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Mathematical modeling and machine learning-based optimization for enhancing biofiltration efficiency of volatile

Muhammad Sulaiman¹, Osamah Ibrahim Khalaf², Naveed Ahmad Khan^3,4

¹Department of Mathematics, Abdul Wali Khan University, 23200, Mardan, Pakistan.

Scientific Reports

|July 23, 2024

Summary

This study models biofiltration of volatile organic compounds (VOCs) using Elman neural networks (ENN) and Levenberg-Marquardt (LM) optimization. The ENN-LM method accurately predicts methanol and p-pinene saturation in biofilms, offering a robust tool for chemical engineering applications.

Keywords:

Artificial Intelligence Elman neural networks Mathematical modeling Michaelis-Menten kinetics Optimization Reaction mechanism Supervised machine learning Volatile organic compounds

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Area of Science:

Environmental Engineering
Biotechnology
Computational Chemistry

Background:

Biofiltration is a biological pollution management technique using bioreactors to degrade pollutants.
Volatile organic compounds (VOCs) pose environmental and health risks, necessitating effective removal strategies.
Mathematical modeling is crucial for understanding and optimizing biofiltration processes.

Purpose of the Study:

To develop and validate a novel computational approach for modeling biofiltration of mixed VOCs.
To investigate the saturation dynamics of hydrophilic (methanol) and hydrophobic (p-pinene) VOCs in a biofilter.
To assess the efficiency and accuracy of an Elman neural network (ENN) coupled with Levenberg-Marquardt (LM) optimization for this task.

Main Methods:

Developed a mathematical model based on nonlinear diffusion equations and Michaelis-Menten kinetics.
Implemented a supervised machine learning algorithm using Elman neural networks (ENN).
Employed the Levenberg-Marquardt (LM) optimization algorithm to train the ENN for parameter estimation.

Main Results:

The ENN-LM technique accurately predicted the saturation of methanol and p-pinene under varying physical parameters.
The model demonstrated high performance, validated by low absolute errors, mean absolute deviations, and mean square errors.
Computational complexity was efficiently managed, showing the practicality of the developed method.

Conclusions:

The proposed ENN-LM approach provides a highly accurate and efficient numerical solution for biofiltration modeling.
This method effectively simulates the complex interactions of VOCs within biofilms.
The validated model can be applied to real-world chemical engineering problems for pollution control and process optimization.