The health of our planet earth's environment is gravely threatened by plastic waste. Since the 1950s, 8.3 billion metric tons of plastic have been produced worldwide, yet only 9% of it has been recycled. As a result, billions of tons of waste are poisoning ecosystems or decomposing in landfills.
It's a good thing that artificial intelligence (AI) can help in this situation. Early results show promise as researchers, corporations, and governments have begun using AI in almost every phase of the plastic life cycle.
Here are five strategies AI is using to combat plastic waste
Autonomous waste collection
Many pieces of plastic garbage end up in waterways, endangering aquatic life and affecting water quality. To solve this issue, researchers in Singapore have developed an AI-driven robot. The technology, known as Clearbot Neo, moves along the water's surface while utilizing machine vision to identify and catalog debris that it then picks up.
The daily capacity of Clearbot Neo is one metric ton of rubbish collection. It also gathers information about the types of rubbish and the locations where it appears as it goes along. This data can offer valuable insights into regional waste patterns, influencing future management decisions or legislation changes to address a region's particular problems.
Clearbot Neo can avoid aquatic life with the aid of machine vision. The robot can clean water systems in this way without interfering with the ecosystems' normal processes.
Automated sorting
Only the initial stage of the waste management process is the plastic collection. Organizations must then sift it in order to decide how to properly dispose of it. However, only 29% of worldwide e-waste makes it to the right recycling channel. AI can improve this method.
Robots with machine vision capabilities can assess waste to decide the appropriate channel for it. Like with many repetitive, data-intensive tasks, AI is much faster and more accurate than humans with this function. Some businesses have discovered that these robots can sort materials twice as precisely as humans, doubling recycled goods' resale value.
Recycling facilities can use AI to automate the sorting process to recover as much plastic waste as feasible. Since algorithms stay energized and focused, they will be less likely to make mistakes that would result in recyclable plastics ending up in landfills.
Discovering new disposal methods
After sorting, AI can assist in identifying new, less damaging ways to eliminate plastic garbage. Some machine learning algorithms can analyze tens of millions of options in a fraction of a second. They are able to do this to discover the best plastic disposal options that researchers might not have considered otherwise.
University of Texas at Austin researchers accomplished this recently. They found an enzyme that breaks down plastic in hours as opposed to the usual years using an advanced machine learning algorithm. The program examined various enzyme mutations to see which one would produce the greatest outcomes.
Without AI, such findings may call for days to months of research and lab effort. Machine learning can expedite the process by running numerous accurate simulations at once.
Optimizing package design
By reducing plastic waste from the start, AI can also contribute to its reduction. Some businesses have begun employing AI to create less wasteful packaging, as much of this garbage comes from packaging.
Amazon employed artificial intelligence to examine customer complaints to find shipment damage and overpackaging patterns. The algorithms might then discover the best ways to offer adequate protection during shipment while using the least amount of material. The ensuing package modifications decreased each item's carbon footprint and transportation expenses by 5%.
Similar to this, L'Oréal makes use of AI to predict how well new packaging concepts will succeed. The business can then determine the best approach to include more recycled materials in its packaging while using less raw plastic. These algorithms may enhance their predictions over time, leading to even better design adjustments.
Preventing manufacturing waste
By adjusting production levels to changing demand, predictive analytics can further aid in preventing plastic waste. Customers' needs might change quickly, leaving businesses with excess inventory that could go bad before use. By anticipating these changes, AI can decrease that waste.
When compared to traditional methods, AI-based demand estimates can cut forecasting mistakes by 30%–50%. Then, producers can more reliably scale up or down production in response to incoming changes. They will produce less waste if they can properly meet client demands.
Similarly, supply chain failures that may otherwise harm products can be decreased with the aid of AI forecasts. Less packaging is lost due to product loss in transportation, which contributes to even less plastic waste.
Use of plastic waste to curb pollution
The use of plastic waste to curb pollution is an important approach to address the environmental challenges associated with plastic pollution.
Here are several ways in which plastic waste can be utilized to mitigate pollution:
Recycling:
Recycling plastic waste is a crucial step in reducing pollution. Collecting and processing plastic waste can be transformed into new products, reducing the need for virgin plastic production.
Plastic-to-Fuel Conversion:
Advanced AI technologies can convert certain types of plastic waste into fuel. Through processes like pyrolysis or gasification, plastic waste can be transformed into various forms of energy, such as liquid fuel or gas.
Construction Materials:
Plastic waste can be incorporated into construction materials like bricks or paving blocks. Incorporating plastic waste into these materials diverts plastic from landfills and reduces the need for other resource-intensive materials like concrete.
Education and Innovation:
By promoting awareness about the environmental impact of plastic waste and supporting research and development in recycling technologies, society can explore new methods to effectively utilize plastic waste and minimize its harm to the environment.
Closing remarks
Plastic trash is a significant issue that calls for action from numerous parties across industries. The speed and accuracy of AI can make this enormous challenge much more manageable, enabling humans to accomplish much more than they could on their own. As more businesses adopt AI, the battle against plastic waste may favor the environment.
Although AI is a fantastic tool, it cannot solve the plastic problem on its own. Reducing the ecological impact of plastic will be made possible by implementing this technology throughout enterprises, agencies, and workflows.