Prediction of Viscosity of Blends of Heavy Oils with Diluents by Empirical Correlations and Artificial Neural Network

Stratiev, Dicho; Nenov, Svetoslav; Shishkova, Ivelina; Sotirov, Sotir; Sotirova, Evdokia; Dinkov, Rosen; Yordanov, Dobromir; Pilev, Dimitar; Atanassov, Krassimir; Vasilev, Svetlin; Stratiev, Danail Dichev

doi:10.1021/acs.iecr.3c02472

Cited by 7 publications

(4 citation statements)

References 41 publications

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“…A clear exponential decrease in the reduction of the viscosity of HCO is observed. This behavior is in line with others reported in the literature . This exponential reduction is more representative at values between 5 and 40 vol % diluent because of the low viscosity of the diluent compared with that of HCO.…”

Section: Resultssupporting

confidence: 92%

Techno-Economic Study of the Effect of Different Distillates and Crude Oil Diluents on the Transportation by Pipeline of Heavy Crude Oil

Muñoz,

Ancheyta

2024

Ind. Eng. Chem. Res.

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The transportation of a heavy crude oil of 12.71° API was studied by dilution with different hydrocarbons (crude oils and their distillates) in blending ratios from 95/5 to 5/95 vol % HCO/diluent. The target of the maximum viscosity value of the blends was fixed at 300 cSt at 37.8 °C to allow the crude oil to be transported. The evaluation was carried out to transport in a 10 km pipeline 10 000 BPD of heavy crude oil with the different diluents. As expected, the viscosity of the blends is reduced when the diluent is lighter. The highest required amount of diluent was for Maya crude oil (64.2 vol %) and the lowest was for naphtha from Isthmus crude (19.7 vol %) to meet the required maximum viscosity. Capital expenditures resulted in 2.7 and 3.5 MMUSD for pipeline diameters of 4 and 6 in., respectively. The lowest operation expenditures (53.22 USD/bbl) were for dilution with straight-run heavy gasoil from Isthmus crude oil. This diluent also presented the lowest total blending cost. The contribution of pumping, maintenance, and labor costs was marginal, with less than 1% with respect to the total blending cost.

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Section: Resultssupporting

confidence: 92%

Techno-Economic Study of the Effect of Different Distillates and Crude Oil Diluents on the Transportation by Pipeline of Heavy Crude Oil

Muñoz,

Ancheyta

2024

Ind. Eng. Chem. Res.

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“…Having in mind that the artificial neural network (ANN) approach to model different oil properties reported a higher accuracy of prediction than that of the regression methods [106][107][108][109][110][111][112][113], we decided to develop an ANN model to predict the Sp critical of petroleum fluids. For this purpose, we used more data than those listed in Table 2 and collected data on 110 crude oils and residues from the literature [34,[42][43][44], as well as some additional unpublished data of our own.…”

Section: Crude Oil Properties and Compatibility Index Relationsmentioning

confidence: 99%

Application of Intercriteria and Regression Analyses and Artificial Neural Network to Investigate the Relation of Crude Oil Assay Data to Oil Compatibility

Shiskova,

Stratiev,

Tavlieva

et al. 2024

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The compatibility of constituents making up a petroleum fluid has been recognized as an important factor for trouble-free operations in the petroleum industry. The fouling of equipment and desalting efficiency deteriorations are the results of dealing with incompatible oils. A great number of studies dedicated to oil compatibility have appeared over the years to address this important issue. The full analysis of examined petroleum fluids has not been juxtaposed yet with the compatibility characteristics in published research that could provide an insight into the reasons for the different values of colloidal stability indices. That was the reason for us investigating 48 crude oil samples pertaining to extra light, light, medium, heavy, and extra heavy petroleum crudes, which were examined for their colloidal stability by measuring solvent power and critical solvent power utilizing the n-heptane dilution test performed by using centrifuge. The solubility power of the investigated crude oils varied between 12.5 and 74.7, while the critical solubility power fluctuated between 3.3 and 37.3. True boiling point (TBP) analysis, high-temperature simulation distillation, SARA analysis, viscosity, density and sulfur distribution of narrow petroleum fractions, and vacuum residue characterization (SARA, density, Conradson carbon, asphaltene density) of the investigated oils were performed. All the experimentally determined data in this research were evaluated by intercriteria and regression analyses. Regression and artificial neural network models were developed predicting the critical solubility power with correlation coefficients R of 0.80 and 0.799, respectively.

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“…For example, a machine learning approach was proposed to predict micropollutant ozone reaction rate constants . Prediction of viscosity of diluent-heavy oil mixtures has been realized based on artificial neural networks (ANN) . A machine learning approach was proposed for estimating the surface area of porous silica particles .…”

Section: Introductionmentioning

confidence: 99%

Intelligent Soft Measurement of Catalyst Activity during a Polyethylene Reaction Based on Mechanistic Modeling and Deep Learning

Tian,

Ji,

Wang

et al. 2024

Ind. Eng. Chem. Res.

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The polymerization reaction is the core part of the polymerization process, where the catalyst has an important influence on the polymerization products. Catalyst activity, an essential operational indicator, is not directly monitored, resulting in the operator being unable to understand the operating conditions of the reactor fully. In this paper, an intelligent soft-measurement model based on mechanistic modeling and deep learning is proposed for predicting catalyst activity parameters in polyethylene reaction processes. First, a steady-state model of the polyethylene production process is established and utilized to collect data unavailable in the industry. A K-mean clustering method is used to cluster typical working conditions, and the collected data is used as inputs for mechanism modeling. Second, the primitive reactions in the polyethylene production process are studied to understand the mechanism of the polymerization process and to find the parameters that have the greatest impact on the catalyst activity. Then, the artificial neural network (ANN) deep learning method is selected, and its structure is optimized. The parameters and data provided by the mechanism model are integrated to constitute a complete data set. The catalyst activity characterized by the finger forward factor is predicted using the artificial neural network deep learning model. Finally, the self-built soft measurement model is validated with a maximum relative error of 0.44%. The validation results show that this method can reasonably predict the catalyst activity and guide the production process.

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Prediction of Viscosity of Blends of Heavy Oils with Diluents by Empirical Correlations and Artificial Neural Network

Cited by 7 publications

References 41 publications

Techno-Economic Study of the Effect of Different Distillates and Crude Oil Diluents on the Transportation by Pipeline of Heavy Crude Oil

Techno-Economic Study of the Effect of Different Distillates and Crude Oil Diluents on the Transportation by Pipeline of Heavy Crude Oil

Application of Intercriteria and Regression Analyses and Artificial Neural Network to Investigate the Relation of Crude Oil Assay Data to Oil Compatibility

Intelligent Soft Measurement of Catalyst Activity during a Polyethylene Reaction Based on Mechanistic Modeling and Deep Learning

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