Assessing the effect of a liquid water layer on the adsorption of hydrate anti-agglomerants using molecular simulations.

We have performed molecular dynamics simulations to study the adsorption of ten hydrate anti-agglomerants onto a mixed methane-propane sII hydrate surface covered by layers of liquid water of various thickness. As a general trend, we found that the more liquid water that is present on the hydrate surface, the less favorable the adsorption becomes even though there are considerable differences between the individual molecules, indicating that the presence and thickness of this liquid water layer are crucial parameters for anti-agglomerant adsorption studies. Additionally, we found that there exists an optimal thickness of the liquid water layer favoring hydrate growth due to the presence of both liquid water and hydrate-forming guest molecules. For all other cases of liquid water layer thickness, hydrate growth is slower due to the limited availability of hydrate-forming guests close to the hydrate formation front. Finally, we investigated the connection between the thickness of the liquid water layer and the degree of subcooling and found a very good agreement between our molecular dynamics simulations and theoretical predictions.

Size dependence of the dissociation process of spherical hydrate particles via microsecond molecular dynamics simulations.

The dissociation process of spherical sII mixed methane-propane hydrate particles in liquid hydrocarbon was investigated via microsecond-long molecular dynamics simulations. A strong dependence of the melting temperature on the particle size was found. Analysis in the context of the Gibbs-Thomson effect provided insights into the fundamental properties of gas hydrates.

Molecular Dynamics Simulations of Asphaltene Aggregation: Machine-Learning Identification of Representative Molecules, Molecular Polydispersity, and Inhibitor Performance.

Molecular dynamics simulations have been employed to investigate the effect of molecular polydispersity on the aggregation of asphaltene. To make the large combinatorial space of possible asphaltene blends accessible to a systematic study via simulation, an upfront unsupervised machine-learning approach (clustering) was employed to identify a reduced set of model molecules representative of the diversity of asphaltene. For these molecules, single asphaltene model simulations have shown a broad range of aggregation behaviors, driven by their structural features: size of the aromatic core, length of the aliphatic chains, and presence of heteroatoms. Then, the combination of these model molecules in a series of mixtures have highlighted the complex and diverse effects of molecular polydispersity on the aggregation process of asphaltene. Simulations yielded both antagonistic and synergistic effects mediated by the trigger or facilitator action of specific asphaltene model molecules. These findings illustrate the necessity of accounting for molecular polydispersity when studying the asphaltene aggregation process and have permitted establishing a robust protocol for the in silico evaluation of the performance of asphaltene inhibitors, as illustrated for the case of a nonylphenol resin.

Ranking the Efficiency of Gas Hydrate Anti-agglomerants through Molecular Dynamic Simulations.

Using both computational and experimental methods, the capacity of four different surfactant molecules to inhibit the agglomeration of sII hydrate particles was assessed. The computational simulations were carried out using both steered and non-steered molecular dynamics (MD), simulating the coalescence process of a hydrate slab and a water droplet, both covered with surfactant molecules. The surfactants were ranked according to free energy calculations (steered MD) and the number of agglomeration events (non-steered MD). The experimental work was based on rocking cell measurements, determining the minimum effective dose necessary to inhibit agglomeration. Overall, good agreement was obtained between the performance predicted by the simulations and the experimental measurements. Moreover, the simulations allowed us to gain additional insights that are not directly accessible via experiments, such as an analysis of the mass density profiles, the diffusion coefficients, or the orientations of the long tails.