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

Abstract

During the past decade, pyrolysis routes have been identified as one of the most promising solutions for plastic waste management. However, the industrial adoption of such technologies has been limited and several unresolved blind spots hamper the commercial application of pyrolysis. Despite many years and efforts to explain pyrolysis models based on global kinetic approaches, recent advances in computational modelling such as machine learning and quantum mechanics offer new insights. For example, the kinetic and mechanistic information about plastic pyrolysis reactions necessary for scaling up processes is unravelling. This selective literature review reveals some of the foundational knowledge and accurate views on the reaction pathways, product yields, and other features of pyrolysis created by these new tools. Pyrolysis routes mapped by machine learning and quantum mechanics will gain more relevance in the coming years, especially studies that combine computational models with different time and scale resolutions governed by “first principles.” Existing research suggests that, as machine learning is further coupled to quantum mechanics, scientists and engineers will better predict products, yields, and compositions, as well as more complicated features such as ideal reactor design.

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

“…Currently, ML advancement in PCR polymer research has been attracting the interest of researchers around the world. ML is capable in classification and identification of the various PCR polymers in mixed plastic waste streams , and pyrolysis of PCR polymers . However, there has been no notable study that used a machine-learning model to predict the PCR polymer properties.…”

“…ML is capable in classification and identification of the various PCR polymers in mixed plastic waste streams 157,158 and pyrolysis of PCR polymers. 159 However, there has been no notable study that used a machine-learning model to predict the PCR polymer properties.…”

Active learning-based exploration of the catalytic pyrolysis of plastic waste

“…There is still a need to develop suitable reactor and catalyst technology, although multiple catalysts and reactor configurations have been tested in the past [41,42]. Currently, plastic pyrolysis is modelled via global kinetic models and semi-detailed kinetic models such as kinetic Monte Carlo, method-of-moments, deterministic models [38,43]. However these models currently lack the incorporation of mass and heat transfer, catalyst, type of feedstock, which limits the applicability of these models [43].…”

“…Currently, plastic pyrolysis is modelled via global kinetic models and semi-detailed kinetic models such as kinetic Monte Carlo, method-of-moments, deterministic models [38,43]. However these models currently lack the incorporation of mass and heat transfer, catalyst, type of feedstock, which limits the applicability of these models [43]. Therefore, MLmodels based on both in silico and experimental data are increasingly investigated as they allow to overcome these drawbacks [44].…”

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

“…Using a combination of machine learning and optimization, a fully automated screening method was constructed to rapidly identify candidate catalyst types (Tran and Ulissi, 2018). In addition, machine learning is also widely used in chemical process engineering, such as plastic pyrolysis (Armenise et al, 2021), Fischer-Tropsch synthesis (Chakkingal et al, 2022), and ethylene thermal cracking (Bi et al, 2021), the topic of this paper.…”