Waste LEDs in China: Generation estimation and potential recycling benefits

Tian, Xi; Xie, Jinliang; Hu, Lei; He, Xiao; Liu, Yaobin

doi:10.1016/j.resconrec.2022.106640

Cited by 5 publications

(5 citation statements)

References 68 publications

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“…Transition metals such as Ag, Pd, Au, Ni, and Pt form part of the minimal metallic loads on these PCBs, which are eventually recovered via hydrometallurgical-based operations. , On the other hand, the nonmetallic constituents in waste PCBs (WPCBs) are mostly polymeric materials, i.e., majorly acrylonitrile butadiene styrene (ABS), fibers, and brominated flame retardants (BFRs)) . BFRs are typically added to delay or prevent the onset of fire when electrical devices are subjected to high thermal stresses during their operation.…”

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 methodology of the DFT calculations is fully described in previous publications. 9 In a nutshell, the DMol 3 code 33 is used in calculations based on the Perdew and Wang (PAW) functional along with a double numerical plus polarization (DNP) basis set. 34,35 Final energies converge to less than 1 × 10 −5 Ha.…”

Section: Model-free and Model Fitting Kineticsmentioning

confidence: 99%

“…The base of kinetic analysis starts from Arrhenius equation represented as eqn (1) and basic rate equation, eqn (2), where a signies the degree of conversion which can be determined by using eqn (3). In this formula w i , w o and w f denote weight loss at the initial, at the moment, and at nal stages, respectively.…”

Section: Kinetic Analysismentioning

confidence: 99%

“… 2 The valuable (yet minute) metallic loads in PCBs constitute Ag, Pd, Au, Ni, and Pt. 3 These constituents are typically recovered via hydrometallurgical-based operations. 4 The non-metallic load in waste PCBs (WPCBs) consists of polymeric materials most notably Acrylonitrile Butadiene Styrene (ABS), fibers, and brominated flame retardants (BFRs).…”

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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“…During the period from 2014 to 2019, the e-waste generated annually increased by ∼23% to 55 t (Mt) and is further projected to increase by an alarming ∼40% to 75 Mt in the next 7 years. − The primary constituents of e-waste are printed circuit boards (PCBs), which consist of both metallic and nonmetallic fractions (NMFs). The metallic constituents such as Au, Ag, Cu, Al, Fe, Ni, and Pb are recovered using leaching techniques through strong inorganic acids, but the efficiency of this process is dependent on the fineness (size of the metallic species) of the e-wastes. − Other limitations of this extraction are that it is slow, generates toxic effluents, consumes a lot of concentrated acids, and is expensive. Brominated flame retardants (BFRs) along with resins and ceramics constitute the nonmetallic part of PCBs, where the BFRs release brominated benzo-dioxins, benzofurans, and most notably toxic hydrogen bromide (HBr) as byproducts during their thermal recycling process due to favorable reaction conditions .…”

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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Waste LEDs in China: Generation estimation and potential recycling benefits

Cited by 5 publications

References 68 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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