Number of samples for each age and sex after removing samples with missing values.

Deasphalting bitumen using paraffinic solvent injection is a commonly used technique to reduce both its viscosity and density and ease its flow through pipelines. Common modeling approaches for asphaltene precipitation prediction such as population balance model (PBM) contains complex mathematical relation and require conducting precise experiments to define initial and boundary conditions. Machine learning (ML) approach is considered as a robust, fast, and reliable alternative modeling approach. The main objective of this research work was to model the effect of paraffinic solvent injection on the amount of asphaltene precipitation using ML and PBM approaches. Five hundred and ninety (590) experimental data were collected from the literature for model development. The gathered data was processed using box plot, data scaling, and data splitting. Data pre-processing led to the use of 517 data points for modeling. Then, multilayer perceptron, random forest, decision tree, support vector machine, committee machine intelligent system optimized by annealing, and random search techniques were used for modeling. Precipitant molecular weight, injection rate, API gravity, pressure, C5 asphaltene content, and temperature were determined as the most relevant features for the process. Although the results of the PBM model are precise, the AI/ML model (CMIS) is the preferred model due to its robustness, reliability, and relative accuracy. The committee machine intelligent system is the superior model among the developed smart models with an RMSE of 1.7% for the testing dataset and prediction of asphaltene precipitation during bitumen recovery.

Estimated parameters, α(q) and Cq for 2nd, 10th, 90th, and 98th centile for females.

Mathematical details of the computation of the means, variances, and covariance of the nonstationary Gompertz process.

Estimated parameters, α(q) and Cq for 2nd, 10th, 90th, and 98th centile for males.

Probability of crossing 3 population thresholds defined in Treves and Louchouarn (TL) [2] for various fall 2021 harvest scenarios when starting population is specified as a predicted “count” (Count) or a scaled occupancy estimate (Occ; Stauffer et al. [5]), and using parameterization from TL or corrected parameterization from this paper (Current).