Due to the complex nature of near-infrared (NIR) spectra, it is usually very difficult to provide quantitative interpretations of spectral data. As a consequence, careful building and validation of calibration models are of fundamental importance prior to development of useful applications of NIR technologies. For this reason, this work presents a statistical study about the NIR spectroscopy, analyzing the NIR behavior when the experimental conditions are changed. Near-infrared spectra were measured at different temperatures and stirring velocities for systems containing a pure solvent and a suspension of polymer powder in order to perform the error analysis. Then, mixtures of xylene and toluene were analyzed through NIR at different temperatures and stirring velocities and the obtained data were used to build calibration models with multivariate techniques. The results showed that the precision of the NIR measurements depends on the analytical conditions and that unavoidable fluctuations of spectral data (or spectral data variability) are strongly correlated, leading to full covariance matrices of spectral fluctuations, which has been surprisingly neglected during quantitative analyses. In particular, modeling of the xylene/toluene NIR data performed with different multivariate techniques revealed that the principal directions are not preserved when the real covariance matrix of measurement errors is taken into account.
Pollution by plastics constitutes an urgent problem that demands immediate actions, including development of efficient polymer recycling technologies. In this scenario, the catalytic degradation of plastic wastes constitutes a promising technology, as suitable catalysts can be used to perform cracking reactions and controlled plastic degradation, yielding high quality end products. Catalyst investments are expected to be recovered by benefits related to reduction of reaction temperature and time and by manufacture of higher valued products. However, proper environmental assessment of catalyst usage has yet to be performed in most plastics chemical recycling processes. For these reasons, in the present study, life cycle assessment (LCA) based on system expansion methodologies is carried out to determine the environmental impacts of catalytic pyrolysis transformations of high‐impact polystyrene (HIPS) and high‐density polyethylene (HDPE) using zeolite H‐USY (ultrastable Y) and SO4/SnO2 catalysts, respectively, based on actual collected experimental data to represent conversions and yields. Surprisingly, the obtained results indicate that the use of catalysts for plastic waste degradation reactions can be environmentally disadvantageous sometimes, depending on the blend of obtained products. Therefore, the environmental impact of catalysts on plastics chemical recycling should be carefully assessed to avoid problems derived from positive bias, which assumes that the catalytic process is necessarily better than the noncatalytic counterpart. However, the positive impacts of styrene and olefins recovery can indeed contribute with positive environmental performances of both catalytic and non‐catalytic processes, particularly regarding global warming, acidification, human toxicity, ecotoxicity, eutrophication, and ozone layer depletion.
Many methods have been developed to allow for consideration of measurement errors during multivariate data analyses. The incorporation of the error structure into the analytical framework, usually described in terms of the covariance matrix of measurement errors, can provide better model estimation and prediction. However, little effort has been made to evaluate the effects of heteroscedastic measurement uncertainties on multivariate analyses when the covariance matrix of measurement errors changes with the measurement conditions. For this reason, the present work describes a new numerical procedure for analyses of heteroscedastic systems (heteroscedastic principal component regression or H-PCR) that takes into consideration the variations of the covariance matrix of measurement fluctuations. In order to illustrate the proposed approach, near infrared (NIR) spectra of xylene and toluene mixtures were measured at different temperatures and stirring velocities and the obtained data were used to build calibration models with different multivariate techniques, including H-PCR. Modeling of available xylene–toluene NIR data revealed that H-PCR can be used successfully for calibration purposes and that the principal directions obtained with the proposed approach can be quite different from the ones calculated through standard PCR, when heteroscedasticity is disregarded explicitly.
The massive consumption of plastic material and its waste worldwide has made it necessary to find alternative solutions to reduce the size of landfill areas and recover their energy content. Pyrolysis is one of the chemical recovery technologies for plastics whose return on investment is estimated between 16% and 21%. This article aims to use the Triple Layer Business Model Canvas tool, which considers the elements of a business model visually and dynamically based on the three pillars of sustainability (economic, environmental, and social). This approach allowed for a comprehensive analysis of the sustainability-oriented business model. As a result, we concluded that pyrolysis is viable as a complementary process to other means of mechanical recycling and energy recovery.
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