ASPHALTENE PRECIPITATION PREDICTION DURING BITUMEN RECOVERY: EXPERIMENTAL APPROACH VERSUS POPULATION BALANCE AND CONNECTIONIST MODELS
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Date
2022
Authors
Yerkenov, Turar
Tazikeh, Simin
Tatar, Afshin
Shafiei, Ali
Journal Title
Journal ISSN
Volume Title
Publisher
ACS Omega
Abstract
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 preprocessing
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.
Description
Keywords
Type of access: Open Access, Bitumen Recovery, Population Balance
Citation
Yerkenov, T., Tazikeh, S., Tatar, A., & Shafiei, A. (2022). Asphaltene Precipitation Prediction during Bitumen Recovery: Experimental Approach versus Population Balance and Connectionist Models. ACS Omega, 7(37), 33123–33137. https://doi.org/10.1021/acsomega.2c03249