Light hydrocarbons solvents solubility modeling in bitumen using learning approaches

Kuyakhi, Hossein Rajabi; Boldaji, Ramin Tahmasebi; Azadian, Meysam

doi:10.1080/10916466.2020.1863986

Cited by 5 publications

(2 citation statements)

References 46 publications

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“…Linear Models (e.g., Multivariate Linear Regression) provide a foundational approach but may struggle with complex relationships between solubility and influencing factors. Nonlinear Models, including ANN, − SVM, Decision Trees, Random Forests (RF), , Extreme Learning Machine (ELM), Adaptive-Network-Based Fuzzy Inference System (ANFIS), and Light Gradient Boosting Machine (LightGBM), can capture these complexities and often demonstrate superior accuracy. For instance, studies comparing MLR with ANNs have shown the latter’s ability to achieve more nuanced predictions .…”

Section: Crystal Properties Predictionmentioning

confidence: 99%

Recent Advances in the Application of Machine Learning to Crystal Behavior and Crystallization Process Control

Lu,

Rao,

Yue

et al. 2024

Crystal Growth & Design

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Crystals are integral to a variety of industrial applications, such as the development of pharmaceuticals and advancements in material science. To anticipate crystal behavior and pinpoint effective crystallization techniques, a thorough investigation of crystal structures, properties, and the associated processes is essential. However, conventional methods like experimental procedures and quantum mechanics calculations, while crucial, can be expensive and time-consuming. In response, machine learning has risen as an effective alternative, complementing the traditional approaches based on quantum mechanics and classical force fields. In the recent years, the deployment of machine learning in the realm of crystallization has yielded notable progress. This review offers a concise overview of the application of machine learning techniques in crystallization, focusing on the past five years. Our analysis of the literature indicates that machine learning has accelerated the prediction of crystal structures by streamlining the generation and evaluation of structures. Additionally, it has facilitated the prediction of key crystal properties such as solubility, melting point, and habit. The review further explores the role of machine learning in refining the control and optimization of crystallization processes, highlighting the restrictions of conventional algorithms and sensing technologies. The advantages of endto-end processing for enhancing the accuracy of predictions and the combination of data-driven with mechanism-based models for robustness are also considered. In summary, this review provides insights into the current state of machine learning in the field of intelligent crystallization and suggests pathways for future research and development.

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Section: Crystal Properties Predictionmentioning

confidence: 99%

Recent Advances in the Application of Machine Learning to Crystal Behavior and Crystallization Process Control

Lu,

Rao,

Yue

et al. 2024

Crystal Growth & Design

View full text Add to dashboard Cite

show abstract

“…On the other hand, the other two parameters have little effect on the viscosity of crude oil [51]. Good results in this field can be achieved by using appropriate statistical methods, modeling and artificial intelligence [53][54][55][56][57][58][59].…”

Section: The Effect Of Ultrasonic Frequencymentioning

confidence: 99%

The Effect of Ultrasonic Waves on Crude Oil Recovery

Tahmasebi-Boldaji

2023

Topics on Oil and Gas

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In recent years, ultrasonic technology has played an important role in the development of oil fields, which has improved oil recovery. Ultrasonic waves are a very suitable method for producing oil at a low cost and without environmental pollution. The reservoir is treated using high-power sonication, which affects the physical properties of the oil and thus improves the permeability, which increases the oil recovery. The ultrasonic technique is also used to reduce the damage of the formation in the areas near the well, and this reduces the penetration of mud and sediments. However, ultrasonic waves remove oil barriers to the well and improve oil recovery for a long time. In this chapter, recent developments and laboratory and field results of ultrasonic waves in improving oil recovery will be discussed, and it will be shown that these waves are highly efficient.

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Experimental investigation of ultrasonic cycle/magnetic stirrer (UC/MS) effect on water/α -Al2O3 nanofluid stability and thermal conductivity and its ANFIS/PSO modeling

Boldaji

Ghazanfari

Kuyakhi

et al. 2023

Results in Engineering

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Light hydrocarbons solvents solubility modeling in bitumen using learning approaches

Cited by 5 publications

References 46 publications

Recent Advances in the Application of Machine Learning to Crystal Behavior and Crystallization Process Control

Recent Advances in the Application of Machine Learning to Crystal Behavior and Crystallization Process Control

The Effect of Ultrasonic Waves on Crude Oil Recovery

Experimental investigation of ultrasonic cycle/magnetic stirrer (UC/MS) effect on water/α -Al2O3 nanofluid stability and thermal conductivity and its ANFIS/PSO modeling

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