Related Experiment Video
Updated: Jan 7, 2026
12:11
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
Published on: April 8, 2020
8.6K
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
Summary
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.
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.
Related Concept Videos
01:46Oxidative Cleavage of Alkenes: Ozonolysis
12.6K
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.
Ozone is a symmetrical bent molecule stabilized by a resonance structure.
12.6K
01:20Maxwell-Boltzmann Distribution: Problem Solving
2.8K
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).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
2.8K
01:24Laminar Flow: Problem Solving
478
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...
478
01:25Atomic Absorption Spectroscopy: Atomization Methods
1.4K
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...
1.4K
01:17Accelerating Fluids
2.0K
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.
The motion of the liquid within this infinitesimal cylinder is considered to obtain the pressure difference. Three vertical forces act on this liquid:
The motion of the liquid within this infinitesimal cylinder is considered to obtain the pressure difference. Three vertical forces act on this liquid:
2.0K
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
255
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
255