Application of computational approach in plastic pyrolysis kinetic modelling: a review

Physicochemical reactions in e-waste recycling

Electronic waste (e-waste) recycling is becoming a global concern owing to its immense quantity, hazardous character and the potential loss of valuable metals. The many processes involved in e-waste recycling stem from a mixture of physicochemical reactions, and understanding the principles of these reactions can lead to more efficient recycling methods. In this Review, we discuss the principles behind photochemistry, thermochemistry, mechanochemistry, electrochemistry and sonochemistry for metal recovery, polymer decomposition and pollutant elimination from e-waste. We also discuss how these processes induce or improve reaction rates, selectivity and controllability of e-waste recycling based on thermodynamics and kinetics, free radicals, chemical bond energy, electrical potential regulation and more. Lastly, key factors, limitations and suggestions for improvements of these physicochemical reactions for e-waste recycling are highlighted, wherein we also indicate possible research directions for the future.

Computational Exploration of Bio-Degradation Patterns of Various Plastic Types

Plastic materials are recalcitrant in the open environment, surviving for longer without complete remediation. The current disposal methods of used plastic material are inefficient; consequently, plastic wastes are infiltrating the natural resources of the biosphere. The mixed composition of urban domestic waste with different plastic types makes them unfavorable for recycling; however, natural assimilation in situ is still an option to explore. In this research work, we have utilized previously published reports on the biodegradation of various plastics types and analyzed the pattern of microbial degradation. Our results demonstrate that the biodegradation of plastic material follows the chemical classification of plastic types based on their main molecular backbone. The clustering analysis of various plastic types based on their biodegradation reports has grouped them into two broad categories of C-C (non-hydrolyzable) and C-X (hydrolyzable). The C-C and C-X groups show a statistically significant difference in their biodegradation pattern at the genus level. The Bacilli class of bacteria is found to be reported more often in the C-C category, which is challenging to degrade compared to C-X. Genus enrichment analysis suggests that Pseudomonas and Bacillus from bacteria and Aspergillus and Penicillium from fungi are potential genera for the bioremediation of mixed plastic waste. The lack of uniformity in reporting the results of microbial degradation of plastic also needs to be addressed to enable productive growth in the field. Overall, the result points towards the feasibility of a microbial-based biodegradation solution for mixed plastic waste.

Machine-Learning-Based Predictions of Polymer and Postconsumer Recycled Polymer Properties: A Comprehensive Review

There has been a tremendous increase in demand for virgin and postconsumer recycled (PCR) polymers due to their wide range of chemical and physical characteristics. Despite the numerous potential benefits of using a data-driven approach to polymer design, major hurdles exist in the development of polymer informatics due to the complicated hierarchical polymer structures. In this review, a brief introduction on virgin polymer structure, PCR polymers, compatibilization of polymers to be recycled, and their characterization using sensor array technologies as well as factors affecting the polymer properties are provided. Machine-learning (ML) algorithms are gaining attention as cost-effective scalable solutions to exploit the physical and chemical structures of polymers. The basic steps for applying ML in polymer science such as fingerprinting, algorithms, open-source databases, representations, and polymer design are detailed in this review. Further, a state-of-the-art review of the prediction of various polymer material properties using ML is reviewed. Finally, we discuss open-ended research questions on ML application to PCR polymers as well as potential challenges in the prediction of their properties using artificial intelligence for more efficient and targeted PCR polymer discovery and development.

Direct prediction of steam cracking products from naphtha bulk properties: Application of the two sub-networks ANN

Steam cracking of naphtha is an important process for the production of olefins. Applying artificial intelligence helps achieve high-frequency real-time optimization strategy and process control. This work employs an artificial neural network (ANN) model with two sub-networks to simulate the naphtha steam cracking process. In the first feedstock composition ANN, the detailed feedstock compositions are determined from the limited naphtha bulk properties. In the second reactor ANN, the cracking product yields are predicted from the feedstock compositions and operating conditions. The combination of these two sub-networks has the ability to accurately and rapidly predict the product yields directly from naphtha bulk properties. Two different feedstock composition ANN strategies are proposed and compared. The results show that with the special design of dividing the output layer into five groups of PIONA, the prediction accuracy of product yields is significantly improved. The mean absolute error of 11 cracking products is 0.53wt% for 472 test sets. The comparison results show that this indirect feedstock composition ANN has lower product prediction errors, not just the reduction of the total error of the feedstock composition. The critical factor is ensuring that PIONA contents are equal to the actual values. The use of an indirect feedstock composition strategy is a means that can effectively improve the prediction accuracy of the whole ANN model.