Today more than half the world’s population lives in cities, and this is expected to rise to nearly 70 percent by 2050. Rapid growth in urbanization has also led to an increase in municipal solid waste — all the trash, garbage and rubbish thrown away every day. This typically includes common household waste, newspapers and plastic packaging, office and retail waste but also used tires and furniture.
The issue of municipal waste is exacerbated by the relentless rise in plastic waste. Estimates project a daunting 1.1 billion tons of plastic burden by 2050, meaning sustainable waste-management solutions are a priority for cities around the world.
Researchers are investigating ways to turn this plastic waste into hydrogen. Hydrogen is billed as the energy source of the future, but its production is hindered by scalability, environmental impact and economic viability. Despite hydrogen’s reputation as green fuel, current methods of producing it rely on fossil fuels and steam-methane reforming, which is energy-intensive and creates carbon dioxide as a byproduct.
Gasification, on the other hand, could be the hydrogen production technique we need to produce hydrogen at the scale we want it. Expose plastic waste to temperatures high enough and the hydrogen present in the plastic vaporizes, leaving behind an additional valuable byproduct: graphene.
Plastic is, after all, a hydrocarbon: polymers of hydrogen and carbon atoms. Heating the plastic quickly enough reorganizes the chemical bonds, with the carbon atoms combining to graphene and the hydrogen atoms becoming hydrogen gas. Flash joule heating is a technique for rapidly heating materials to extremely high temperatures with a jolt of electricity. Electricity converts into heat, achieving temperatures of thousands of Kelvin for a small energy cost and a large valuable product output.
Research led by Pau Loke Show, professor of biochemical engineering at Khalifa University, has applied machine learning techniques to the process to improve efficiency. He says integrating hydrogen production from unconventional feedstocks, bolstered by machine learning and advanced storage, can contribute to a sustainable and pollution-free future:
“Machine learning emerges as a critical enabler in optimizing gasification processes, enhancing efficiency and reducing emissions. Moving forward, these integrated approaches are key to advancing carbon-neutral energy solutions and fulfilling global environmental goals.”
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