Methane can be stored by metal-organic frameworks (MOFs). However, there remain challenges in the implementation of MOFs for adsorbed natural gas (ANG) systems. These challenges include thermal management, storage capacity losses due to MOF packing and densification, and natural gas impurities. In this review, we discuss discoveries about how MOFs can be designed to address these three challenges. For example, Fe(bdp) (bdp 2− = 1,4-benzenedipyrazolate) was discovered to have intrinsic thermal management and released 41% less heat than HKUST-1 (HKUST = Hong Kong University of Science and Technology) during adsorption. Monolithic HKUST-1 was discovered to have a working capacity 259 cm 3 (STP) cm −3 (STP = standard temperature and pressure equivalent volume of methane per volume of the adsorbent material: T = 273.15 K, P = 101.325 kPa), which is a 50% improvement over any other previously reported experimental value and virtually matches the 2012 Department of Energy (Department of Energy = DOE) target of 263 cm 3 (STP) cm −3 after successful packing and densification. In the case of natural gas impurities, higher hydrocarbons and other molecules may poison or block active sites in MOFs, resulting in up to a 50% reduction of the deliverable energy. This reduction can be mitigated by pore engineering. of 9.2 MJ L −1 , which is 70% less than that of gasoline [2,15]. However, carrying an extremely pressurized tank raises safety concerns in vehicles in the case of accidents and has an energy cost associated with compression. Furthermore, CNG, which is the established and predominant technology for NGVs, has a driving range of 350-450 km as compared to 400-600 km for gasoline-powered vehicles [16]. Based on this, there is a need to develop gas storage technology beyond that which is already established in real life applications. Further improvements in natural gas storage for NGV technology should seek to improve driving range to decrease time at the pump and the corresponding number of required tank recharges. Increasing driving range would be helpful to implement the technology in areas where natural gas filling stations are not as abundant. In addition, the CNG tank that holds the fuel takes up cargo space and technological advancements that decrease the volume of the natural gas fuel tank are beneficial. Another approach to store natural gas is LNG, which has an energy density of 22.2 MJ L −1 . Some drawbacks of LNG are the energy and cost associated with liquefaction (−162 • C), which present major technological obstacles [17,18] Lastly natural gas can be stored as ANG. Fairly large volumetric capacities of 4-6 MJ L −1 at pressures of around 35 bar at room temperature for different adsorbents were achieved [19]. The presence of sorbent materials in high pressure tanks reduces the pressure requirement of the tanks, making storage and delivery safer and allows for the use of single-stage compressors. ANG may increase the driving range and decrease the volume required of the fuel tank to achieve a specific driving dis...

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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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