{"id":840956,"date":"2026-08-03T02:22:10","date_gmt":"2026-08-03T02:22:10","guid":{"rendered":"https:\/\/www.newsbeep.com\/au\/840956\/"},"modified":"2026-08-03T02:22:10","modified_gmt":"2026-08-03T02:22:10","slug":"australian-start-up-automates-waste-management-to-help-prevent-fires-in-recycling-facilities","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/au\/840956\/","title":{"rendered":"Australian start-up automates waste management to help prevent fires in recycling facilities"},"content":{"rendered":"

As Australia\u2019s domestic waste stream becomes more complex, materials recovery facilities face increasingly volatile contaminants. Every week, news headlines detail another facility fire caused by compromised lithium-ion batteries. <\/p>\n

These incidents threaten worker safety and escalate operational downtime\u00a0and\u00a0insurance liabilities across the board.In response, Australian startup Oscorp Energy is developing advanced autonomous technologies to mitigate hazardous inputs before disaster strikes.<\/p>\n

Founded in 2024, the artificial intelligence (AI) company develops intelligent material sorting equipment capable of processing complex waste\u00a0streams.<\/p>\n

\u201cWe are building intelligent machines for the waste and recycling industry with a vision to enable fully autonomous waste facilities of tomorrow,\u201d says Ani Goswami, Founder of Oscorp Energy.<\/p>\n

\u201cSpecifically, we are building machines that can precisely identify hazardous and flammable materials, such as batteries, in any form of waste and safely remove them so that facilities don\u2019t catch fire.<\/p>\n

\u201cWe call it the world\u2019s first end-of-life battery electronics AI model, essentially covering anything and everything that contains a battery.\u201d<\/p>\n

Operating from a specialised prototype robotics laboratory in Queensland, Oscorp Energy is led by Ani, as well as co-founders Dr Chandrakant Bothe, who holds a doctorate in artificial intelligence, and Dhiren Rami, a robotics expert holding a master\u2019s degree in the field.<\/p>\n

At the core of Oscorp Energy\u2019s technology is a high-speed robotic sorting machine that can be retrofitted into existing materials recovery infrastructure and sorting lines without requiring costly facility overhauls.<\/p>\n

It has AI-based sensors, that can precisely identify batteries and battery electronics on a sorting belt and safely remove them.<\/p>\n

Ani says the company built its \u201cAI brain\u201d from scratch for this job, rather than customising off-the-shelf software.<\/p>\n

To accurately spot items moving fast on unpredictable conveyor belts the software needs to see thousands of examples. The company has spent a lot of time and computing power building a massive library of real-world facility data to train its AI.<\/p>\n

Achieving this standard requires constant, disciplined data collection. The system must account for structural deterioration, crushing, and varied form\u00a0factors typical of consumer electronics waste.<\/p>\n

\"wasteAni Goswami, Dr Chandrakant Bothe and Dhiren Rami with Michael Kron (Antler) and Mark Griffiths (Livium Ltd). Image: Oscorp Energy<\/p>\n

\u201cOur AI models should be able to identify all of them, so that\u2019s what we\u2019ve been working towards,\u201d Ani says. \u201cWe have trained our AI on close to a million different batteries and battery electronics so far, and our goal is two million, at least, by the end of this year.\u201d<\/p>\n

While the initial goal is battery electronics to limit active fire risks,<\/a> the core software has expanded into broader municipal solid waste and commercial streams. The system now categorises household recyclables, organic waste fractions, heavy metals, medical waste, and discarded textiles.<\/p>\n

To achieve this, the development team blends supervised training methods with autonomous refinement models, allowing the machinery to process real-world volumes without stalling.<\/p>\n

\u201cThere are three ways you train your AI: first is supervised learning, the second is unsupervised learning, and third is reinforcement learning,\u201d Ani says.<\/p>\n

\u201cOn a fundamental level, supervised learning is essentially you have pictures of\u00a0different wastes, you manually tag every item, and then train your AI, so that you\u2019re telling the AI, \u2018Hey, this is a plastic, this is a phone, this is a battery, etc.<\/p>\n

\u201cOnce the AI has been trained to a sufficient level, then comes the component of unsupervised learning, where the AI can try and see other objects and try to identify patterns from what it has learned. We have started implementing unsupervised learning, where the AI trains itself.\u201d<\/p>\n

Because no two recycling facilities process the exact same types of waste, the\u00a0software is built to adapt quickly to new locations. It uses a central AI system that studies the local waste mix when first installed. The equipment then tracks and memorises\u00a0these local differences to create a custom sorting plan tailored to that specific facility.<\/p>\n

\u201cOur goal is to provide as much accurate information as possible, and our benchmark is 99.5 per cent accuracy,\u201d Ani says.<\/p>\n

\u201cTo achieve that, our base models would be trained heavily, and then each facility would have its own waste fingerprint. Within two weeks that AI should be able to understand \u2018Okay, this is the type of waste I\u2019m dealing with on a day-to-day basis\u2019.\u201d<\/p>\n

Alongside the sorting machines, the company is developing a data platform called Vision OS. This software connects multiple sensors placed throughout a recycling facility to give managers a bird\u2019s-eye view of their entire operation. It allows them to quickly spot where sorting errors happen, find belt jams, and track real-time trash trends.<\/p>\n

\u201cIt tracks and logs each and every material that is on your belt and gives you the visibility on what is flowing on your belt, where your contamination rates are higher, on what specific lines, what you could do to improve,\u201d Ani says.<\/p>\n

The technology has attracted significant commercial attention and financial backing, with the company securing capital from European venture capital group Atlas, alongside global investor Antler and Antipodean Capital.<\/p>\n

On the industrial front, a memorandum of understanding (MoU) signed with lithium battery recycler Livium Limited has taken the technology from laboratory\u00a0testing into commercial field\u00a0trials.<\/p>\n

As part of the MoU, Oscorp is now building a robotics sorting component to complement the AI.<\/p>\n

\u201cSo, the brain is sufficiently developed, now we have started building the body,\u201d Ani says.<\/p>\n

\u201cIt\u2019s pretty exciting and I think batteries is a tough challenge when it comes to robotics, because batteries come in so many different forms, shapes, and sizes. They are chemically active as well, so the risk of them blowing up is high.<\/p>\n

\u201cFor us to have a robot pick and sort different form factors and shapes of batteries is an engineering challenge.\u201d<\/p>\n

Ani says by replacing manual sorting with robotics, operators can shield employees from hazardous materials while optimising plant uptime.<\/p>\n

\u201cWaste sorting is a manual, dangerous, dusty job. We\u2019re wanting to create systems that are safer for everybody.\u201d<\/p>\n

He says that ultimately, installing automated systems addresses fundamental blind spots that have impacted resource recovery yields for decades.<\/p>\n

\u201cGiven the way this industry is operating, I would say our technology is a game changer for the industry, because once they see the value in it, they will realize what they have been missing all these years.\u201d \u00a0<\/p>\n

For more information, visit: www.oscorpenergy.com.au<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"As Australia\u2019s domestic waste stream becomes more complex, materials recovery facilities face increasingly volatile contaminants. Every week, news…\n","protected":false},"author":2,"featured_media":840957,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[64,63,128],"class_list":["post-840956","post","type-post","status-publish","format-standard","has-post-thumbnail","category-science","tag-au","tag-australia","tag-science"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/posts\/840956","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/comments?post=840956"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/posts\/840956\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/media\/840957"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/media?parent=840956"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/categories?post=840956"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/tags?post=840956"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}