AI Scrap Grading With Carbon Emission Calculation
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Solution Overview
Problem
Current metal scrap grading and carbon emission calculation methods are inefficient and lack universality across international markets, particularly for small distribution companies, due to reliance on visual inspection and country-specific AI models.
Innovation Solution
A recycled metal processing system using AI that includes a vision camera and grade determining unit to classify metal scrap grades based on preset criteria, and a carbon emission calculating unit to quantify emissions, employing filtering and Fourier transform techniques to enhance image analysis and neural networks for accurate grading and emission calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If visual inspection by inspectors is used to determine metal scrap grades, then the system is simple and easy to implement, but the inspection efficiency is low and productivity is reduced
Solution Approach 1:
The patent replaces the mechanical visual inspection system (inspectors using eyes and monitors) with an AI-based automated inspection system using vision cameras and neural networks. This substitution dramatically improves inspection efficiency while the modular AI system design keeps complexity manageable.
Solution Approach 2:
The AI inspection system performs self-service by automatically capturing images, analyzing metal scrap grades, and generating inspection reports without human intervention. The system serves itself through automated image processing and grade determination algorithms.
2Measurement precision
If country-specific AI models are trained according to domestic standards, then the model accuracy for local markets is improved, but the adaptability to international transaction markets deteriorates
Solution Approach 1:
The patent creates a universal AI inspection model that can determine metal scrap grades across different countries and standards. The system is designed to handle multiple quality criteria and standards simultaneously, making it adaptable to international transaction markets while maintaining accuracy for local requirements.
Solution Approach 2:
The system allows dynamic adjustment of quality criteria parameters to match different country standards. By changing the parameter sets used in the AI model, the same system can adapt to various international standards without requiring separate country-specific models.
3Extent of automation
If AI inspection is implemented for metal scrap grading, then the measurement precision and automation level are improved, but the device complexity and implementation cost increase
Solution Approach 1:
The patent divides the AI inspection system into modular components: image capture module, image processing module, grade determination module, and report generation module. This segmentation allows the system to achieve high automation while managing complexity through independent, reusable components.
4Reliability
If post-compensation procedures are used for quality deviation in metal scrap transactions, then the measurement precision can be adjusted after delivery, but the loss of time and additional costs are incurred
Solution Approach 1:
The patent performs quality inspection and grade determination before metal scrap transactions occur. By conducting AI-based inspection in advance, the system provides reliable quality assurance upfront, eliminating the need for time-consuming post-delivery compensation procedures and reducing transaction time.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables rapid, accurate classification of metal scrap grades and calculation of carbon emissions, facilitating efficient quality inspection and environmental impact assessment, applicable to both distribution and steel companies.
Implementation Method 1
a vision camera installed above metal scrap that is crushed, the vision camera being configured to obtain a captured image by capturing an image of an upper part of the metal scrap
Implementation Method 2
a filtering unit configured to filter out shadow noise formed by lighting, a thermal noise component, and a dust noise component formed by scattering dust in the obtained captured image
Implementation Method 3
a Fourier transform unit configured to perform Fourier transform on the filtered captured image
Data Source
AI summary
A recycled metal processing system and method using artificial intelligence (AI) and a computer program for the same are discloses. The recycled metal processing system using AI may include: a vision camera installed above metal scrap that is crushed, the vision camera being configured to obtain a captured image by capturing an image of an upper part of the metal scrap; and a grade determining unit configured to determine a grade of the metal scrap according to a preset quality criterion, by analyzing the captured image based on an AI model.