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Related Concept Videos

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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
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Laminar flow occurs when a fluid moves smoothly in parallel layers with minimal mixing and turbulence. In fluid mechanics, ensuring laminar flow within a pipe is essential for precise control of flow characteristics, especially in engineering applications. The key factor in determining whether flow remains laminar is the Reynolds number, a dimensionless quantity that depends on the fluid's velocity, density, viscosity, and the pipe's diameter. A Reynolds number of 2100 or lower...
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Atomic Absorption Spectroscopy: Atomization Methods01:25

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Atomic Absorption Spectroscopy (AAS) atomizes samples through flame atomization or electrothermal atomization. Flame atomization typically involves a nebulizer and spray chamber assembly to combine the sample with a fuel–oxidant mixture, creating a fine aerosol mist that enters a burner. Typically, the fuel and oxidant are combined in an approximately stoichiometric ratio. However, for atoms that are easily oxidized, a fuel-rich mixture may be more advantageous. Only about 5% of the...
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When a fluid is in constant acceleration, the pressure and buoyant force equations are modified. Suppose a beaker is placed in an elevator accelerating upward with a constant acceleration, a. In the beaker, assume there is a thin cylinder of height h with an infinitesimal cross-sectional area, ΔS.
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Updated: Jan 7, 2026

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Intelligent delignification: leveraging explainable AI for ozone transport modeling and optimization.

Muhammad Rizwan1, Muhammad Ahmad Khan1, Sharifullah Khan1

  • 1School of Computing Sciences, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Haripur, 22650, Pakistan.

Scientific Reports
|December 29, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately predict lignin removal efficiency during eco-friendly ozonation pretreatment. This approach optimizes biomass processing for sustainable biofuel production by understanding key delignification factors.

Keywords:
DelignificationLigninMachine LearningModellingSHAPXAI

Related Experiment Videos

Last Updated: Jan 7, 2026

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.6K

Area of Science:

  • Biomass valorization and sustainable energy production.
  • Chemical engineering and process optimization.
  • Application of artificial intelligence in green chemistry.

Background:

  • Lignin, a major biomass component, hinders cellulose and hemicellulose accessibility due to its recalcitrant structure.
  • Efficient lignin removal is crucial for biofuel production, as lignin impedes hydrolysis and non-productively binds enzymes.
  • Ozonation is an emerging, eco-friendly delignification technique offering potential for improved biomass pretreatment.

Purpose of the Study:

  • To investigate the application of machine learning (ML) techniques for predicting lignin removal efficiency during ozonation pretreatment.
  • To evaluate the performance of various ML regression models in capturing the complex relationships in delignification.
  • To identify key process variables influencing lignin removal using feature importance analysis.

Main Methods:

  • Trained and evaluated 19 regression models using the PyCaret framework with experimental ozonation data.
  • Utilized Extra Trees Regressor, which showed the highest predictive accuracy.
  • Employed SHapley Additive exPlanations (SHAP) for interpreting feature importance and quantifying variable contributions.

Main Results:

  • The Extra Trees Regressor model achieved the highest predictive accuracy for delignification outcomes.
  • Machine learning models effectively captured the intricate relationships governing ozonation-based delignification.
  • SHAP analysis provided quantitative insights into the contribution of each process variable to lignin removal.

Conclusions:

  • Machine learning serves as a powerful predictive and interpretative tool for optimizing ozonation-based delignification.
  • Data-driven approaches utilizing ML can enhance the efficiency of biomass valorization for sustainable biofuel production.
  • This study demonstrates the potential of ML in advancing chemical engineering processes for greener energy solutions.