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Updated: Aug 27, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for

Zeeshan Ul Haq¹, Hafeez Ullah¹, Muhammad Nouman Aslam Khan¹

¹Laboratory of Alternative Fuel and Sustainability, School of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan.

Bioresource Technology

|September 26, 2022

Summary

Machine learning models optimized with genetic algorithms and particle swarm optimization accurately predict biochar yield. The Ensembled Learning Tree (ELT-PSO) model offers a user-friendly software solution for efficient biochar production analysis.

Keywords:

Artificial Intelligence Biomass Metaheuristic Techniques Partial Dependence Analysis Pyrolysis

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Last Updated: Aug 27, 2025

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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Published on: December 9, 2012

Area of Science:

Agricultural Science
Environmental Science
Chemical Engineering

Background:

Biochar production is crucial for soil amendment and carbon sequestration.
Predicting biochar yield accurately requires understanding complex interactions between biomass properties and process conditions.
Existing methods for yield prediction can be time-consuming and resource-intensive.

Purpose of the Study:

To develop and compare machine learning (ML) models integrated with optimization algorithms for biochar yield prediction.
To identify the most effective ML-PSO/GA model for analyzing biomass properties and process conditions influencing biochar yield.
To create a practical software tool for accessible biochar yield estimation.

Main Methods:

Development of ML models integrated with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).
Comparative analysis of different ML algorithms (e.g., Ensembled Learning Tree) coupled with GA and PSO.
Utilizing Partial Dependence Plots (PDPs) to analyze parameter effects and interactions on biochar yield.
Development of a user-friendly software interface based on the optimal ML-PSO model.

Main Results:

The Ensembled Learning Tree integrated with Particle Swarm Optimization (ELT-PSO) model demonstrated superior performance, achieving R² = 0.99 and RMSE = 2.33.
PDP analysis revealed significant impacts and interactions of biomass properties and process parameters on biochar yield.
The developed GUI-based software achieved prediction accuracy within 2% of experimental yields, simplifying the process.

Conclusions:

Optimized ML models, particularly ELT-PSO, provide highly accurate biochar yield predictions.
The developed software facilitates efficient and cost-effective biochar yield estimation, reducing the need for extensive experimentation.
This approach enhances the understanding and optimization of biochar production processes.