{"id":94672,"date":"2022-05-08T14:25:30","date_gmt":"2022-05-08T14:25:30","guid":{"rendered":"https:\/\/aijourn.com\/?p=94672"},"modified":"2022-05-08T14:25:38","modified_gmt":"2022-05-08T14:25:38","slug":"how-ai-is-fighting-plastic-waste","status":"publish","type":"post","link":"https:\/\/aijourn.com\/how-ai-is-fighting-plastic-waste\/","title":{"rendered":"How AI Is Fighting Plastic Waste"},"content":{"rendered":"\n

Plastic waste poses a daunting problem to Earth\u2019s environmental health. The world has generated 8.3 billion metric tons<\/a> of plastic since the 1950s but only recycled 9% of it. That leaves billions of tons degrading in landfills or polluting ecosystems as litter.<\/p>\n\n\n\n

Thankfully, artificial intelligence (AI) can help. Researchers, governments and businesses have started applying AI to virtually every point in the plastic life cycle, and early results are promising. Here are five ways AI is fighting plastic waste.<\/p>\n\n\n\n

<\/a>1. Autonomous Waste Collection<\/strong><\/h2>\n\n\n\n

Much plastic waste ends up in waterways, where it endangers aquatic life and impacts water quality. Researchers in Singapore have created an AI-driven robot to tackle this problem. Dubbed Clearbot Neo, the system travels along the water\u2019s surface and uses machine vision to recognize and catalog trash that it then picks up.<\/p>\n\n\n\n

Clearbot Neo can pick up as much as a metric ton of waste<\/a> per day. As it works, it also collects data on the types of trash it gathers and where it appears. This information can provide helpful insights about local waste patterns, guiding future management or legislative changes to address an area\u2019s specific issues.<\/p>\n\n\n\n

Machine vision capabilities also help Clearbot Neo avoid aquatic life. That way, the robot can clean water systems without disrupting their natural ecosystems.<\/p>\n\n\n\n

<\/a>2. Automated Sorting<\/strong><\/h2>\n\n\n\n

Plastic collection is only the first step in the waste management process. Next, organizations must sort it to determine how to dispose of it properly, but only 29% of global e-waste<\/a> ends up in the appropriate recycling channel. AI can refine this process.<\/p>\n\n\n\n

Machine vision-enabled robots can analyze waste to determine which channel is best for it. AI is far faster and more accurate than humans in this role, like many repetitive, data-heavy tasks. Some companies have found these robots can work twice as fast as humans<\/a> and double recycled products\u2019 resale value through more precise sorting.<\/p>\n\n\n\n

Automating the sorting process with AI helps ensure recycling centers reclaim as much plastic waste as possible. Algorithms don\u2019t get tired or distracted, so they\u2019ll make fewer mistakes that would otherwise send recyclable plastics into landfills.<\/p>\n\n\n\n

<\/a>3. Discovering New Disposal Methods<\/strong><\/h2>\n\n\n\n

AI can help find new, less environmentally harmful ways to dispose of plastic waste after sorting. Some machine learning algorithms can consider tens of millions of possibilities<\/a> in fractions of a second. This lets them find optimal plastic disposal alternatives that researchers may not think of on their own.<\/p>\n\n\n\n

Researchers at the University of Texas at Austin recently did just that. They used an advanced machine learning model to discover an enzyme that breaks down plastic in hours<\/a> instead of the years it normally takes. The algorithm looked at multiple mutations of an existing enzyme to predict which one would yield the best results.<\/p>\n\n\n\n

Without AI, discoveries like this may require days to months of lab work and experimentation. Machine learning can run several accurate simulations simultaneously, accelerating the process.<\/p>\n\n\n\n

<\/a>4. Optimizing Package Design<\/strong><\/h2>\n\n\n\n

AI can also help reduce plastic waste by minimizing it from the beginning. Much of this refuse comes from packaging, so some companies have started using AI to design less wasteful packages.<\/p>\n\n\n\n

Amazon used AI to analyze real-world customer complaints<\/a> to uncover trends in shipping damage and excessive packaging. The algorithms could then find optimal solutions to provide sufficient protection during shipping while minimizing material usage. The resulting packaging changes reduced shipping costs by 5% and lowered each item\u2019s carbon footprint.<\/p>\n\n\n\n

Similarly, L\u2019Or\u00e9al uses AI to estimate the performance of new packaging designs. The company can then find the ideal way to use more recycled materials and less raw plastic in its packaging. Over time, these algorithms may also improve on their own predictions, informing even better design changes.<\/p>\n\n\n\n

<\/a>5. Preventing Manufacturing Waste<\/strong><\/h2>\n\n\n\n

Predictive analytics can further help prevent plastic waste by tailoring production levels to incoming demand. Customer needs can shift rapidly, leaving companies with surplus products that may expire before use. AI can reduce that waste by predicting these changes.<\/p>\n\n\n\n

AI-based demand predictions can reduce forecasting errors by 30%-50% compared to conventional methods<\/a>. Manufacturers can then scale up or slow down production in response to incoming changes far more reliably. Meeting customer demand more accurately means they\u2019ll generate less waste.<\/p>\n\n\n\n

Similarly, AI forecasts can help reduce supply chain errors that would otherwise damage products. Less product loss in transit translates into less wasted packaging, helping further reduce plastic waste.<\/p>\n\n\n\n

<\/a>AI Is a Revolutionary Tool for Tackling the Plastic Problem<\/strong><\/h2>\n\n\n\n

Plastic waste is a substantial problem, requiring action from multiple parties across industries. AI\u2019s speed and accuracy can help make this considerable task far more manageable, helping humans do far more than they could by themselves. The fight against plastic waste could turn in the environment\u2019s favor as more organizations embrace AI.<\/p>\n\n\n\n

AI alone won\u2019t solve the plastic problem, but it is a remarkable tool. Applying this technology across agencies, organizations and workflows will help the world reduce plastic\u2019s ecological footprint.<\/p>\n","protected":false},"excerpt":{"rendered":"

Plastic waste poses a daunting problem to Earth\u2019s environmental health. The world has generated 8.3 billion metric tons of plastic since the 1950s but only recycled 9% of it. That leaves billions of tons degrading in landfills or polluting ecosystems as litter. Thankfully, artificial intelligence (AI) can help. Researchers, governments and businesses have started applying …<\/p>\n","protected":false},"author":192,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"nf_dc_page":"","footnotes":""},"categories":[220],"tags":[],"ppma_author":[604],"class_list":["post-94672","post","type-post","status-publish","format-standard","hentry","category-enviromental","author-zacharyamos"],"authors":[{"term_id":604,"user_id":192,"is_guest":0,"slug":"zacharyamos","display_name":"Zachary Amos","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/a371a3c58587125d84fd866e1864d6db?s=96&d=mm&r=g","last_name":"Amos","first_name":"Zachary","job_title":"","user_url":"https:\/\/rehack.com\/","description":"Zachary works as an AI and cybersecurity writer. He is also the Features Editor of ReHack."}],"_links":{"self":[{"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/posts\/94672","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/users\/192"}],"replies":[{"embeddable":true,"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/comments?post=94672"}],"version-history":[{"count":1,"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/posts\/94672\/revisions"}],"predecessor-version":[{"id":94674,"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/posts\/94672\/revisions\/94674"}],"wp:attachment":[{"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/media?parent=94672"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/categories?post=94672"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/tags?post=94672"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/aijourn.com\/wp-json\/wp\/v2\/ppma_author?post=94672"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}