Exploring the potential of hematite as a debromination agent for 2,4,6-tribromophenol

Mousa, Nada A.; Ali, Labeeb; Kuttiyathil, Mohamed Shafi; Mousa, Hussein A.; Altarawneh, Mohammednoor

doi:10.1016/j.ceja.2022.100334

Cited by 12 publications

(11 citation statements)

References 43 publications

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“…To tackle this issue of HBr contamination in the gaseous products and possible bromine release in the liquid product streams as well, BFRs are thermally reacted with transition metal oxides as effective debrominating agents to aid with bromine recovery as well as production of bromine-free hydrocarbons. Oxides of different metals such as iron (Ferrous/hematite - Fe 2 O 3 and Ferric - Fe 3 O 4 ), Lanthanum (La 2 O 3 ), Calcium (CaO), and Aluminum (Al 2 O 3 )) along with zeolites doped with Ni and Fe have been utilized by previous researchers as debrominating agents. − For example, Ma et al utilized Fe/zeolite catalysts to reduce the Br content in the pyrolyzed oil by 80%, forming mono- and diaromatic compounds. Hematite has also been found to decrease the Br content by 55% from the combustive and oxidative pyrolysis of 2,4,6-Tribromophenol (TBP) …”

Section: Introductionmentioning

confidence: 99%

Prediction of Thermogravimetric Data in Bromine Captured from Brominated Flame Retardants (BFRs) in e-Waste Treatment Using Machine Learning Approaches

Ali

Sivaramakrishnan

Kuttiyathil

et al. 2023

J. Chem. Inf. Model.

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The principal objective in the treatment of e-waste is to capture the bromine released from the brominated flame retardants (BFRs) added to the polymeric constituents of printed circuits boards (PCBs) and to produce pure bromine-free hydrocarbons. Metal oxides such as calcium hydroxide (Ca(OH)2) have been shown to exhibit high debromination capacity when added to BFRs in e-waste and capturing the released HBr. Tetrabromobisphenol A (TBBA) is the most commonly utilized model compound as a representative for BFRs. Our coauthors had previously studied the pyrolytic and oxidative decomposition of the TBBA:Ca(OH)2 mixture at four different heating rates, 5, 10, 15, and 20 °C/min, using a thermogravimetric (TGA) analyzer and reported the mass loss data between room temperature and 800 °C. However, in the current work, we applied different machine learning (ML) and chemometric techniques involving regression models to predict the TGA data at different heating rates. The motivation of this work was to reproduce the TGA data with high accuracy in order to eliminate the physical need of the instrument itself, so that this could save significant experimental time involving sample preparation and subsequently minimizing human errors. The novelty of our work lies in the application of ML techniques to predict the TGA data from e-waste pyrolysis since this has not been conducted previously. The significance of our work lies in the fact that e-waste is ever increasing, and predicting the mass loss curves faster will enable better compositional analysis of the e-waste samples in the industry. Three ML models were employed in our work, namely Linear, random forest (RF), and support vector regression (SVR), out of which the RF method exhibited the highest coefficient of determination (R 2) of 0.999 and least error of prediction as estimated by the root mean squared error (RMSEP) at all 4 heating rates for both pyrolysis and oxidation conditions. An 80:20 split was used for calibration and validation data sets. Furthermore, for showing versatility and robustness of the best-predicting RF model, it was also trained using all the data points in the lower heating rates of 5 and 10 °C/min and predicted on all the data points for the higher heating rates of 15 and 20 °C/min to again obtain a high R 2 of 0.999. The excellent performance of the RF model showed that ML techniques can be used to eliminate the physical use of TGA equipment, thus saving experimental time and potential human errors, and can further be applied in other real-time e-waste recycling scenarios.

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Section: Introductionmentioning

confidence: 99%

Prediction of Thermogravimetric Data in Bromine Captured from Brominated Flame Retardants (BFRs) in e-Waste Treatment Using Machine Learning Approaches

Ali

Sivaramakrishnan

Kuttiyathil

et al. 2023

J. Chem. Inf. Model.

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“…The fingerprint region for pure TBBA starts from a strong absorption peak at 1454 cm −1 owing to the presence of aromatic C–C bonds, a strong phenolic O–H bending at 1378 cm −1 , strong aromatic C–O stretching at 1256 cm −1 , strong C–C–O asymmetric stretch of alcohols at 1020 cm −1 , strong CC bending at 980 cm −1 , strong C–H aromatics bending at 873 and 735 cm −1 along with strong C–Br and aromatic C–Br stretching vibrations at 673 and 564 cm −1 respectively. 15,53–56 The biochar analysis of pyrolyzed TBBA in Fig. 3b shows that there is a substantial reduction in the absorption peaks notably in the fingerprint region.…”

Section: Resultsmentioning

confidence: 99%

“…3a, the short broad peak at 3444 cm −1 is due to the alcoholic OH stretching and those at 2964 and 2870 cm −1 are from the strong symmetrical and asymmetrical stretching of CH 3 groups present in pure TBBA. 53 Whereas the short strong peak at 1755 cm −1 corresponds to the weak C-H bending in an aromatic compound and the one at 1583 cm 15,[53][54][55][56] The biochar analysis of pyrolyzed TBBA in Fig. 3b shows that there is a substantial reduction in the absorption peaks notably in the ngerprint region.…”

Section: Ir Spectrum Analysismentioning

confidence: 95%

“…We have recently surveyed literature pertinent to the debromination ability of various BRs-metal oxides mixtures. 15 For example, catalytic upgrading of vapor intermediates from pyrolysis of Br-ABS mixture (i.e., bromine in the form of TBBA) over Fe/zeolite catalysts reduced the bromine content in oil by ∼80%. The use of the Fe/zeolite catalysts promoted the formation of single and two ring aromatic molecules.…”

Section: Introductionmentioning

confidence: 99%

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Degradation of tetrabromobisphenol A (TBBA) with calcium hydroxide: a thermo-kinetic analysis

et al. 2023

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“…Typical model compounds of BFRs include 2,4,6-tribromophenol (TBP) and tetrabromobisphenol-A (TBBA) since they contain a number of brominated phenolic species and aromatics . A plethora of works in literature have focused on the copyrolysis of TBBA with various metal oxides, − but very few have focused on TBP. , We would like to highlight a recent work by us, where thermogravimetric analysis (TGA) of TBBA combined with Ca(OH) 2 was investigated and various machine learning (ML) techniques were applied to reproduce the TGA data . This work was an extension of a previous work also by Ali et al, where TGA data were obtained for samples of both pure TBBA and TBBA combined with Ca(OH) 2 .…”

Section: Introductionmentioning

confidence: 99%

Prediction of Thermogravimetric Data in the Thermal Recycling of e-waste Using Machine Learning Techniques: A Data-driven Approach

Ali,

Sivaramakrishnan,

Kuttiyathil

et al. 2023

ACS Omega

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The release of bromine-free hydrocarbons and gases is a major challenge faced in the thermal recycling of e-waste due to the corrosive effects of produced HBr. Metal oxides such as Fe 2 O 3 (hematite) are excellent debrominating agents, and they are copyrolyzed along with tetrabromophenol (TBP), a lesser used brominated flame retardant that is a constituent of printed circuit boards in electronic equipment. The pyrolytic (N 2 ) and oxidative (O 2 ) decomposition of TBP with Fe 2 O 3 has been previously investigated with thermogravimetric analysis (TGA) at four different heating rates of 5, 10, 15, and 20 °C/min, and the mass loss data between room temperature and 800 °C were reported. The objective of our paper is to study the effectiveness of machine learning (ML) techniques to reproduce these TGA data so that the use of the instrument can be eliminated to enhance the potential of online monitoring of copyrolysis in e-waste treatment. This will reduce experimental and human errors as well as improve process time significantly. TGA data are both nonlinear and multidimensional, and hence, nonlinear regression techniques such as random forest (RF) and gradient boosting regression (GBR) showed the highest prediction accuracies of 0.999 and lowest prediction errors among all the ML models employed in this work. The large data sets allowed us to explore three different scenarios of model training and validation, where the number of training samples were varied from 10,000 to 40,000 for both TBP and TBP + hematite samples under N 2 (pyrolysis) and O 2 (combustion) environments. The novelty of our study is that ML techniques have not been employed for the copyrolysis of these compounds, while the significance is the excellent potential of enhanced online monitoring of e-waste treatment and extension to other characterization techniques such as spectroscopy and chromatography. Lastly, e-waste recycling could greatly benefit from ML applications since it has the potential to reduce total and operational costs and improve overall process time and efficiency, thereby encouraging more treatment plants to adopt these techniques, resulting in reducing the increasing environmental footprint of e-waste.

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Exploring the potential of hematite as a debromination agent for 2,4,6-tribromophenol

Cited by 12 publications

References 43 publications

Prediction of Thermogravimetric Data in Bromine Captured from Brominated Flame Retardants (BFRs) in e-Waste Treatment Using Machine Learning Approaches

Prediction of Thermogravimetric Data in Bromine Captured from Brominated Flame Retardants (BFRs) in e-Waste Treatment Using Machine Learning Approaches

Degradation of tetrabromobisphenol A (TBBA) with calcium hydroxide: a thermo-kinetic analysis

Prediction of Thermogravimetric Data in the Thermal Recycling of e-waste Using Machine Learning Techniques: A Data-driven Approach

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