on neural networks (NNs) to deconvolute the UV signals of toluene and [Emim][EtSO4].10 * To whom correspondence should be addressed. Fax: +34 91 394 42 43 . Tel.: +34 91 394 42 40. E-mail: [email protected].

The chemicals concentrations were determined with a mean error of 0.99

10 80

Torrecilla et al. (2007).10

Figure 3. Principal component analysis score plot PC1 versus PC2: (a) wavelength selected between 190 and 230 nm and (b) wavelength selected between 231 and 300 nm.

As can be seen in Figures 2 and 3, although with PCs the spectra can be partially decorrelated, given the tails shown in Figure 3, toluene and [Emim][EtSO4] compounds cannot be deconvoluted adequately using PCA technique. This is probably because of the nonlinear relationship between UV absorption signals and real concentrations of chemicals and the assumptions and limits of PCA algorithm. These considerations are as follows: large variances in the data having importance and lower variances representing noise; the principal components being orthogonal; and the mean and the variance being sufficient statistics. 3.2. PCA Modeling. Although the UV absorption spectra of toluene and [Emim][EtSO4] in aqueous solutions cannot be adequately decorrelated, the PCA technique was tested to measure these compounds. With this purpose, a global model was used. 3.2.1. Global Model. The new strategy used attempted to establish a single global calibration model for determining toluene and [Emim][EtSO4] concentrations using wavelengths between 190 and 300 nm. For this purpose, the spectra data were randomly split into two sets. These were the calibration and validation sets that contain 80% (144 UV absorption spectrums) and the remaining 20% (36 UV absorption spectrums), respectively.10 The calibration and validation samples are used to optimize and validate the global model, respectively. The model was optimized using the calibration samples, and then this global model was tested using the validation samples. The mean prediction error (MPE, eq 2) and correlation coefficient (R2) between experimental and estimated values are less than 2.3% and greater than 0.98, respectively. Results of the testing stage are shown in Table 1. Given that the relationship

Figure 4. Residuals from global model applied to the estimations of (a) [Emim][EtSO4] and (b) toluene concentrations.

between UV absorbance signal and real concentrations data is nonlinear, the results are worse than those calculated with the nonlinear algorithm.10 Otherwise, the number of parameters used and the complexity of the model tested are notably lower and simpler, respectively (Table 2). MPE )

1 N

| ∑

|

rK - yK

k

rK

× 100

(2)

In eq 2, N, yk, and rk are the number of observations, the global model estimations, and the real value, respectively. 3.3. Application of PCA Model. Finally, different aqueous solutions of [Emim][EtSO4] and toluene between 0 and 15 ppm were prepared and monitored to make a new validation sample and validate the prediction capability of the optimized global model. In this process, the mean prediction error is 0.96. The residuals from global model versus [Emim][EtSO4] and toluene concentrations are shown in Figure 4. Taking into account the correlation coefficients of residuals’ values versus concentration values and the residuals’ ranges (y axes of parts

4028 Ind. Eng. Chem. Res., Vol. 47, No. 11, 2008

a and b of Figure 4 ), the global model is able to estimate the [Emim][EtSO4] concentration better than the toluene concentration (2.5 and 1.6%, respectively), which is in agreement with the results shown in Table 1. Although these values are worse than those calculated by the model proposed by Torrecilla et al.,10 the model and the optimization process shown here are notably simpler. For example, the MPE < 1% and the R2 > 0.99 are taken with 80 parameters and nonlineal optimization. Here, the MPE < 2.6% and R2 > 0.96 is reached using a model with 10 parameters, and its optimization process is notably simpler. 4. Conclusion The PCA technique has been used to identify the main sources of variation in the UV absorption spectra of aqueous solutions of toluene and [Emim][EtSO4] between 0 and 15 ppm and to estimate their concentrations. Given the nonlinear relation between UV absorption signal and concentration of these compounds, this linear technique is not able to adequately deconvolute these signals. However, the PCA technique is sufficient to estimate their concentrations as a function of UV absorption spectra. Using a new validation sample composed of UV absorbance signals of toluene and [Emim][EtSO4] between 0 and 15 ppm, the mean difference between the real and predicted concentration values of these compounds was 0.96. The PCA technique can be adapted to quantify [Emim][EtSO4] and toluene concentrations in aqueous solutions in a simple way, without using previous phenomenological knowledge or mechanistic premises and with only input and output variables. On the other hand, from a measurement accuracy point of view, the PCA technique is very interesting for further applications to digital control or measurement devices. Acknowledgment The authors are grateful to the Spanish “Ministerio de Educacio´n y Ciencia” for financial support of both Project CTQ2006-04644 and the Ramo´n y Cajal research contract of J.S.T. Literature Cited (1) Earle, M. J.; Esperanc¸a, J. M. S. S.; Gilea, M. A.; Lopes, J. N. C.; Rebelo, L. P. N.; Magee, J. W.; Seddon, K. R.; Widegren, J. A. The distillation and volatility of ionic liquids. Nature 2006, 439, 831.

(2) Renner, R. Ionic liquids: An industrial cleanup solution. EnViron. Sci. Technol. 2001, 35, 410A (3) Clark, J. H.; Tavener, S. J. Alternative Solvents: Shades of Green. Org. Process Res. DeV. 2007, 11, 149. (4) Meindersma, G. W.; Podt, A. J. G.; de Haan, A. B. Ternary liquidliquid equilibria for mixtures of toluene + n-heptane + an ionic liquid. Fluid Phase Equilib. 2006, 247, 158. (5) Meindersma, G. W.; Podt, A. J. G.; de Haan, A. B. Selection of ionic liquids for the extraction of aromatic hydrocarbons from aromatic/ aliphatic mixtures. Fuel Process. Technol. 2005, 87, 59. (6) Wei, G. T.; Yang, Z.; Chen, C. J. Room temperature ionic liquid as a novel medium for liquid/liquid extraction of metal ions. Anal. Chim. Acta 2003, 488, 183. (7) Li, Z.; Wei, Q.; Yuan, R.; Zhou, X.; Liu, H.; Shan, H.; Song, Q. A new room temperature ionic liquid 1-butyl-3-trimethylsilylimidazolium hexafluorophosphate as a solvent for extraction and preconcentration of mercury with determination by cold vapor atomic absorption spectrometry. Talanta 2007, 71, 68. (8) Arce, A.; Rodrı´guez, O.; Soto, A. A comparative study on solvents for separation of tert-amyl ethyl ether and ethanol mixtures. New experimental data for 1-ethyl-3-methyl imidazolium ethyl sulfate ionic liquid. Chem. Eng. Sci. 2006, 61, 6929. (9) Meindersma, G. W.; Podt, J. G.; Meseguer, M. G.; De Haan, A. B. S. Ionic Liquids IIIB, Fundamentals, Progress, Challenges, and Opportunities; American Chemical Society: Washington, DC, 2005. (10) Torrecilla, J. S.; Ferna´ndez, A.; Garcı´a, J.; Rodrı´guez, F. Determination of 1-ethyl-3-methylimidazolium ethylsulfate ionic liquid and toluene concentration in aqueous solutions by artificial neural network/UV spectroscopy. Ind. Eng. Chem. Res. 2007, 46, 3787. (11) Benjathapanun, N.; Boyle, W. J. O.; Grattan, K. T. V. Classification of UV-Vis spectroscopic data using principal component analysis and neural network techniques. Measurement 1998, 24, 1. (12) Hadad, G. M.; El-Gindy, A.; Mahmoud, W. M. M. HPLC and chemometrics-assisted UV-spectroscopy methods for the simultaneous determination of ambroxol and doxycycline in capsule Spectrochim. Acta, Part A 2007, doi:10.1016/j.saa.2007.08.016. (13) Skrobot, V. L.; Castro, E. V. R.; Pereira, R. C. C.; Pasa, V. M. D.; Fortes, I. C. P. Use of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) in Gas Chromatographic (GC) Data in the Investigation of Gasoline Adulteration. Energy Fuels 2007, 21, 3394. (14) Pereira, R. C. C.; Skrobot, V. L.; Castro, E. V. R.; Fortes, I. C. P.; Pasa, V. M. D. Determination of Gasoline Adulteration by Principal Components Analysis-Linear Discriminant Analysis Applied to FTIR Spectra. Energy Fuels 2006, 20, 1097. (15) Wang, X.; Paliwal, K. K. Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition. Pattern Recognit. 2003, 36, 2429. ´ lvarez, P. J.; Montilla, A.; Corzo, (16) Montan˜e´s, F.; Fornari, T.; Martı´n-A N.; Olano, A.; Iba´n˜ez, E. Selective fractionation of disaccharide mixtures by supercritical CO2 with ethanol as co-solvent. J. Supercrit. Fluids. 2007, 41, 61.

ReceiVed for reView December 10, 2007 ReVised manuscript receiVed March 10, 2008 Accepted March 31, 2008 IE701677M