Bayesian tuned kinetic Monte Carlo modeling of polystyrene pyrolysis: Unraveling the pathways to its monomer, dimers, and trimers formation

Dogu, Onur; Eschenbacher, Andreas; Varghese, Robin John; Dobbelaere, Maarten R.; D’hooge, Dagmar; Steenberge, Paul H. M. Van

doi:10.1016/j.cej.2022.140708

Cited by 12 publications

(5 citation statements)

References 112 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PS is the fifth mostly used synthetic polymer 93,94 in packaging materials, food containers and construction materials. 95,96,97 PS is an aromatic thermoplastic with a C-C backbone, 81 makes its biodegradation very difficult. 81,98,99 PS degradation can be performed via direct ring cleavage and side-chain oxygenation.…”

Section: Ps Mechanism Biodegradationmentioning

confidence: 99%

Sustainable bioconversion of synthetic plastic wastes to polyhydroxyalkanoate (PHA) bioplastics: recent advances and challenges

Neifar¹,

Hammami²,

Souissi³

et al. 2023

MOJABB

View full text Add to dashboard Cite

Millions of tons of chemical plastics are accumulated annually worldwide in terrestrial and marine environments due to inadequate recycling plants and facilities and low circular use. Their continuous accumulation and contamination of soil and water pose a severe threat to the environment and to human, animal and plant health. There is therefore an urgent need to develop effective eco-environmental strategies to overcome the significant environmental impacts of traditional plastic waste management practises (incineration, landfilling, and recycling). In recent years, reports on microbial strains equipped with the potential of degrading plastic materials, which can further be converted into usable products such as PHA bioplastics have sprung up, and these offer a possibility to develop microbial and enzymatic technologies for plastic waste treatment and then progressing plastics circularity. In this chapter, an overview of the reported microbial and enzymatic degradations of petroleum-based synthetic plastics, specifically polyethylene, polystyrene, polypropylene, polyvinyl chloride, polyurethane and polyethylene terephthalate, is detailed. Furthermore, the harvesting of depolymerization products to produce new PHA materials with high added industrial value can be considered as an innovative solution, helping to increase synthetic plastic recycling rate and creating new circular economy opportunities. Finally, the challenge of ending plastic pollution is still difficult, but sustainable, renewable, bio-based and completely biodegradable, PHA will hold enormous promise for replacing plastics made from petrochemicals.

show abstract

Section: Ps Mechanism Biodegradationmentioning

confidence: 99%

Sustainable bioconversion of synthetic plastic wastes to polyhydroxyalkanoate (PHA) bioplastics: recent advances and challenges

Neifar¹,

Hammami²,

Souissi³

et al. 2023

MOJABB

View full text Add to dashboard Cite

show abstract

“…The kinetic Monte Carlo (KMC) method has been widely used for the simulation of radical polymerization, which was first developed by Gillespie (also termed the Stochastic Simulation Algorithm). − One of the most distinctive advantages of KMC simulation in polymer reaction engineering (PRE) is the ability to track the explicit monomer sequences and other microstructural characteristics that directly affect the macroscopic properties of polymers, − which are generally difficult to be obtained by experiments and other modeling techniques (such as deterministic solvers). , Saeb et al employed a KMC-based program to monitor and evaluate the distribution of copolymer composition and ethylene sequence length and to obtain tailored copolymers. Fierens et al proposed a new design strategy to achieve sequence-controlled polymers and select appropriate mediating agents using KMC simulation.…”

Section: Introductionmentioning

confidence: 99%

Scaling Acceleration Algorithm for Hybrid Kinetic Monte Carlo Simulation of Linear Radical Polymerization

Fang,

Gao

2023

Macromolecules

View full text Add to dashboard Cite

Polymer materials design is essential in a wide range of industries, and the simulation of polymerization reactions provides a promising approach to the efficient design of synthesis recipes for desired property targets. The kinetic Monte Carlo (KMC) algorithm has been widely used in the simulation of polymerization reactions due to its ability to track the most delicate details of individual molecular sequences. However, the disadvantage of high computational cost has limited its broader applications, primarily caused by the large number of molecules that must be sampled in KMC simulations. This work proposes a novel scaling acceleration algorithm (SAA) specifically designed for hybrid KMC simulations of linear radical polymerization, which can significantly reduce the number of molecules in KMC simulations of radical polymerization, thus greatly accelerating the simulations. The algorithm can be used in radical reaction systems (currently limited to simple kinetic schemes) with species concentrations on significantly different scales, thus presenting a novel method for simulating polymerization reactions for efficient polymer materials design. To be more specific, this was achieved by solving a continuum model in parallel with the KMC simulation to provide an accurate value of radical concentrations for calculating a scaling factor (SF), which was then used to recover the correct results in KMC simulations when using an insufficient number of molecules. The algorithm was tested on three case studies: (1) free-radical polymerization, copolymerization, (2) atom-transfer radical polymerization (ATRP), homopolymerization, and (3) ATRP, copolymerization. The results showed that SAA simulates the system with at least 100 times fewer molecules than what would have been required for an accurate KMC simulation while achieving acceptable accuracy in the key characteristics of the polymerization system (including conversion, molecular weight distribution, and sequence distribution). In addition, sensitivity analysis of the ATRP model showed that the algorithm can be adequately applied to a wide range of cases with extreme rate constants. While SAA offers a speedier approach to simulate radical polymerization reactions, it is crucial to note that this acceleration process incurs a minor compromise in accuracy, particularly in the calculation of z-average molecular weight (M z ). The algorithm currently accounts for basic polymerization reaction schemes, assuming rapid ordinary differential equation solving and omitting chain-length dependencies.

show abstract

“…[12,[29][30][31][47][48][49][50] In PRE, the kMC method has been successfully applied for both polymerization (e.g., homogeneous free radical, [11,15,[51][52][53] nitroxide mediated, [54][55][56] atom transfer radical polymerization, [57][58][59][60][61] and dispersed phase thus heterogeneous polymerization [62][63][64][65][66][67][68] ), polymer modification [27,69,70] as well as polymer recycling, both via chemical and mechanical means. [71][72][73][74] As opposed to conditional Monte Carlo, [75][76][77] which deals with a posteriori mimicking of chemical structures simplifying the natural flow of reaction kinetics, the kMC method is based on the stochastic simulation algorithm (SSA), as proposed in the seminal work of Gillespie for reactions between elemental species. [16,78] According to SSA, each considered chemical reaction between species is listed as a reaction channel, the time evolution is tracked by sampling a stochastic time step, and a reaction channel is selected based on its probability of occurrence.…”

Section: Introductionmentioning

confidence: 99%

“…[ 12,29–31,47–50 ] In PRE, the k MC method has been successfully applied for both polymerization (e.g., homogeneous free radical, [ 11,15,51–53 ] nitroxide mediated, [ 54–56 ] atom transfer radical polymerization, [ 57–61 ] and dispersed phase thus heterogeneous polymerization [ 62–68 ] ), polymer modification [ 27,69,70 ] as well as polymer recycling, both via chemical and mechanical means. [ 71–74 ]…”

Section: Introductionmentioning

confidence: 99%

A Signal‐To‐Noise‐Ratio‐Based Automated Algorithm to accelerate Kinetic Monte Carlo Convergence in Basic Polymerizations

Trigilio,

Marien,

De Smit

et al. 2023

Advcd Theory and Sims

Self Cite

View full text Add to dashboard Cite

Kinetic Monte Carlo (kMC) modelling is ubiquitous to simulate the time evolution of (bio)chemical processes, specifically if populations are involved. A recurring task is the selection of the smallest control volume that leads to convergence, which means that the model outputs are accurate and sufficiently free from stochastic noise and do not significantly change upon further increasing this volume. Selecting a too high (safe) control volume leads to an excessive simulation time, while many small incremental control volume increases are inefficient. This work therefore presents an automated tool to determine the smallest control volume leading to convergence. The tool is illustrated for (intrinsic) free radical and nitroxide mediated polymerization (FRP/NMP), in which the chain length distribution (CLD) is a crucial output. The algorithm starts with a very low volume to then check if the desired (monomer) conversion can be reached, the number average chain length is accurate, and finally the signal‐to‐noise (SNR) ratio at the CLD level is below a threshold. The execution time of the algorithm is less than twice the time of running the converged simulation directly, hence, saving tremendous time in setting up a kMC simulation and facilitating benchmark studies even beyond polymer reaction engineering applications.

show abstract

Bayesian tuned kinetic Monte Carlo modeling of polystyrene pyrolysis: Unraveling the pathways to its monomer, dimers, and trimers formation

Cited by 12 publications

References 112 publications

Sustainable bioconversion of synthetic plastic wastes to polyhydroxyalkanoate (PHA) bioplastics: recent advances and challenges

Sustainable bioconversion of synthetic plastic wastes to polyhydroxyalkanoate (PHA) bioplastics: recent advances and challenges

Scaling Acceleration Algorithm for Hybrid Kinetic Monte Carlo Simulation of Linear Radical Polymerization

A Signal‐To‐Noise‐Ratio‐Based Automated Algorithm to accelerate Kinetic Monte Carlo Convergence in Basic Polymerizations

Contact Info

Product

Resources

About