How AI is Revolutionising Waste & Rubbish Sorting

by All Gone Admin

Our growing waste problem devastates our planet faster than we could imagine. And every second, a lot of stuff is thrown in our bins. But what happens to the rubbish thrown in these bins remains a mystery to most.

While some of these wastes might end up elsewhere, general garbage bins go straight into landfills. In Australia alone, the country generates a whopping 67 million tonnes of waste yearly. That’s a lot.

Unfortunately, Australians only recycle half of their waste, sending almost a third to landfills. Despite efforts to solve this, waste management issues persist. For instance, inadequate facilities make waste collection and disposal challenging.

Adding to that problem, we’re throwing more rubbish of non-biodegradable and hazardous wastes. This often results in waste segregation and contamination issues. At worst, inadequate education and awareness about proper disposal are common even in developed cities and suburbs.

To combat these issues, we need more innovative solutions to manage waste. Integrating AI, or artificial intelligence, solves these problems and reshapes the waste management landscape. Its ability to process vast amounts of data and learn from patterns revolutionises how we sort and recycle waste.

This powerful tool helps us improve recycling to achieve our sustainability goals. This article explores AI’s current state in waste sorting, its benefits, and the promising future it holds.

Understanding AI in Waste Sorting

We often hear about self-driving cars and digital assistants. But there’s more to AI than these that benefit the planet. AI is changing how we collect, transport and sort out all types of rubbish.

Traditional waste collection methods are often inefficient due to irregular schedules. But AI solves this with intelligent waste collection systems. AI can accurately and efficiently sort rubbish when combined with machine learning, computer vision and robotics automation.

To better understand this, let’s understand how AI-driven waste sorting works. AI technologies are often integrated into smart bin systems. These are equipped with sensors to monitor the waste levels in real-time.

Computer vision classifies patterns and features of thrown objects. For example, mixed plastics can be precisely separated from paper streams during the sorting process. Robotic automation sorts collected waste into its respective bins.

These smart bins also incorporate machine learning to predict the level of rubbish thrown accurately. Once smart bins are full, AI technologies help collect data on traffic and weather forecasts to optimise routes and ensure efficient collection. Users then receive notifications once these bins are emptied.

Benefits of AI in Waste Sorting

Managing wastes efficiently ensures a clean and sustainable environment. While it protects workers from risks, AI-driven waste sorting also offers a myriad of benefits. These include:

Enhanced Accuracy

While traditional manual methods are still prevalent in most cities, AI-driven systems enhance the accuracy of waste sorting. This is due to several factors such as:

Objective classification - AI algorithms can be trained and learned to recognise and sort various materials like plastics, paper, metals, glass, food or organic wastes. This is often augmented with image recognition, machine vision and sensor-based sorting techniques.
Continuous learning - Advanced machine-learning algorithms can be continuously trained so it can extract detailed features from waste item images. This also allows systems to adapt to changes in waste composition to improve sorting accuracy over time.
Reduced human error - By reducing the need for manual labour, AI-automated sorting process minimises human errors. This helps streamline recycling operations by detecting defects, misclassification, contamination and non-compliance.

Increased Efficiency

AI-driven inspection systems often ensure higher-quality recycled materials. Aside from that, it offers significant efficiency gains compared to traditional methods:

Faster processing time - AI technologies can process waste items at a much faster rate than a manual workforce which reduces overall processing time and improves throughput.
Reduced errors - AI systems accurately sort out contaminants and waste types that can potentially slow down operations or lead to misclassification.
Handle large volumes - this allows waste management infrastructure to handle larger waste volumes without the need to increase resources or manpower.
Reduced resource utilisation: AI algorithms collect traffic and weather conditions to optimise collection schedules without delays, improving operational efficiency.
24/7 Operation: AI systems sort and operate without the need for breaks or rest periods, ensuring uninterrupted waste processing.

Cost Savings

Integrating AI technologies in waste management can be hefty. This often requires specialised hardware, software and computational resources to handle massive databases. Regardless of these upfront costs, this offers long-term cost savings for waste management facilities, including:

Reduced labour costs - Automating the sorting process reduces the need for human labour which leads to lower labour costs.
Lower processing expenses - Enhancing accuracy and efficiency can result in lower waste processing expenses. This can be achieved as fewer errors and faster processing time reduce the overall costs of handling, collecting and sorting waste.
Lower transport costs - Transportation accounts for almost 80% of the cost during waste collection. But AI technologies prevent disorganised collection plans that may incur significant costs from waste logistics and transportation processes.
Revenue from higher-quality recyclables - AI-driven sorting processes can produce higher-quality recyclables. These can then be sold for higher prices which potentially increases revenue for waste management facilities.
Reduced disposal costs - By reducing contaminants and improving recycling rates, AI systems help reduce heaps of rubbish that require landfill disposal, resulting in lower disposal costs.

Environmental Impact

Oftentimes, opting for recyclable, green products helps reduce waste products. While most commit to recycling, refurbishing or upcycling, countless items still end up in landfills. These might not be enough.

But by enhancing sorting accuracy, we can effectively reduce waste volumes sent to landfills. AI-driven waste sorting systems do this, which positively impacts the environment, including:

Reduce landfill waste - Improving recycling rates and preventing contamination helps divert waste from landfills. This conserves more space while reducing the environmental impacts of landfilling.
Lower greenhouse emissions: Landfills contribute to massive greenhouse emissions like methane and carbon dioxide. Reducing rubbish sent to landfills with AI-driven sorting helps lower greenhouse emissions and mitigate climate change.
Conserve natural resources - Recycling materials through AI-driven sorting reduces the need for natural materials necessary for manufacturing processes. This also means reducing the need to extract more resources from our forests.
Prevent pathological contamination - Traditional waste sorting and collection often exposes workers to risks, especially when excretory wastes combine with domestic wastes. This can be prevented by automating operations with AI, ensuring safer waste handling while preventing further soil and water contamination.
Reduce pollution - machine learning algorithms using AI help in measuring and separating impurities from raw materials, compost and other waste types. Reducing these impurities from the landfill and incineration process lowers soil, water and air pollutants.

Current Applications of AI in Waste Management

Most municipalities in Australia spend over 35% of their budget to manage solid waste. But much of this rubbish is either getting dumped in landfills or incinerated. But where in Australia has successfully implemented AI waste sorting systems?

In Victoria and New South Wales, the first recycling robots used AI to sort out different types of cartons. This robot automation has been training, learning to recognise all sorts of beverage cartons from the residents’ yellow-lidded bins. But how do these robots sort cartons from heaps of rubbish?

These smart robots use AI-powered sensors to make multiple picks per minute. This increases the purity of sorted materials, significantly reducing contamination and improving overall recycling rates.

Meanwhile, the University of Melbourne is developing an advanced waste-sorting prototype platform. This prototype uses hyperspectral imaging that differentiates waste materials based on their physical and chemical properties. These properties are often indistinguishable from the human eye, making sorting efficient and accurate.

The machine’s AI-enabled technologies aim to sort local waste streams, including plastics and e-waste. It uses advanced imaging systems, which enhance separation accuracy. This will also be upgraded by sorting hazardous waste generated from decommissioned oil and gas infrastructure facilities.

The Future of AI in Waste Sorting

AI technologies employ advanced algorithms to accurately identify recyclable materials rapidly. This swiftly increases the purity of recyclables and helps reduce the contamination rate, which may result in incorrectly sending items to landfills.

On the other hand, converging AI and IoT (Internet of Things) is promising to revolutionise waste management. IoT is a system of interconnected devices linked via the internet. When AI-enabled sensors are connected to these IoT devices, it’s possible to monitor waste composition, volume and location in real-time. This not only improves operational efficiency but also avoids unnecessary collection trips.

Further, robotic systems potentially offer greater automation in waste sorting facilities. When equipped with AI, these robots can accurately identify and sort waste items, far exceeding human capabilities. And this could result in increased efficiency, reduced labour costs and improved safety.

The Role of AI in a Circular Economy

But what if sorting items demands more resources and costs than how much these recycled materials can be sold? This is one of the barriers to the circularity of the economy. In a circular economy, we need to repeatedly use or consume things while minimising resource demands, waste and emissions.

AI-driven sorting perfectly aligns with the principles of a circular economy. Its ability to accurately sort and identify waste maximises recycling rates and reduces rubbish sent to landfills. This contributes to our attaining a more sustainable and resource-efficient economy.

AI is refining how we manage our waste. By leveraging this powerful tool and combining it with computer vision and machine learning, we’re on the path to enhancing the accuracy, efficiency, and sustainability of our waste management system.

As AI advances, it is essential to stay informed about the latest innovations and consider adopting AI-driven solutions in managing your waste operations. By embracing these technologies, we can contribute to a healthier, more sustainable planet for future generations.