The recycling industry makes use of artificial intelligence (AI) to scan mixed waste streams and identify the different materials in them. Using machine and deep learning techniques, it is possible to continuously improve the way paper, plastics, and metals are identified and categorized in accordance with their color, size, shape, brand, and other characteristics.
Uses of Artificial Intelligence (AI) and Machine Learning in the Recycling Industry
With this fast-moving intelligent robotics system, you are able to automate the physical task of sorting, picking, and placing recyclable materials based on data that you receive. With accuracy of up to 99%, this high-speed robot can complete the physical task of sorting, picking, and placing recyclables at a rate of 100 items every minute.
Benefits in a circular economy are as follows:
Almost 300 billion dollars worth of recyclable materials are estimated to go unrecycled each year, and this amount is estimated to be more than 300 billion dollars. It is a major challenge to recover material from waste streams due to the economics and efficiency involved in identifying and sorting paper, plastics, metals, and other recyclable materials.
It has also been challenging to meet the stringent quality standards for the import of contamination-free recyclable materials in recent years, leaving the waste industry looking for cost-effective solutions to meet these standards.
A recycling center in the United States dedicated to recycling has been able to increase the volume of recycled material by 10% since installing artificial intelligence to collect more recyclables from waste streams and produce an increase in high-quality secondary materials.
Materials are usually sorted by humans, but the job is physically demanding, there are safety concerns, and there is a risk of human error involved with it. When humans and robots work together in harmony, the workforce can be stabilized and the quality of full-time jobs will be improved.
The capital costs of the technology at the USA plant were offset by the reduction of recruitment and training costs for hard-to-find temporary workers, which offset the capital costs of the technology.
The benefits of artificial intelligence and machine learning in recycling
With the use of artificial intelligence and automation, waste sorting performance can be improved, operating costs can be reduced, and secondary resources can be valued more.
Material recovery facilities can use the data captured to save time and lower costs by:
Verify that resale material is free of contamination to avoid costly rejections
Processes for manual monitoring that are error-prone are eliminated
By anticipating issues before they arise, it is possible to prevent system downtime
Recognizing hazards that could pose a risk to the health and safety of employees
In order to optimize operations, it is important to identify trends in volume and material composition
Maximize the profits of recycling plants by maximizing their productivity
The sustainability of the environment in the United States