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BPL Nigeria | https://bplnigeria.com A metal recycling and commodity trading company Tue, 27 Apr 2021 12:57:32 +0000 en-US hourly 1 https://wordpress.org/?v=5.2.20 https://bplnigeria.com/wp-content/uploads/2019/08/fav.png BPL Nigeria | https://bplnigeria.com 32 32 Recycling of Lead-Acid Batteries: Perspectives https://bplnigeria.com/2021/04/27/recycling-of-lead-acid-batteries-perspectives/ Tue, 27 Apr 2021 12:57:32 +0000 http://bplnigeria.com/?p=1871 Lead-acid batteries are used on a mass-scale in all parts of the world for energy storage. Lead-acid batteries contain sulphuric acid and large amounts of lead. The acid is extremely corrosive and is also a good carrier for soluble lead and lead particulate. Lead is a highly toxic metal that produces a range of adverse health impacts, particularly among young children.

 

Exposure to excessive levels of lead can cause damage to the brain and kidneys, impair hearing; and lead to numerous other associated problems. On average, each automobile manufactured contains approximately 12 kilograms of lead. Around 96% lead is used in the common lead-acid battery, while the remaining 4% in other applications including wheel balance weights, protective coatings, and vibration dampers.

 

Recycling Perspectives

Recycling of Lead-Acid Batteries is a profitable business, albeit dangerous, in developing countries. Many developing countries buy used lead-acid batteries (also known as ULABs) from industrialized countries (and the Middle East) in bulk in order to extract lead. ULAB recycling occurs in almost every city in the developing world where ULAB recycling and smelting operations are often located in densely populated urban areas with hardly any pollution control and safety measures for workers.

 

Usually, ULAB recycling operations release lead-contaminated waste into the environment and natural ecosystems.  In fact, Blacksmith Institute estimates that over 12 million people are affected by lead contamination from the processing of Used Lead Acid Batteries in the developing world, with South America, South Asia, and Africa being the most affected regions.

 

Associated Problems

The problems associated with the recycling of ULABs are well-documented and recognized by the industry and the Basel Convention Secretariat. As much of the informal ULAB recycling is small-scale and difficult to regulate or control, progress is possible only through cleanup, outreach, policy, and education.

 

For example, Blacksmith’s Lead Poisoning and Car Batteries Project is currently active in eight countries, including Senegal, the Dominican Republic, India, and the Philippines. The Project aims to end widespread lead poisoning from the improper recycling of ULABs, and consists of several different strategies and programs, with the most important priority being the health of children in the surrounding communities.

 

Lead poisoning, from improper recycling of used batteries, impacts tens of millions of people worldwide.

 

There is no effective means of tracking shipments of used lead-acid batteries from foreign exporters to recycling plants in the developing world which makes it difficult to trace ULABs going to unauthorized or inadequate facilities.

 

The Way Forward

An effective method to reduce the hazards posed by transboundary movements of ULABs is to encourage companies that generate used lead batteries to voluntarily stop exporting lead batteries to developing countries. These types of voluntary restrictions on transboundary shipments can help pressure companies involved in recycling lead batteries in developing to improve their environmental performance. It may also help encourage policymakers to close the gaps in both regulations and enforcement capacity.

 

Another interesting way is to encourage the regeneration of lead-acid batteries which can prolong their life significantly. The advantage of battery regeneration over regular recycling is the reduced carbon footprint incurred by mitigating the collecting, packing, shipping, and smelting of millions of tonnes of batteries and their cases. Most importantly, it takes about 25kWh of energy to remake a 15Kg, 12V 70Ah battery and just 2.1KWh to regenerate it electronically.

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Machine Learning Helps In Interpreting Battery Life https://bplnigeria.com/2021/03/12/machine-learning-helps-in-interpreting-battery-life/ Fri, 12 Mar 2021 10:10:53 +0000 http://bplnigeria.com/?p=1855 A study carried out jointly by Stanford University, SLAC National Accelerator Laboratory, the Massachusetts Institute of Technology, and the Toyota Research Institute (TRI) demonstrated the use of machine learning algorithms to understand the lifecycle of lithium-ion batteries. Until now, machine learning in battery technology was limited to identifying patterns in data to speed up scientific analysis. The latest discovery will help researchers in designing and developing longer-lasting batteries.

ML And Battery Life Cycle

The research team has been working to develop a long-lasting electric vehicle battery that can be charged in 10 minutes.

“Battery technology is important for any type of electric powertrain. By understanding the fundamental reactions that occur within the battery we can extend its life, enable faster charging and ultimately design better battery materials. We look forward to building on this work through future experiments to achieve lower-cost, better-performing batteries,” said Patrick Herring, a senior scientist of Toyota Research Institute.

Earlier studies used more conventional machine learning forms to accelerate battery testing and find out the best charging method. Though the studies made major progress in determining battery lifetime, they did not reveal much about the science behind why a few batteries last longer than the others.

The current research teaches machines how to learn a new type of failure mechanism to design better and safer fast-charging batteries. In general, fast charging stresses and damages the battery. Better practices would help battery technology and fight climate change, the team said. Further, this approach can be used for developing grid-scale battery systems for producing wind and solar electricity.

The team was able to optimize the fast charging protocol for lithium-ion batteries within a month using machine learning. Without ML, this would usually take two years. “At the end of the day, we see our job as accelerating the pace of battery R&D. Whether it’s discovering new chemistry or finding a way to make a safer battery, it’s all very time-consuming. We’re trying to save time,” said Will Chueh, an associate professor at Stanford University, who also led the study. 

The Experiment

For this experiment, the team took a closer look at the Lithium ions movement between the cathode and anode — made of nano-sized grains lumped together as particles — during charging and discharging. In particular, the behavior of cathode particles, comprising nickel, manganese, and cobalt (NMC), were observed in detail. NMC is the most widely used material in electric vehicle batteries. The particles absorb lithium ions when the battery is discharging and release them when the battery is charging.

The team got an overall look at the particles when the battery was being fast charged using X-rays from SLAC’s Stanford Synchrotron Radiation Lightsource. The same particles were later examined with scanning X-ray transmission microscopy, which focuses on individual particles. The data obtained from these experiments; information from mathematical models on fast charging; and physics and chemistry equations were used in the scientific machine learning algorithm. The team said this is the first time scientific machine learning has been used in battery technology.

 

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