Machine learning-based exploration of biochar for environmental management and remediation

Area of Science:

• Environmental Science
• Materials Science
• Data Science

Background:

• Biochar offers diverse environmental applications, including pollution prevention and heavy metal remediation.
• Challenges in biochar application generate extensive experimental data.
• Optimizing biochar for environmental remediation requires advanced analytical methods.

Purpose of the Study:

• To investigate machine learning applications in biochar processes for environmental remediation.
• To summarize recent advancements in biochar utilization for environmental management.
• To identify trends and future research directions in biochar-based environmental solutions.

Main Methods:

• Bibliometric analysis of biochar research trends, focusing on keywords like heavy metal, wastewater, and adsorption.
• Detailed review of machine learning techniques applied to biochar utilization.
• Analysis of experimental data to identify optimal variable combinations for biochar properties.

Main Results:

• Bibliometric analysis highlights heavy metal, wastewater, and adsorption as key research areas.
• Machine learning primarily targets adsorption efficiency and capacity in biochar applications.
• Identified trends indicate a growing interest in data-driven approaches for biochar optimization.

Conclusions:

• Machine learning can uncover hidden patterns in biochar data for accurate predictions.
• Optimized biochar properties through machine learning support decision-making in environmental management.
• This approach facilitates efficient resource allocation and enhances environmental remediation strategies.