Majik Water is a social enterprise that specializes in air to water technologies in arid and semi arid regions. They are creating a new source of affordable, clean drinking water for water scarce communities. If you have air, you can have drinking water.
Technology developed at Argonne can help narrow the field of candidates for molten salts, a new study demonstrates.
Scientists are searching for new materials to advance the next generation of nuclear power plants. In a recent study, researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory showed how artificial intelligence could help pinpoint the right types of molten salts, a key component for advanced nuclear reactors.
The ability to absorb and store heat makes molten salt important to clean energy and national climate goals. Molten salts can serve as both coolant and fuel in nuclear power reactors that generate electricity without emitting greenhouse gases. They can also store large amounts of energy, which is increasingly needed on an electric grid with fluctuating sources such as wind and solar power.
The carbon-negative concrete blocks absorb more CO2 during production than they emit.
A Limburg (Netherlands) company called Masterbloc has engineered an eco-friendly building material from steel slag left over from the steel industry, according to an article by The Brussels Times.
The company CEO Bjorn Gubbels claims the block stores CO2 and can help boost the circular economy.
Continue reading “Carbon-eating blocks ingest eight tonnes of CO2 a day, says company” »
In April, Tesla Inc TSLA CEO Elon Musk said he was done selling Tesla shares to help finance his ongoing overhaul at Twitter.
Since then, he jettisoned more than $20 billion worth of Tesla stock and has continued the selling spree this week.
As the stock continues to skid, Jim Cramer sees an electric buying opportunity.
Chemist Martial Gervais Oden-Bella developed a method for recycling used cooking oil by turning it into soap! Not only is he helping the environment, but he’s made a profitable business in the process.
In Cameroon, people were tossing the used oil down the drain, which can pollute water treatment systems and the environment. When the oil ends up in the environment, it’s harmful and toxic to wildlife. Now the businesses (restaurants and hotels in the southwestern city of Douala) save the used oil to give to Oden-Bella, who turns it into soap and other cleaning products.
Continue reading “Chemist From Cameroon Makes Cleaning Products” »
AI is used by the robotic solution to swiftly adapt to all types of blades used in the sector.
Danish energy solutions provider Vestas has unveiled BladeRobots as a stand-alone business with an automated robotic technology solution for the maintenance of wind turbines.
The robot performs automated blade-leading edge maintenance up to “four times faster” than traditional manual methods, according to a press release by the company published on Wednesday.
Building a wind power operation that can thrive in icy conditions requires a keen understanding of the underlying physics.
Winter is supposed to be the best season for wind power — the winds are more potent, and since air density increases as the temperature drops, more force is pushing on the blades. But winter also comes with a problem: freezing weather.
Continue reading “How do wind turbines spin during winter? The science behind frozen blades” »
Both animals and people use high-dimensional inputs (like eyesight) to accomplish various shifting survival-related objectives. A crucial aspect of this is learning via mistakes. A brute-force approach to trial and error by performing every action for every potential goal is intractable even in the smallest contexts. Memory-based methods for compositional thinking are motivated by the difficulty of this search. These processes include, for instance, the ability to: recall pertinent portions of prior experience; (ii) reassemble them into new counterfactual plans, and (iii) carry out such plans as part of a focused search strategy. Compared to equally sampling every action, such techniques for recycling prior successful behavior can considerably speed up trial-and-error. This is because the intrinsic compositional structure of real-world objectives and the similarity of the physical laws that control real-world settings allow the same behavior (i.e., sequence of actions) to remain valid for many purposes and situations. What guiding principles enable memory processes to retain and reassemble experience fragments? This debate is strongly connected to the idea of dynamic programming (DP), which using the principle of optimality significantly lowers the computing cost of trial-and-error. This idea may be expressed informally as considering new, complicated issues as a recomposition of previously solved, smaller subproblems.
This viewpoint has recently been used to create hierarchical reinforcement learning (RL) algorithms for goal-achieving tasks. These techniques develop edges between states in a planning graph using a distance regression model, compute the shortest pathways across it using DP-based graph search, and then use a learning-based local policy to follow the shortest paths. Their essay advances this field of study. The following is a summary of their contributions: They provide a strategy for long-term planning that acts directly on high-dimensional sensory data that an agent may see on its own (e.g., images from an onboard camera). Their solution blends traditional sampling-based planning algorithms with learning-based perceptual representations to recover and reassemble previously recorded state transitions in a replay buffer.
The two-step method makes this possible. To determine how many timesteps it takes for an optimum policy to move from one state to the next, they first learn a latent space where the distance between two states is the measure. They know contrastive representations using goal-conditioned Q-values acquired through offline hindsight relabeling. To establish neighborhood criteria across states, the second threshold this developed latent distance metric. They go on to design sampling-based planning algorithms that scan the replay buffer for trajectory segments—previously recorded successions of transitions—whose ends are adjacent states.
Researchers from the University of Toronto’s Faculty of Applied Science & Engineering and Fujitsu have developed a new way of searching through ‘chemical space’ for materials with desirable properties.
The technique has resulted in a promising new catalyst material that could help lower the cost of producing clean hydrogen.
The discovery represents an important step toward more sustainable ways of storing energy, including from renewable but intermittent sources, such as solar and wind power.
The team has successfully tested a sustainable membrane-based seawater electrolyzer.
A research team in China has developed a device to split salty seawater to produce hydrogen directly. The device, a membrane-based seawater electrolyzer, helps address the side-reaction and corrosion problems of traditional methods.
Why traditional methods are not sustainable.
