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

Ali, Labeeb; Sivaramakrishnan, Kaushik; Kuttiyathil, Mohamed Shafi; Chandrasekaran, Vignesh; Ahmed, Oday H.; Al-Harahsheh, Mohammad; Altarawneh, Mohammednoor

doi:10.1039/d2ra08223c

Cited by 14 publications

(5 citation statements)

References 74 publications

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“…As seen in Figure , the actual thermal decomposition started at 250 °C and yielded 3 regions namely as follows: 250–340 °C, 357–435 °C, and the final one being 553–670 °C. As described in our previous work, the first mass loss event was attributed to decomposition of long-chained TBBA units, where the C–Br bond-scission occurs resulting in Br free radicals. However, the formation of HBr was prevented due to the presence of Ca(OH) 2 to form CaBr 2 ultimately.…”

Section: Machine Learning Methods For Prediction Of Tga Datamentioning

confidence: 56%

“…The plots of TGA for the combined and individual samples at other heating rates followed by further detailed explanation for the peaks observed during the mass-losses are provided in our previous work. 14,24 Furthermore, in our latest work where we developed kinetic models to explain the TGA data, 24 we have provided a detailed explanation of each of the peak regions of the thermal decomposition describing each thermal event and the possible scientific reasoning for it. The readers are requested to refer to section 4.1 of Ali et al 24 Each individual ML model was developed on 80% of the data points available for all heating rates in pyrolysis and combustion conditions, and this is called the training/calibration set.…”

Section: ■ Introductionmentioning

confidence: 99%

“…14,24 Furthermore, in our latest work where we developed kinetic models to explain the TGA data, 24 we have provided a detailed explanation of each of the peak regions of the thermal decomposition describing each thermal event and the possible scientific reasoning for it. The readers are requested to refer to section 4.1 of Ali et al 24 Each individual ML model was developed on 80% of the data points available for all heating rates in pyrolysis and combustion conditions, and this is called the training/calibration set. The rest of the data points consisting of the rest of the 20% random samples at all heating rates were considered for validation, where the model was used to predict the TGA data and measure the performance of the model through two types of statistical measures given in the next subsection.…”

Section: ■ Introductionmentioning

confidence: 99%

“…This has never been done before and, hence, is the novelty of our work. In order to train our machine learning models and predict the TGA data at different heating rates with maximum possible accuracy, we obtained the necessary experimental data from the TGA characterization conducted in our previous work , involving the copyrolysis of TBBA and Ca(OH) 2 as the metal oxide at 4 different heating rates.…”

Section: Introductionmentioning

confidence: 99%

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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: Machine Learning Methods For Prediction Of Tga Datamentioning

confidence: 56%

Section: ■ Introductionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…Besides, using moringa leaf meal up to 0.8% from total meat used [9] or processing cooked goat meat patties containing leaf flour up to 0.9% of the total meat used [11] have been performed and revealed its positive effect. Furthermore, other researchers reported the utilization of moringa leaf extract flour up to a level of 5% for mutton patties [10] and 20% in chicken burgers [12]. Conducted studies draw similar conclusion from the inclusion of the moringa plant in meat products to increase its nutrient content, maintain and improve physical quality, decrease lipid oxidation and extend its shelf life [13].…”

Section: Introductionmentioning

confidence: 65%

Physical quality and consumer acceptance of beef patties containing different levels of moringa oleifera leaf flour extract

Kartikasari,

Hertanto,

Swastike

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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The aim of this study was to determine the effect of Moringa oleifera leaf flour extract (MOLFE) utilization on medium beef patties. The addition levels of MOLFE were set at 0 (P0), 2 (P1) and 4% (P2) (w/w). A total of 25 semi-trained panellists was asked to evaluate consumer acceptance using a preference measurement instrument of a 9-point hedonic scale. The physical quality and hedonic test data were analysed using analysis of variance followed by the Tukey test for a significantly different data. This study revealed a notable effect of MOLFE on raw patties pH (p<0.05), wherein its addition markedly decreased the raw pH value. No significant effect was observed for cooked pH, cooking loss and water holding capacity (WHC). Likewise, the hedonic test result was not differed among treatments (p>0.05), indicating acceptable inclusion of MOLFE into beef patties up to 4%. This study suggests the utilization of MOLFE up to 4% level can be applied to the manufacture of beef patties while maintaining its consumer acceptance.

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Thermal Degradation Kinetic Study of Expanded Perlite‐Modified Butadiene Rubber Composites

Edres,

Buniyatzadeh,

Aliyeva

et al. 2024

Macro Reaction Engineering

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Mineral‐rubber composites based on phosphorylated butadiene rubber (PhBR), including pure expanded perlite (EP) and modified phosphorylated expanded perlite (PhEP) as fillers, have been developed. The process involved forming PhBR and its composites – EP/PhBR and PhEP/PhBR‐through the oxidative chlorophosphorylation (OxCh) reaction. An in‐depth comparative analysis was conducted on the thermal destruction of the PhBR matrix and the EP/PhBR, and PhEP/PhBR composites. The thermogravimetric (TG)/differential thermogravimetry (DTG) analyses revealed three stages of thermal degradation for the PhBR matrix and both composites, highlighting the notable effects of EP and PhEP in the second and third stages of the degradation process. In comparison, the PhEP/PhBR composite exhibited reduced weight loss, the highest integral procedural decomposition temperature (IPDT) value, and a lower Tmax on the DTG curve, compared with the EP/PhBR composite and the PhBR matrix. The mechanism of the thermal destruction reaction and the kinetic parameters Ea and A were calculated using the model‐fitting Coats‐Redfern method.This article is protected by copyright. All rights reserved

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

Abstract: Thermal treatment of bromine-contaminated polymers (i.e., as in e-waste) with metal oxides is currently deployed as a mainstream strategy in recycling and resources recovery from these objects.

Cited by 14 publications

References 74 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

Physical quality and consumer acceptance of beef patties containing different levels of moringa oleifera leaf flour extract

Thermal Degradation Kinetic Study of Expanded Perlite‐Modified Butadiene Rubber Composites

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