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Related Concept Videos

Oxidative Cleavage of Alkenes: Ozonolysis01:46

Oxidative Cleavage of Alkenes: Ozonolysis

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In ozonolysis, ozone is used to cleave a carbon–carbon double bond to form aldehydes and ketones, or carboxylic acids, depending on the work-up.
Ozone is a symmetrical bent molecule stabilized by a resonance structure.
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Oxidation of Alkenes: Syn Dihydroxylation with Osmium Tetraoxide02:44

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Alkenes are converted to 1,2-diols or glycols through a process called dihydroxylation. It involves the addition of two hydroxyl groups across the double bond with two different stereochemical approaches, namely anti and syn. Dihydroxylation using osmium tetroxide progresses with syn stereochemistry.
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Radical Autoxidation01:20

Radical Autoxidation

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The oxidation of an organic compound in the presence of air or oxygen is called autoxidation. For example, cumene reacts with oxygen to form hydroperoxide. Autoxidation involves initiation, propagation, and termination steps. Many organic compounds are susceptible to autoxidation—especially ethers in the presence of oxygen, which form hydroperoxides. Even though this reaction is slow, old ether bottles contain small amounts of peroxide, which leads to laboratory explosions during ether...
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Residual Plots01:07

Residual Plots

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A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
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Mass Spectrometry: Branched Alkane Fragmentation01:29

Mass Spectrometry: Branched Alkane Fragmentation

1.1K
This lesson delves into the mass spectrometry of branched alkane fragmentation. Branched alkanes possess secondary or tertiary carbon atoms, which generate relatively stable carbocations if the cleavage occurs at the branching point. The high stability of carbocations drives the instant fragmentation of branched alkanes. Accordingly, the branched alkane's molecular ion peak is very weak or invisible in the mass spectra, especially in comparison to a linear alkane.
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Oxidation of Alkenes: Anti Dihydroxylation with Peroxy Acids02:04

Oxidation of Alkenes: Anti Dihydroxylation with Peroxy Acids

6.1K
Diols are compounds with two hydroxyl groups. In addition to syn dihydroxylation, diols can also be synthesized through the process of anti dihydroxylation. The process involves treating an alkene with a peroxycarboxylic acid to form an epoxide. Epoxides are highly strained three-membered rings with oxygen and two carbons occupying the corners of an equilateral triangle. This step is followed by ring-opening of the epoxide in the presence of an aqueous acid to give a trans diol.
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Related Experiment Video

Updated: Sep 14, 2025

Original Experimental Approach for Assessing Transport Fuel Stability
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Modeling residue formation from crude oil oxidation using tree-based machine learning approaches.

Mohammad-Reza Mohammadi1, Seyyed-Mohammad-Mehdi Hosseini1, Behnam Amiri-Ramsheh1

  • 1Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.

Scientific Reports
|July 19, 2025
PubMed
Summary

Machine learning accurately predicts crude oil behavior during in-situ combustion (ISC) by modeling thermo-oxidative profiles and residue formation. The CatBoost model excels, aiding in effective fuel management for enhanced oil recovery.

Keywords:
Crude oilIn-situ combustionMachine learningResidue formationThermal EORThermo-oxidative behavior

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Area of Science:

  • Petroleum Engineering
  • Chemical Engineering
  • Machine Learning Applications

Background:

  • In-situ combustion (ISC) is vital for heavy oil recovery, relying on hydrocarbon oxidation and pyrolysis.
  • Understanding thermo-oxidative profiles and residue formation is key to optimizing ISC efficiency.
  • Existing models may not fully capture the complex interactions influencing residue generation.

Purpose of the Study:

  • To develop accurate predictive models for crude oil thermo-oxidative profiles and residue formation during ISC.
  • To compare the performance of advanced machine learning algorithms for this predictive task.
  • To identify key factors influencing residual crude oil content.

Main Methods:

  • Thermogravimetric analysis (TGA) data from 18 crude oils (API gravity 5-42) were used.
  • Four tree-based machine learning algorithms (CatBoost, LightGBM, RF, XGBoost) were employed.
  • Model performance was evaluated using Mean Absolute Relative Error (MARE) and determination coefficient (R²).

Main Results:

  • The CatBoost model demonstrated superior performance with a MARE of 4.95% and R² of 0.9993.
  • Temperature was the most significant factor influencing residual crude oil content (negative correlation).
  • Asphaltene, resin, and heating rate positively correlated with residual content, while API gravity showed a negative impact.

Conclusions:

  • The CatBoost model provides a reliable and accurate method for predicting crude oil behavior in ISC.
  • Effective management of fuel consumption and residue formation is achievable with these predictive capabilities.
  • The developed models support optimizing ISC processes for enhanced oil recovery.