Recycling waste classification using emperor penguin optimizer with deep learning model for bioenergy production.

Area of Science:

• Sustainable Energy
• Waste Management
• Artificial Intelligence

Background:

• The increasing demand for renewable energy necessitates efficient resource management.
• Waste recycling is crucial for mitigating global resource strain and promoting sustainability.
• Manual waste classification is inefficient and costly, highlighting the need for automated solutions.

Purpose of the Study:

• To introduce a novel deep learning model for classifying recyclable waste materials.
• To enhance bioenergy production through improved waste recognition and sorting.
• To develop an automated system for efficient and accurate waste classification.

Main Methods:

• A two-stage recycling waste classification using emperor penguin optimizer with deep learning (RWC-EPODL) model was developed.
• The AX-RetinaNet model, optimized with Bayesian optimization (BO), was used for initial waste object recognition.
• The emperor penguin optimizer (EPO) algorithm fine-tuned a stacked auto-encoder (SAE) model for waste classification.

Main Results:

• The RWC-EPODL model achieved a high success rate of 98.96% in waste classification.
• Experimental validation demonstrated the model's effectiveness in recognizing and classifying diverse waste materials.
• Comparative analysis confirmed the superior performance of the RWC-EPODL model over existing approaches.

Conclusions:

• The RWC-EPODL model offers a highly accurate and efficient solution for automated waste classification.
• This approach significantly contributes to advancing sustainable bioenergy production through effective recycling.
• The study validates the potential of deep learning and optimization algorithms in revolutionizing waste management practices.