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    AI Technology for Waste Management

    Leanest “Waste Sorting Robot” project with EAS and EU funding.

    AI technology can be integrated into waste management and sorting processes for achieving efficiency and optimization.

    In 2022 Leanest set the ambition to contribute to a cleaner environmental future. With the help of EAS and European funding, we have designed and developed a waste sorting robot that is operating on AI-based algorithms. This project was funded as part of the Union’s response to the Covid-19 pandemic.

    Leanest engineers have developed a robotic arm for waste sorting that can be installed to the existing manual sorting lines and ensure 60% more sorting efficiency and at the same time to reduce the amount of waste sent to incineration.


    About the Project

    The purpose of the project was to contribute to more accurate and faster sorting of collected packaging. The goal is to send at least 60% more packaging for recycling compared to the current situation and avoid burning them.

    In Estonia, as an example, the bulk of waste is manually sorted. This is also how the majority of smaller sorting centers in Europe work. When sorting by hand, it is enough to pick out about half of what is being sorted. The focus is on larger pieces or on items that can be sold and give more value.

    Leanest has designed and developed a sorting robot as a replacement for one person on the existing manual sorting lines of the collected packages. The first tests gave the result, that with the developed robotic arm operating on AI, it is possible to sort 60% more materials, which increases recycling and reduces waste incineration.

    This project was being supported by EU European Regional Development Fund by 125 354 euros.


    About Technology

    In the sorting mill the waste on the conveyor belt is being scanned via cameras, and information is processed by deep learning algorithms that perform a categorization for sorting decisions – different items on the conveyer belt will be addressed to the appropriate sorting or recycling method, consequently ensuring efficient and sustainable waste management. In our development project we have trained the AI algorithms to detect different types of plastic waste on the conveyor belt. Our focus in the AI training process was on reusable plastic and the aim to collect from the belt plastic items that were left unsorted in the manual sorting phase – as when sorting by hand, it is enough to pick out only about half of what is being sorted. Artificial Intelligence can contribute significantly to waste management, especially in regard to automated waste sorting at waste collecting and sorting mills and one major bottleneck in the process of waste management can be solved.

    In addition to the automated sorting decision making process also AI-operated robots can efficiently sort and lift the items instead of human labor. Robots and relevant technology can pull off the sorted items from the belt for further processing. Essentially, AI speeds up the whole process.

    Leanest waste sorting robot performs a lift with 1,8 seconds and potentially 33 lifts in a minute. Due to the small size of the robot and favorable unit price compared to the similar products on the market, it is possible to install several robotic arms on the same sorting conveyor line, depending on the sorting need of the types of materials.


    Benefits of AI Usage

    Contribute to a cleaner environmental future
    It is possible send at least 60% more packaging for recycling and avoid waste incineration.
    Automated process saves resources
    We replace routine manual work with an automated process and this allows significantly to save resources.
    Efficiency and stability
    Solutions based on artificial intelligence ensure stable and effective processes. AI can work 24/7 and we can reduce instability caused by the human factor.
    Improving occupational safety
    With the help of AI and corresponding devices, work processes can be executed safely that could otherwise potentially endanger the health of employees (e.g. sorting waste processes).