Economic benefit analysis of lithium battery recycling based on machine learning algorithm

Area of Science:

• Materials Science
• Energy Storage Systems
• Machine Learning Applications

Background:

• Lithium batteries are crucial for renewable energy and electric vehicles.
• The burgeoning lithium battery recycling industry faces challenges due to high recycling costs and difficulty in value assessment.
• Accurate economic evaluation is vital for the sustainable development of lithium battery recycling.

Purpose of the Study:

• To develop a robust model for analyzing and predicting the economic benefits of lithium battery recycling.
• To address the limitations of traditional methods in evaluating recycling value.
• To incorporate social and commercial values into the economic benefit analysis.

Main Methods:

• Application of machine learning, specifically combining backpropagation neural networks with stepwise regression.
• Design of an economic benefit analysis model for lithium battery recycling and utilization.
• Validation through experimental testing and comparison with actual recycling data.

Main Results:

• The developed model achieved a mean square error converging between 10^-6 and 10^-7.
• The model demonstrated a 33% improvement in convergence speed compared to existing methods.
• Predictions of economic benefits for a batch of recycled lithium batteries closely matched the true values.

Conclusions:

• The proposed model offers high accuracy and operational speed for economic benefit analysis in lithium battery recycling.
• It provides innovative tools and insights for the recycling industry.
• The model shows significant potential for evaluating the economic viability of lithium battery recycling operations.