Development of a Population Balance Model to Describe the Influence of Shear and Nanoparticles on the Aggregation and Fragmentation of Asphaltene Aggregates

The precipitation and deposition of asphaltenes is a primary problem related to the processing, transportation, and production of oil. Flocculation of asphaltene aggregates is likely to occur during the production and processing of crude oil. Recently, it has been shown that nanotechnology in the form of nanopartides is useful for the inhibition or prevention of asphaltene formation damage. Although it is well-known that the adsorption of asphaltenes on the nanopartide surface would reduce the capacity of these asphaltic compounds to interact with each other, limited studies have been performed regarding the processes and the mechanisms associated with the effect of nanopartides on the inhibition of the formation damage due to asphaltenes. To better understand this phenomenon from a mathematical approach, a population balance model (PBM) is proposed to describe the kinetics of asphaltene flocculation-fragmentation in the presence of nanoparticles. The model assumes that asphaltenes in the presence of a shear rate are related to the aggregation and fragmentation phenomena and includes a term related to the asphaltene adsorption on nanopartides. An adsorption kinetic term was introduced into the model using the double exponential model. Experimental data of the kinetics of asphaltene aggregation were obtained by dynamic light scattering (DLS) measurements at a fixed initial asphaltene concentration of 1000 mg/L and with different Heptol mixtures. In this study, commercial silica, gamma-alumina, and magnetite nanoparticles were used as adsorbents to study the effect of the chemical nature of the nanopartides on the inhibition of the asphaltene growth and for model validation. Additionally, to demonstrate the versatility of the proposed model, the effect of asphaltene was also evaluated. The obtained results from the proposed population balance model agree well with the experimental data, within an RSME % < 9%.