Systematic Optimization of Asphaltene Molecular Structure and Molecular Weight Using the Quantitative Molecular Representation Approach

Abstract

We describe results of the ongoing development of a computer algorithm to optimize asphaltene molecular weight (MW) by systematically deriving quantitative molecular representations (QMRs) for asphaltenes and a comparison to experimental data. The QMR method consists of the generation and optimization of molecular structures based on experimental data, including elemental analysis, nuclear magnetic resonance, and MW. In this case, we use the MW as a variable in the problem, keeping all other experimental variables constant, and systematically investigate the effect of an increasing MW on the quality of the model optimization, as measured by the penalty or objective function. In this fashion, we find an optimal value for the MW using the QMR algorithm. We observed a significant increase in the objective function when optimizing sets of increasing target MW. We then analyzed the individual contributions of the different properties of the optimized sets toward the objective function and tested a number of possible alterations to the sampling parameters in an attempt to reduce the value of the objective function. It was found that constructing a single set of molecules, prior to optimization, from smaller subsets that are generated using a variety of sampling parameters could yield a great reduction in the value of the objective function following optimization. The resulting asphaltene molecular structures may be used as input for molecular dynamics simulations.