School of Engineering

Subterms

    Latest story

    100 Shares14 Views

    How to make small modular reactors more cost-effective

    by

    When Youyeon Choi was in high school, she discovered she really liked “thinking in geometry.” The shapes, the dimensions … she was into all of it. Today, geometry plays a prominent role in her doctoral work under the guidance of Professor Koroush Shirvan, as she explores ways to increase the competitiveness of small modular reactors (SMRs).Central to the thesis is metallic nuclear fuel in a helical cruciform shape, which improves surface area and lowers heat flux as compared to the traditional cylindrical equivalent.A childhood in a prominent nuclear energy countryHer passion for geometry notwithstanding, Choi admits she was not “really into studying” in middle school. But that changed when she started excelling in technical subjects in her high school years. And because it was the natural sciences that first caught Choi’s eye, she assumed she would major in the subject when she went to university.This focus, too, would change. Growing up in Seoul, Choi was becoming increasingly aware of the critical role nuclear energy played in meeting her native country’s energy needs. Twenty-six reactors provide nearly a third of South Korea’s electricity, according to the World Nuclear Association. The country is also one of the world’s most prominent nuclear energy entities.In such an ecosystem, Choi understood the stakes at play, especially with electricity-guzzling technologies such as AI and electric vehicles on the rise. Her father also discussed energy-related topics with Choi when she was in high school. Being soaked in that atmosphere eventually led Choi to nuclear engineering.

    Youyeon Choi: Making small modular reactors more cost-effective

    Early work in South KoreaExcelling in high school math and science, Choi was a shoo-in for college at Seoul National University. Initially intent on studying nuclear fusion, Choi switched to fission because she saw that the path to fusion was more convoluted and was still in the early stages of exploration.Choi went on to complete her bachelor’s and master’s degrees in nuclear engineering from the university. As part of her master’s thesis, she worked on a multi-physics modeling project involving high-fidelity simulations of reactor physics and thermal hydraulics to analyze reactor cores.South Korea exports its nuclear know-how widely, so work in the field can be immensely rewarding. Indeed, after graduate school, Choi moved to Daejeon, which has the moniker “Science City.” As an intern at the Korea Atomic Energy Research Institute (KAERI), she conducted experimental studies on the passive safety systems of nuclear reactors. Choi then moved to the Korea Institute of Nuclear Nonproliferation and Control, where she worked as a researcher developing nuclear security programs for countries. Given South Korea’s dominance in the field, other countries would tap its knowledge resource to tap their own nuclear energy programs. The focus was on international training programs, an arm of which involved cybersecurity and physical protection.While the work was impactful, Choi found she missed the modeling work she did as part of her master’s thesis. Looking to return to technical research, she applied to the MIT Department of Nuclear Science and Engineering (NSE). “MIT has the best nuclear engineering program in the States, and maybe even the world,” Choi says, explaining her decision to enroll as a doctoral student.Innovative research at MITAt NSE, Choi is working to make SMRs more price competitive as compared to traditional nuclear energy power plants.Due to their smaller size, SMRs are able to serve areas where larger reactors might not work, but they’re more expensive. One way to address costs is to squeeze more electricity out of a unit of fuel — to increase the power density. Choi is doing so by replacing the traditional uranium dioxide ceramic fuel in a cylindrical shape with a metal one in a helical cruciform. Such a replacement potentially offers twin advantages: the metal fuel has high conductivity, which means the fuel will operate even more safely at lower temperatures. And the twisted shape gives more surface area and lower heat flux. The net result is more electricity for the same volume.The project receives funding from a collaboration between Lightbridge Corp., which is exploring how advanced fuel technologies can improve the performance of water-cooled SMRs, and the U.S. Department of Energy Nuclear Energy University Program.With SMR efficiencies in mind, Choi is indulging her love of multi-physics modeling, and focusing on reactor physics, thermal hydraulics, and fuel performance simulation. “The goal of this modeling and simulation is to see if we can really use this fuel in the SMR,” Choi says. “I’m really enjoying doing the simulations because the geometry is really hard to model. Because the shape is twisted, there’s no symmetry at all,” she says. Always up for a challenge, Choi learned the various aspects of physics and a variety of computational tools, including the Monte Carlo code for reactor physics.Being at MIT has a whole roster of advantages, Choi says, and she especially appreciates the respect researchers have for each other. She appreciates being able to discuss projects with Shirvan and his focus on practical applications of research. At the same time, Choi appreciates the “exotic” nature of her project. “Even assessing if this SMR fuel is at all feasible is really hard, but I think it’s all possible because it’s MIT and my PI [principal investigator] is really invested in innovation,” she says.It’s an exciting time to be in nuclear engineering, Choi says. She serves as one of the board members of the student section of the American Nuclear Society and is an NSE representative of the Graduate Student Council for the 2024-25 academic year.Choi is excited about the global momentum toward nuclear as more countries are exploring the energy source and trying to build more nuclear power plants on the path to decarbonization. “I really do believe nuclear energy is going to be a leading carbon-free energy. It’s very important for our collective futures,” Choi says. More

    More stories

    • 100 Shares56 Views

      in Energy

      Toward sustainable decarbonization of aviation in Latin America

      by

      According to the International Energy Agency, aviation accounts for about 2 percent of global carbon dioxide emissions, and aviation emissions are expected to double by mid-century as demand for domestic and international air travel rises. To sharply reduce emissions in alignment with the Paris Agreement’s long-term goal to keep global warming below 1.5 degrees Celsius, the International Air Transport Association (IATA) has set a goal to achieve net-zero carbon emissions by 2050. Which raises the question: Are there technologically feasible and economically viable strategies to reach that goal within the next 25 years?To begin to address that question, a team of researchers at the MIT Center for Sustainability Science and Strategy (CS3) and the MIT Laboratory for Aviation and the Environment has spent the past year analyzing aviation decarbonization options in Latin America, where air travel is expected to more than triple by 2050 and thereby double today’s aviation-related emissions in the region.Chief among those options is the development and deployment of sustainable aviation fuel. Currently produced from low- and zero-carbon sources (feedstock) including municipal waste and non-food crops, and requiring practically no alteration of aircraft systems or refueling infrastructure, sustainable aviation fuel (SAF) has the potential to perform just as well as petroleum-based jet fuel with as low as 20 percent of its carbon footprint.Focused on Brazil, Chile, Colombia, Ecuador, Mexico and Peru, the researchers assessed SAF feedstock availability, the costs of corresponding SAF pathways, and how SAF deployment would likely impact fuel use, prices, emissions, and aviation demand in each country. They also explored how efficiency improvements and market-based mechanisms could help the region to reach decarbonization targets. The team’s findings appear in a CS3 Special Report.SAF emissions, costs, and sourcesUnder an ambitious emissions mitigation scenario designed to cap global warming at 1.5 C and raise the rate of SAF use in Latin America to 65 percent by 2050, the researchers projected aviation emissions to be reduced by about 60 percent in 2050 compared to a scenario in which existing climate policies are not strengthened. To achieve net-zero emissions by 2050, other measures would be required, such as improvements in operational and air traffic efficiencies, airplane fleet renewal, alternative forms of propulsion, and carbon offsets and removals.As of 2024, jet fuel prices in Latin America are around $0.70 per liter. Based on the current availability of feedstocks, the researchers projected SAF costs within the six countries studied to range from $1.11 to $2.86 per liter. They cautioned that increased fuel prices could affect operating costs of the aviation sector and overall aviation demand unless strategies to manage price increases are implemented.Under the 1.5 C scenario, the total cumulative capital investments required to build new SAF producing plants between 2025 and 2050 were estimated at $204 billion for the six countries (ranging from $5 billion in Ecuador to $84 billion in Brazil). The researchers identified sugarcane- and corn-based ethanol-to-jet fuel, palm oil- and soybean-based hydro-processed esters and fatty acids as the most promising feedstock sources in the near term for SAF production in Latin America.“Our findings show that SAF offers a significant decarbonization pathway, which must be combined with an economy-wide emissions mitigation policy that uses market-based mechanisms to offset the remaining emissions,” says Sergey Paltsev, lead author of the report, MIT CS3 deputy director, and senior research scientist at the MIT Energy Initiative.RecommendationsThe researchers concluded the report with recommendations for national policymakers and aviation industry leaders in Latin America.They stressed that government policy and regulatory mechanisms will be needed to create sufficient conditions to attract SAF investments in the region and make SAF commercially viable as the aviation industry decarbonizes operations. Without appropriate policy frameworks, SAF requirements will affect the cost of air travel. For fuel producers, stable, long-term-oriented policies and regulations will be needed to create robust supply chains, build demand for establishing economies of scale, and develop innovative pathways for producing SAF.Finally, the research team recommended a region-wide collaboration in designing SAF policies. A unified decarbonization strategy among all countries in the region will help ensure competitiveness, economies of scale, and achievement of long-term carbon emissions-reduction goals.“Regional feedstock availability and costs make Latin America a potential major player in SAF production,” says Angelo Gurgel, a principal research scientist at MIT CS3 and co-author of the study. “SAF requirements, combined with government support mechanisms, will ensure sustainable decarbonization while enhancing the region’s connectivity and the ability of disadvantaged communities to access air transport.”Financial support for this study was provided by LATAM Airlines and Airbus. More

    • 75 Shares42 Views

      in Energy

      The multifaceted challenge of powering AI

      by

      Artificial intelligence has become vital in business and financial dealings, medical care, technology development, research, and much more. Without realizing it, consumers rely on AI when they stream a video, do online banking, or perform an online search. Behind these capabilities are more than 10,000 data centers globally, each one a huge warehouse containing thousands of computer servers and other infrastructure for storing, managing, and processing data. There are now over 5,000 data centers in the United States, and new ones are being built every day — in the U.S. and worldwide. Often dozens are clustered together right near where people live, attracted by policies that provide tax breaks and other incentives, and by what looks like abundant electricity.And data centers do consume huge amounts of electricity. U.S. data centers consumed more than 4 percent of the country’s total electricity in 2023, and by 2030 that fraction could rise to 9 percent, according to the Electric Power Research Institute. A single large data center can consume as much electricity as 50,000 homes.The sudden need for so many data centers presents a massive challenge to the technology and energy industries, government policymakers, and everyday consumers. Research scientists and faculty members at the MIT Energy Initiative (MITEI) are exploring multiple facets of this problem — from sourcing power to grid improvement to analytical tools that increase efficiency, and more. Data centers have quickly become the energy issue of our day.Unexpected demand brings unexpected solutionsSeveral companies that use data centers to provide cloud computing and data management services are announcing some surprising steps to deliver all that electricity. Proposals include building their own small nuclear plants near their data centers and even restarting one of the undamaged nuclear reactors at Three Mile Island, which has been shuttered since 2019. (A different reactor at that plant partially melted down in 1979, causing the nation’s worst nuclear power accident.) Already the need to power AI is causing delays in the planned shutdown of some coal-fired power plants and raising prices for residential consumers. Meeting the needs of data centers is not only stressing power grids, but also setting back the transition to clean energy needed to stop climate change.There are many aspects to the data center problem from a power perspective. Here are some that MIT researchers are focusing on, and why they’re important.An unprecedented surge in the demand for electricity“In the past, computing was not a significant user of electricity,” says William H. Green, director of MITEI and the Hoyt C. Hottel Professor in the MIT Department of Chemical Engineering. “Electricity was used for running industrial processes and powering household devices such as air conditioners and lights, and more recently for powering heat pumps and charging electric cars. But now all of a sudden, electricity used for computing in general, and by data centers in particular, is becoming a gigantic new demand that no one anticipated.”Why the lack of foresight? Usually, demand for electric power increases by roughly half-a-percent per year, and utilities bring in new power generators and make other investments as needed to meet the expected new demand. But the data centers now coming online are creating unprecedented leaps in demand that operators didn’t see coming. In addition, the new demand is constant. It’s critical that a data center provides its services all day, every day. There can be no interruptions in processing large datasets, accessing stored data, and running the cooling equipment needed to keep all the packed-together computers churning away without overheating.Moreover, even if enough electricity is generated, getting it to where it’s needed may be a problem, explains Deepjyoti Deka, a MITEI research scientist. “A grid is a network-wide operation, and the grid operator may have sufficient generation at another location or even elsewhere in the country, but the wires may not have sufficient capacity to carry the electricity to where it’s wanted.” So transmission capacity must be expanded — and, says Deka, that’s a slow process.Then there’s the “interconnection queue.” Sometimes, adding either a new user (a “load”) or a new generator to an existing grid can cause instabilities or other problems for everyone else already on the grid. In that situation, bringing a new data center online may be delayed. Enough delays can result in new loads or generators having to stand in line and wait for their turn. Right now, much of the interconnection queue is already filled up with new solar and wind projects. The delay is now about five years. Meeting the demand from newly installed data centers while ensuring that the quality of service elsewhere is not hampered is a problem that needs to be addressed.Finding clean electricity sourcesTo further complicate the challenge, many companies — including so-called “hyperscalers” such as Google, Microsoft, and Amazon — have made public commitments to having net-zero carbon emissions within the next 10 years. Many have been making strides toward achieving their clean-energy goals by buying “power purchase agreements.” They sign a contract to buy electricity from, say, a solar or wind facility, sometimes providing funding for the facility to be built. But that approach to accessing clean energy has its limits when faced with the extreme electricity demand of a data center.Meanwhile, soaring power consumption is delaying coal plant closures in many states. There are simply not enough sources of renewable energy to serve both the hyperscalers and the existing users, including individual consumers. As a result, conventional plants fired by fossil fuels such as coal are needed more than ever.As the hyperscalers look for sources of clean energy for their data centers, one option could be to build their own wind and solar installations. But such facilities would generate electricity only intermittently. Given the need for uninterrupted power, the data center would have to maintain energy storage units, which are expensive. They could instead rely on natural gas or diesel generators for backup power — but those devices would need to be coupled with equipment to capture the carbon emissions, plus a nearby site for permanently disposing of the captured carbon.Because of such complications, several of the hyperscalers are turning to nuclear power. As Green notes, “Nuclear energy is well matched to the demand of data centers, because nuclear plants can generate lots of power reliably, without interruption.”In a much-publicized move in September, Microsoft signed a deal to buy power for 20 years after Constellation Energy reopens one of the undamaged reactors at its now-shuttered nuclear plant at Three Mile Island, the site of the much-publicized nuclear accident in 1979. If approved by regulators, Constellation will bring that reactor online by 2028, with Microsoft buying all of the power it produces. Amazon also reached a deal to purchase power produced by another nuclear plant threatened with closure due to financial troubles. And in early December, Meta released a request for proposals to identify nuclear energy developers to help the company meet their AI needs and their sustainability goals.Other nuclear news focuses on small modular nuclear reactors (SMRs), factory-built, modular power plants that could be installed near data centers, potentially without the cost overruns and delays often experienced in building large plants. Google recently ordered a fleet of SMRs to generate the power needed by its data centers. The first one will be completed by 2030 and the remainder by 2035.Some hyperscalers are betting on new technologies. For example, Google is pursuing next-generation geothermal projects, and Microsoft has signed a contract to purchase electricity from a startup’s fusion power plant beginning in 2028 — even though the fusion technology hasn’t yet been demonstrated.Reducing electricity demandOther approaches to providing sufficient clean electricity focus on making the data center and the operations it houses more energy efficient so as to perform the same computing tasks using less power. Using faster computer chips and optimizing algorithms that use less energy are already helping to reduce the load, and also the heat generated.Another idea being tried involves shifting computing tasks to times and places where carbon-free energy is available on the grid. Deka explains: “If a task doesn’t have to be completed immediately, but rather by a certain deadline, can it be delayed or moved to a data center elsewhere in the U.S. or overseas where electricity is more abundant, cheaper, and/or cleaner? This approach is known as ‘carbon-aware computing.’” We’re not yet sure whether every task can be moved or delayed easily, says Deka. “If you think of a generative AI-based task, can it easily be separated into small tasks that can be taken to different parts of the country, solved using clean energy, and then be brought back together? What is the cost of doing this kind of division of tasks?”That approach is, of course, limited by the problem of the interconnection queue. It’s difficult to access clean energy in another region or state. But efforts are under way to ease the regulatory framework to make sure that critical interconnections can be developed more quickly and easily.What about the neighbors?A major concern running through all the options for powering data centers is the impact on residential energy consumers. When a data center comes into a neighborhood, there are not only aesthetic concerns but also more practical worries. Will the local electricity service become less reliable? Where will the new transmission lines be located? And who will pay for the new generators, upgrades to existing equipment, and so on? When new manufacturing facilities or industrial plants go into a neighborhood, the downsides are generally offset by the availability of new jobs. Not so with a data center, which may require just a couple dozen employees.There are standard rules about how maintenance and upgrade costs are shared and allocated. But the situation is totally changed by the presence of a new data center. As a result, utilities now need to rethink their traditional rate structures so as not to place an undue burden on residents to pay for the infrastructure changes needed to host data centers.MIT’s contributionsAt MIT, researchers are thinking about and exploring a range of options for tackling the problem of providing clean power to data centers. For example, they are investigating architectural designs that will use natural ventilation to facilitate cooling, equipment layouts that will permit better airflow and power distribution, and highly energy-efficient air conditioning systems based on novel materials. They are creating new analytical tools for evaluating the impact of data center deployments on the U.S. power system and for finding the most efficient ways to provide the facilities with clean energy. Other work looks at how to match the output of small nuclear reactors to the needs of a data center, and how to speed up the construction of such reactors.MIT teams also focus on determining the best sources of backup power and long-duration storage, and on developing decision support systems for locating proposed new data centers, taking into account the availability of electric power and water and also regulatory considerations, and even the potential for using what can be significant waste heat, for example, for heating nearby buildings. Technology development projects include designing faster, more efficient computer chips and more energy-efficient computing algorithms.In addition to providing leadership and funding for many research projects, MITEI is acting as a convenor, bringing together companies and stakeholders to address this issue. At MITEI’s 2024 Annual Research Conference, a panel of representatives from two hyperscalers and two companies that design and construct data centers together discussed their challenges, possible solutions, and where MIT research could be most beneficial.As data centers continue to be built, and computing continues to create an unprecedented increase in demand for electricity, Green says, scientists and engineers are in a race to provide the ideas, innovations, and technologies that can meet this need, and at the same time continue to advance the transition to a decarbonized energy system. More

    • 100 Shares54 Views

      in Energy

      For clean ammonia, MIT engineers propose going underground

      by

      Ammonia is the most widely produced chemical in the world today, used primarily as a source for nitrogen fertilizer. Its production is also a major source of greenhouse gas emissions — the highest in the whole chemical industry.Now, a team of researchers at MIT has developed an innovative way of making ammonia without the usual fossil-fuel-powered chemical plants that require high heat and pressure. Instead, they have found a way to use the Earth itself as a geochemical reactor, producing ammonia underground. The processes uses Earth’s naturally occurring heat and pressure, provided free of charge and free of emissions, as well as the reactivity of minerals already present in the ground.The trick the team devised is to inject water underground, into an area of iron-rich subsurface rock. The water carries with it a source of nitrogen and particles of a metal catalyst, allowing the water to react with the iron to generate clean hydrogen, which in turn reacts with the nitrogen to make ammonia. A second well is then used to pump that ammonia up to the surface.The process, which has been demonstrated in the lab but not yet in a natural setting, is described today in the journal Joule. The paper’s co-authors are MIT professors of materials science and engineering Iwnetim Abate and Ju Li, graduate student Yifan Gao, and five others at MIT.“When I first produced ammonia from rock in the lab, I was so excited,” Gao recalls. “I realized this represented an entirely new and never-reported approach to ammonia synthesis.’”The standard method for making ammonia is called the Haber-Bosch process, which was developed in Germany in the early 20th century to replace natural sources of nitrogen fertilizer such as mined deposits of bat guano, which were becoming depleted. But the Haber-Bosch process is very energy intensive: It requires temperatures of 400 degrees Celsius and pressures of 200 atmospheres, and this means it needs huge installations in order to be efficient. Some areas of the world, such as sub-Saharan Africa and Southeast Asia, have few or no such plants in operation.  As a result, the shortage or extremely high cost of fertilizer in these regions has limited their agricultural production.The Haber-Bosch process “is good. It works,” Abate says. “Without it, we wouldn’t have been able to feed 2 out of the total 8 billion people in the world right now, he says, referring to the portion of the world’s population whose food is grown with ammonia-based fertilizers. But because of the emissions and energy demands, a better process is needed, he says.Burning fuel to generate heat is responsible for about 20 percent of the greenhouse gases emitted from plants using the Haber-Bosch process. Making hydrogen accounts for the remaining 80 percent.  But ammonia, the molecule NH3, is made up only of nitrogen and hydrogen. There’s no carbon in the formula, so where do the carbon emissions come from? The standard way of producing the needed hydrogen is by processing methane gas with steam, breaking down the gas into pure hydrogen, which gets used, and carbon dioxide gas that gets released into the air.Other processes exist for making low- or no-emissions hydrogen, such as by using solar or wind-generated electricity to split water into oxygen and hydrogen, but that process can be expensive. That’s why Abate and his team worked on developing a system to produce what they call geological hydrogen. Some places in the world, including some in Africa, have been found to naturally generate hydrogen underground through chemical reactions between water and iron-rich rocks. These pockets of naturally occurring hydrogen can be mined, just like natural methane reservoirs, but the extent and locations of such deposits are still relatively unexplored.Abate realized this process could be created or enhanced by pumping water, laced with copper and nickel catalyst particles to speed up the process, into the ground in places where such iron-rich rocks were already present. “We can use the Earth as a factory to produce clean flows of hydrogen,” he says.He recalls thinking about the problem of the emissions from hydrogen production for ammonia: “The ‘aha!’ moment for me was thinking, how about we link this process of geological hydrogen production with the process of making Haber-Bosch ammonia?”That would solve the biggest problem of the underground hydrogen production process, which is how to capture and store the gas once it’s produced. Hydrogen is a very tiny molecule — the smallest of them all — and hard to contain. But by implementing the entire Haber-Bosch process underground, the only material that would need to be sent to the surface would be the ammonia itself, which is easy to capture, store, and transport.The only extra ingredient needed to complete the process was the addition of a source of nitrogen, such as nitrate or nitrogen gas, into the water-catalyst mixture being injected into the ground. Then, as the hydrogen gets released from water molecules after interacting with the iron-rich rocks, it can immediately bond with the nitrogen atoms also carried in the water, with the deep underground environment providing the high temperatures and pressures required by the Haber-Bosch process. A second well near the injection well then pumps the ammonia out and into tanks on the surface.“We call this geological ammonia,” Abate says, “because we are using subsurface temperature, pressure, chemistry, and geologically existing rocks to produce ammonia directly.”Whereas transporting hydrogen requires expensive equipment to cool and liquefy it, and virtually no pipelines exist for its transport (except near oil refinery sites), transporting ammonia is easier and cheaper. It’s about one-sixth the cost of transporting hydrogen, and there are already more than 5,000 miles of ammonia pipelines and 10,000 terminals in place in the U.S. alone. What’s more, Abate explains, ammonia, unlike hydrogen, already has a substantial commercial market in place, with production volume projected to grow by two to three times by 2050, as it is used not only for fertilizer but also as feedstock for a wide variety of chemical processes.For example, ammonia can be burned directly in gas turbines, engines, and industrial furnaces, providing a carbon-free alternative to fossil fuels. It is being explored for maritime shipping and aviation as an alternative fuel, and as a possible space propellant.Another upside to geological ammonia is that untreated wastewater, including agricultural runoff, which tends to be rich in nitrogen already, could serve as the water source and be treated in the process. “We can tackle the problem of treating wastewater, while also making something of value out of this waste,” Abate says.Gao adds that this process “involves no direct carbon emissions, presenting a potential pathway to reduce global CO2 emissions by up to 1 percent.” To arrive at this point, he says, the team “overcame numerous challenges and learned from many failed attempts. For example, we tested a wide range of conditions and catalysts before identifying the most effective one.”The project was seed-funded under a flagship project of MIT’s Climate Grand Challenges program, the Center for the Electrification and Decarbonization of Industry. Professor Yet-Ming Chiang, co-director of the center, says “I don’t think there’s been any previous example of deliberately using the Earth as a chemical reactor. That’s one of the key novel points of this approach.”  Chiang emphasizes that even though it is a geological process, it happens very fast, not on geological timescales. “The reaction is fundamentally over in a matter of hours,” he says. “The reaction is so fast that this answers one of the key questions: Do you have to wait for geological times? And the answer is absolutely no.”Professor Elsa Olivetti, a mission director of the newly established Climate Project at MIT, says, “The creative thinking by this team is invaluable to MIT’s ability to have impact at scale. Coupling these exciting results with, for example, advanced understanding of the geology surrounding hydrogen accumulations represent the whole-of-Institute efforts the Climate Project aims to support.”“This is a significant breakthrough for the future of sustainable development,” says Geoffrey Ellis, a geologist at the U.S. Geological Survey, who was not associated with this work. He adds, “While there is clearly more work that needs to be done to validate this at the pilot stage and to get this to the commercial scale, the concept that has been demonstrated is truly transformative.  The approach of engineering a system to optimize the natural process of nitrate reduction by Fe2+ is ingenious and will likely lead to further innovations along these lines.”The initial work on the process has been done in the laboratory, so the next step will be to prove the process using a real underground site. “We think that kind of experiment can be done within the next one to two years,” Abate says. This could open doors to using a similar approach for other chemical production processes, he adds.The team has applied for a patent and aims to work towards bringing the process to market.“Moving forward,” Gao says, “our focus will be on optimizing the process conditions and scaling up tests, with the goal of enabling practical applications for geological ammonia in the near future.”The research team also included Ming Lei, Bachu Sravan Kumar, Hugh Smith, Seok Hee Han, and Lokesh Sangabattula, all at MIT. Additional funding was provided by the National Science Foundation and was carried out, in part, through the use of MIT.nano facilities. More

    • 63 Shares74 Views

      in Energy

      Explained: Generative AI’s environmental impact

      by

      In a two-part series, MIT News explores the environmental implications of generative AI. In this article, we look at why this technology is so resource-intensive. A second piece will investigate what experts are doing to reduce genAI’s carbon footprint and other impacts.The excitement surrounding potential benefits of generative AI, from improving worker productivity to advancing scientific research, is hard to ignore. While the explosive growth of this new technology has enabled rapid deployment of powerful models in many industries, the environmental consequences of this generative AI “gold rush” remain difficult to pin down, let alone mitigate.The computational power required to train generative AI models that often have billions of parameters, such as OpenAI’s GPT-4, can demand a staggering amount of electricity, which leads to increased carbon dioxide emissions and pressures on the electric grid.Furthermore, deploying these models in real-world applications, enabling millions to use generative AI in their daily lives, and then fine-tuning the models to improve their performance draws large amounts of energy long after a model has been developed.Beyond electricity demands, a great deal of water is needed to cool the hardware used for training, deploying, and fine-tuning generative AI models, which can strain municipal water supplies and disrupt local ecosystems. The increasing number of generative AI applications has also spurred demand for high-performance computing hardware, adding indirect environmental impacts from its manufacture and transport.“When we think about the environmental impact of generative AI, it is not just the electricity you consume when you plug the computer in. There are much broader consequences that go out to a system level and persist based on actions that we take,” says Elsa A. Olivetti, professor in the Department of Materials Science and Engineering and the lead of the Decarbonization Mission of MIT’s new Climate Project.Olivetti is senior author of a 2024 paper, “The Climate and Sustainability Implications of Generative AI,” co-authored by MIT colleagues in response to an Institute-wide call for papers that explore the transformative potential of generative AI, in both positive and negative directions for society.Demanding data centersThe electricity demands of data centers are one major factor contributing to the environmental impacts of generative AI, since data centers are used to train and run the deep learning models behind popular tools like ChatGPT and DALL-E.A data center is a temperature-controlled building that houses computing infrastructure, such as servers, data storage drives, and network equipment. For instance, Amazon has more than 100 data centers worldwide, each of which has about 50,000 servers that the company uses to support cloud computing services.While data centers have been around since the 1940s (the first was built at the University of Pennsylvania in 1945 to support the first general-purpose digital computer, the ENIAC), the rise of generative AI has dramatically increased the pace of data center construction.“What is different about generative AI is the power density it requires. Fundamentally, it is just computing, but a generative AI training cluster might consume seven or eight times more energy than a typical computing workload,” says Noman Bashir, lead author of the impact paper, who is a Computing and Climate Impact Fellow at MIT Climate and Sustainability Consortium (MCSC) and a postdoc in the Computer Science and Artificial Intelligence Laboratory (CSAIL).Scientists have estimated that the power requirements of data centers in North America increased from 2,688 megawatts at the end of 2022 to 5,341 megawatts at the end of 2023, partly driven by the demands of generative AI. Globally, the electricity consumption of data centers rose to 460 terawatts in 2022. This would have made data centers the 11th largest electricity consumer in the world, between the nations of Saudi Arabia (371 terawatts) and France (463 terawatts), according to the Organization for Economic Co-operation and Development.By 2026, the electricity consumption of data centers is expected to approach 1,050 terawatts (which would bump data centers up to fifth place on the global list, between Japan and Russia).While not all data center computation involves generative AI, the technology has been a major driver of increasing energy demands.“The demand for new data centers cannot be met in a sustainable way. The pace at which companies are building new data centers means the bulk of the electricity to power them must come from fossil fuel-based power plants,” says Bashir.The power needed to train and deploy a model like OpenAI’s GPT-3 is difficult to ascertain. In a 2021 research paper, scientists from Google and the University of California at Berkeley estimated the training process alone consumed 1,287 megawatt hours of electricity (enough to power about 120 average U.S. homes for a year), generating about 552 tons of carbon dioxide.While all machine-learning models must be trained, one issue unique to generative AI is the rapid fluctuations in energy use that occur over different phases of the training process, Bashir explains.Power grid operators must have a way to absorb those fluctuations to protect the grid, and they usually employ diesel-based generators for that task.Increasing impacts from inferenceOnce a generative AI model is trained, the energy demands don’t disappear.Each time a model is used, perhaps by an individual asking ChatGPT to summarize an email, the computing hardware that performs those operations consumes energy. Researchers have estimated that a ChatGPT query consumes about five times more electricity than a simple web search.“But an everyday user doesn’t think too much about that,” says Bashir. “The ease-of-use of generative AI interfaces and the lack of information about the environmental impacts of my actions means that, as a user, I don’t have much incentive to cut back on my use of generative AI.”With traditional AI, the energy usage is split fairly evenly between data processing, model training, and inference, which is the process of using a trained model to make predictions on new data. However, Bashir expects the electricity demands of generative AI inference to eventually dominate since these models are becoming ubiquitous in so many applications, and the electricity needed for inference will increase as future versions of the models become larger and more complex.Plus, generative AI models have an especially short shelf-life, driven by rising demand for new AI applications. Companies release new models every few weeks, so the energy used to train prior versions goes to waste, Bashir adds. New models often consume more energy for training, since they usually have more parameters than their predecessors.While electricity demands of data centers may be getting the most attention in research literature, the amount of water consumed by these facilities has environmental impacts, as well.Chilled water is used to cool a data center by absorbing heat from computing equipment. It has been estimated that, for each kilowatt hour of energy a data center consumes, it would need two liters of water for cooling, says Bashir.“Just because this is called ‘cloud computing’ doesn’t mean the hardware lives in the cloud. Data centers are present in our physical world, and because of their water usage they have direct and indirect implications for biodiversity,” he says.The computing hardware inside data centers brings its own, less direct environmental impacts.While it is difficult to estimate how much power is needed to manufacture a GPU, a type of powerful processor that can handle intensive generative AI workloads, it would be more than what is needed to produce a simpler CPU because the fabrication process is more complex. A GPU’s carbon footprint is compounded by the emissions related to material and product transport.There are also environmental implications of obtaining the raw materials used to fabricate GPUs, which can involve dirty mining procedures and the use of toxic chemicals for processing.Market research firm TechInsights estimates that the three major producers (NVIDIA, AMD, and Intel) shipped 3.85 million GPUs to data centers in 2023, up from about 2.67 million in 2022. That number is expected to have increased by an even greater percentage in 2024.The industry is on an unsustainable path, but there are ways to encourage responsible development of generative AI that supports environmental objectives, Bashir says.He, Olivetti, and their MIT colleagues argue that this will require a comprehensive consideration of all the environmental and societal costs of generative AI, as well as a detailed assessment of the value in its perceived benefits.“We need a more contextual way of systematically and comprehensively understanding the implications of new developments in this space. Due to the speed at which there have been improvements, we haven’t had a chance to catch up with our abilities to measure and understand the tradeoffs,” Olivetti says. More

    • 88 Shares79 Views

      in Environment

      Designing tiny filters to solve big problems

      by

      For many industrial processes, the typical way to separate gases, liquids, or ions is with heat, using slight differences in boiling points to purify mixtures. These thermal processes account for roughly 10 percent of the energy use in the United States.MIT chemical engineer Zachary Smith wants to reduce costs and carbon footprints by replacing these energy-intensive processes with highly efficient filters that can separate gases, liquids, and ions at room temperature.In his lab at MIT, Smith is designing membranes with tiny pores that can filter tiny molecules based on their size. These membranes could be useful for purifying biogas, capturing carbon dioxide from power plant emissions, or generating hydrogen fuel.“We’re taking materials that have unique capabilities for separating molecules and ions with precision, and applying them to applications where the current processes are not efficient, and where there’s an enormous carbon footprint,” says Smith, an associate professor of chemical engineering.Smith and several former students have founded a company called Osmoses that is working toward developing these materials for large-scale use in gas purification. Removing the need for high temperatures in these widespread industrial processes could have a significant impact on energy consumption, potentially reducing it by as much as 90 percent.“I would love to see a world where we could eliminate thermal separations, and where heat is no longer a problem in creating the things that we need and producing the energy that we need,” Smith says.Hooked on researchAs a high school student, Smith was drawn to engineering but didn’t have many engineering role models. Both of his parents were physicians, and they always encouraged him to work hard in school.“I grew up without knowing many engineers, and certainly no chemical engineers. But I knew that I really liked seeing how the world worked. I was always fascinated by chemistry and seeing how mathematics helped to explain this area of science,” recalls Smith, who grew up near Harrisburg, Pennsylvania. “Chemical engineering seemed to have all those things built into it, but I really had no idea what it was.”At Penn State University, Smith worked with a professor named Henry “Hank” Foley on a research project designing carbon-based materials to create a “molecular sieve” for gas separation. Through a time-consuming and iterative layering process, he created a sieve that could purify oxygen and nitrogen from air.“I kept adding more and more coatings of a special material that I could subsequently carbonize, and eventually I started to get selectivity. In the end, I had made a membrane that could sieve molecules that only differed by 0.18 angstrom in size,” he says. “I got hooked on research at that point, and that’s what led me to do more things in the area of membranes.”After graduating from college in 2008, Smith pursued graduate studies in chemical engineering at the University of Texas at Austin. There, he continued developing membranes for gas separation, this time using a different class of materials — polymers. By controlling polymer structure, he was able to create films with pores that filter out specific molecules, such as carbon dioxide or other gases.“Polymers are a type of material that you can actually form into big devices that can integrate into world-class chemical plants. So, it was exciting to see that there was a scalable class of materials that could have a real impact on addressing questions related to CO2 and other energy-efficient separations,” Smith says.After finishing his PhD, he decided he wanted to learn more chemistry, which led him to a postdoctoral fellowship at the University of California at Berkeley.“I wanted to learn how to make my own molecules and materials. I wanted to run my own reactions and do it in a more systematic way,” he says.At Berkeley, he learned how make compounds called metal-organic frameworks (MOFs) — cage-like molecules that have potential applications in gas separation and many other fields. He also realized that while he enjoyed chemistry, he was definitely a chemical engineer at heart.“I learned a ton when I was there, but I also learned a lot about myself,” he says. “As much as I love chemistry, work with chemists, and advise chemists in my own group, I’m definitely a chemical engineer, really focused on the process and application.”Solving global problemsWhile interviewing for faculty jobs, Smith found himself drawn to MIT because of the mindset of the people he met.“I began to realize not only how talented the faculty and the students were, but the way they thought was very different than other places I had been,” he says. “It wasn’t just about doing something that would move their field a little bit forward. They were actually creating new fields. There was something inspirational about the type of people that ended up at MIT who wanted to solve global problems.”In his lab at MIT, Smith is now tackling some of those global problems, including water purification, critical element recovery, renewable energy, battery development, and carbon sequestration.In a close collaboration with Yan Xia, a professor at Stanford University, Smith recently developed gas separation membranes that incorporate a novel type of polymer known as “ladder polymers,” which are currently being scaled for deployment at his startup. Historically, using polymers for gas separation has been limited by a tradeoff between permeability and selectivity — that is, membranes that permit a faster flow of gases through the membrane tend to be less selective, allowing impurities to get through.Using ladder polymers, which consist of double strands connected by rung-like bonds, the researchers were able to create gas separation membranes that are both highly permeable and very selective. The boost in permeability — a 100- to 1,000-fold improvement over earlier materials — could enable membranes to replace some of the high-energy techniques now used to separate gases, Smith says.“This allows you to envision large-scale industrial problems solved with miniaturized devices,” he says. “If you can really shrink down the system, then the solutions we’re developing in the lab could easily be applied to big industries like the chemicals industry.”These developments and others have been part of a number of advancements made by collaborators, students, postdocs, and researchers who are part of Smith’s team.“I have a great research team of talented and hard-working students and postdocs, and I get to teach on topics that have been instrumental in my own professional career,” Smith says. “MIT has been a playground to explore and learn new things. I am excited for what my team will discover next, and grateful for an opportunity to help solve many important global problems.” More

    • 113 Shares146 Views

      in Environment

      Minimizing the carbon footprint of bridges and other structures

      by

      Awed as a young child by the majesty of the Golden Gate Bridge in San Francisco, civil engineer and MIT Morningside Academy for Design (MAD) Fellow Zane Schemmer has retained his fascination with bridges: what they look like, why they work, and how they’re designed and built.He weighed the choice between architecture and engineering when heading off to college, but, motivated by the why and how of structural engineering, selected the latter. Now he incorporates design as an iterative process in the writing of algorithms that perfectly balance the forces involved in discrete portions of a structure to create an overall design that optimizes function, minimizes carbon footprint, and still produces a manufacturable result.While this may sound like an obvious goal in structural design, it’s not. It’s new. It’s a more holistic way of looking at the design process that can optimize even down to the materials, angles, and number of elements in the nodes or joints that connect the larger components of a building, bridge, tower, etc.According to Schemmer, there hasn’t been much progress on optimizing structural design to minimize embodied carbon, and the work that exists often results in designs that are “too complex to be built in real life,” he says. The embodied carbon of a structure is the total carbon dioxide emissions of its life cycle: from the extraction or manufacture of its materials to their transport and use and through the demolition of the structure and disposal of the materials. Schemmer, who works with Josephine V. Carstensen, the Gilbert W. Winslow Career Development Associate Professor of Civil and Environmental Engineering at MIT, is focusing on the portion of that cycle that runs through construction.In September, at the IASS 2024 symposium “Redefining the Art of Structural Design in Zurich,” Schemmer and Carstensen presented their work on Discrete Topology Optimization algorithms that are able to minimize the embodied carbon in a bridge or other structure by up to 20 percent. This comes through materials selection that considers not only a material’s appearance and its ability to get the job done, but also the ease of procurement, its proximity to the building site, and the carbon embodied in its manufacture and transport.“The real novelty of our algorithm is its ability to consider multiple materials in a highly constrained solution space to produce manufacturable designs with a user-specified force flow,” Schemmer says. “Real-life problems are complex and often have many constraints associated with them. In traditional formulations, it can be difficult to have a long list of complicated constraints. Our goal is to incorporate these constraints to make it easier to take our designs out of the computer and create them in real life.”Take, for instance, a steel tower, which could be a “super lightweight, efficient design solution,” Schemmer explains. Because steel is so strong, you don’t need as much of it compared to concrete or timber to build a big building. But steel is also very carbon-intensive to produce and transport. Shipping it across the country or especially from a different continent can sharply increase its embodied carbon price tag. Schemmer’s topology optimization will replace some of the steel with timber elements or decrease the amount of steel in other elements to create a hybrid structure that will function effectively and minimize the carbon footprint. “This is why using the same steel in two different parts of the world can lead to two different optimized designs,” he explains.Schemmer, who grew up in the mountains of Utah, earned a BS and MS in civil and environmental engineering from University of California at Berkeley, where his graduate work focused on seismic design. He describes that education as providing a “very traditional, super-strong engineering background that tackled some of the toughest engineering problems,” along with knowledge of structural engineering’s traditions and current methods.But at MIT, he says, a lot of the work he sees “looks at removing the constraints of current societal conventions of doing things, and asks how could we do things if it was in a more ideal form; what are we looking at then? Which I think is really cool,” he says. “But I think sometimes too, there’s a jump between the most-perfect version of something and where we are now, that there needs to be a bridge between those two. And I feel like my education helps me see that bridge.”The bridge he’s referring to is the topology optimization algorithms that make good designs better in terms of decreased global warming potential.“That’s where the optimization algorithm comes in,” Schemmer says. “In contrast to a standard structure designed in the past, the algorithm can take the same design space and come up with a much more efficient material usage that still meets all the structural requirements, be up to code, and have everything we want from a safety standpoint.”That’s also where the MAD Design Fellowship comes in. The program provides yearlong fellowships with full financial support to graduate students from all across the Institute who network with each other, with the MAD faculty, and with outside speakers who use design in new ways in a surprising variety of fields. This helps the fellows gain a better understanding of how to use iterative design in their own work.“Usually people think of their own work like, ‘Oh, I had this background. I’ve been looking at this one way for a very long time.’ And when you look at it from an outside perspective, I think it opens your mind to be like, ‘Oh my God. I never would have thought about doing this that way. Maybe I should try that.’ And then we can move to new ideas, new inspiration for better work,” Schemmer says.He chose civil and structural engineering over architecture some seven years ago, but says that “100 years ago, I don’t think architecture and structural engineering were two separate professions. I think there was an understanding of how things looked and how things worked, and it was merged together. Maybe from an efficiency standpoint, it’s better to have things done separately. But I think there’s something to be said for having knowledge about how the whole system works, potentially more intermingling between the free-form architectural design and the mathematical design of a civil engineer. Merging it back together, I think, has a lot of benefits.”Which brings us back to the Golden Gate Bridge, Schemmer’s longtime favorite. You can still hear that excited 3-year-old in his voice when he talks about it.“It’s so iconic,” he says. “It’s connecting these two spits of land that just rise straight up out of the ocean. There’s this fog that comes in and out a lot of days. It’s a really magical place, from the size of the cable strands and everything. It’s just, ‘Wow.’ People built this over 100 years ago, before the existence of a lot of the computational tools that we have now. So, all the math, everything in the design, was all done by hand and from the mind. Nothing was computerized, which I think is crazy to think about.”As Schemmer continues work on his doctoral degree at MIT, the MAD fellowship will expose him to many more awe-inspiring ideas in other fields, leading him to incorporate some of these in some way with his engineering knowledge to design better ways of building bridges and other structures. More

    • 138 Shares148 Views

      in Security

      Coffee fix: MIT students decode the science behind the perfect cup

      by

      Elaine Jutamulia ’24 took a sip of coffee with a few drops of anise extract. It was her second try.“What do you think?” asked Omar Orozco, standing at a lab table in MIT’s Breakerspace, surrounded by filters, brewing pots, and other coffee paraphernalia.“I think when I first tried it, it was still pretty bitter,” Jutamulia said thoughtfully. “But I think now that it’s steeped for a little bit — it took out some of the bitterness.”Jutamulia and current MIT senior Orozco were part of class 3.000 (Coffee Matters: Using the Breakerspace to Make the Perfect Cup), a new MIT course that debuted in spring 2024. The class combines lectures on chemistry and the science of coffee with hands-on experimentation and group projects. Their project explored how additives such as anise, salt, and chili oil influence coffee extraction — the process of dissolving flavor compounds from ground coffee into water — to improve taste and correct common brewing errors.Alongside tasting, they used an infrared spectrometer to identify the chemical compounds in their coffee samples that contribute to flavor. Does anise make bitter coffee smoother? Could chili oil balance the taste?“Generally speaking, if we could make a recommendation, that’s what we’re trying to find,” Orozco said.A three-unit “discovery class” designed to help first-year students explore majors, 3.000 was widely popular, enrolling more than 50 students. Its success was driven by the beverage at its core and the class’s hands-on approach, which pushes students to ask and answer questions they might not have otherwise.For aeronautics and astronautics majors Gabi McDonald and McKenzie Dinesen, coffee was the draw, but the class encouraged them to experiment and think in new ways. “It’s easy to drop people like us in, who love coffee, and, ‘Oh my gosh, there’s this class where we can go make coffee half the time and try all different kinds of things?’” McDonald says.Percolating knowledgeThe class pairs weekly lectures on topics such as coffee chemistry, the anatomy and composition of a coffee bean, the effects of roasting, and the brewing process with tasting sessions — students sample coffee brewed from different beans, roasts, and grinds. In the MIT Breakerspace, a new space on campus conceived and managed by the Department of Materials Science and Engineering (DMSE), students use equipment such as a digital optical microscope to examine ground coffee particles and a scanning electron microscope, which shoots beams of electrons at samples to reveal cross-sections of beans in stunning detail.Once students learn to operate instruments for guided tasks, they form groups and design their own projects.“The driver for those projects is some question they have about coffee raised by one of the lectures or the tasting sessions, or just something they’ve always wanted to know,” says DMSE Professor Jeffrey Grossman, who designed and teaches the class. “Then they’ll use one or more of these pieces of equipment to shed some light on it.”Grossman traces the origins of the class to his initial vision for the Breakerspace, a laboratory for materials analysis and lounge for MIT undergraduates. Opened in November 2023, the space gives students hands-on experience with materials science and engineering, an interdisciplinary field combining chemistry, physics, and engineering to probe the composition and structure of materials.“The world is made of stuff, and these are the tools to understand that stuff and bring it to life,” says Grossman. So he envisioned a class that would give students an “exploratory, inspiring nudge.”“Then the question wasn’t the pedagogy, it was, ‘What’s the hook?’ In materials science, there are a lot of directions you could go, but if you have one that inspires people because they know it and maybe like it already, then that’s exciting.”Cup of ambitionThat hook, of course, was coffee, the second-most-consumed beverage after water. It captured students’ imagination and motivated them to push boundaries.Orozco brought a fair amount of coffee knowledge to the class. In 2023, he taught in Mexico through the MISTI Global Teaching Labs program, where he toured several coffee farms and acquired a deeper knowledge of the beverage. He learned, for example, that black coffee, contrary to general American opinion, isn’t naturally bitter; bitterness arises from certain compounds that develop during the roasting process.“If you properly brew it with the right beans, it actually tastes good,” says Orozco, a humanities and engineering major. A year later, in 3.000, he expanded his understanding of making a good brew, particularly through the group project with Jutamulia and other students to fix bad coffee.The group prepared a control sample of “perfectly brewed” coffee — based on taste, coffee-to-water ratio, and other standards covered in class — alongside coffee that was under-extracted and over-extracted. Under-extracted coffee, made with water that isn’t hot enough or brewed for too short a time, tastes sharp or sour. Over-extracted coffee, brewed with too much coffee or for too long, tastes bitter.Those coffee samples got additives and were analyzed using Fourier Transform Infrared (FTIR) spectroscopy, measuring how coffee absorbed infrared light to identify flavor-related compounds. Jutamulia examined FTIR readings taken from a sample with lime juice to see how the citric acid influenced its chemical profile.“Can we find any correlation between what we saw and the existing known measurements of citric acid?” asks Jutamulia, who studied computation and cognition at MIT, graduating last May.Another group dove into coffee storage, questioning why conventional wisdom advises against freezing.“We just wondered why that’s the case,” says electrical engineering and computer science major Noah Wiley, a coffee enthusiast with his own espresso machine.The team compared methods like freezing brewed coffee, frozen coffee grounds, and whole beans ground after freezing, evaluating their impact on flavor and chemical composition.“Then we’re going to see which ones taste good,” says Wiley. The team used a class coffee review sheet to record attributes like acidity, bitterness, sweetness, and overall flavor, pairing the results with FTIR analysis to determine how storage affected taste.Wiley acknowledged that “good” is subjective. “Sometimes there’s a group consensus. I think people like fuller coffee, not watery,” he says.Other student projects compared caffeine levels in different coffee types, analyzed the effect of microwaving coffee on its chemical composition and flavor, and investigated the differences between authentic and counterfeit coffee beans.“We gave the students some papers to look at in case they were interested,” says Justin Lavallee, Breakerspace manager and co-teacher of the class. “But mostly we told them to focus on something they wanted to learn more about.”Drip, drip, dripBeyond answering specific questions about coffee, both students and teachers gained deeper insights into the beverage.“Coffee is a complicated material. There are thousands of molecules in the beans, which change as you roast and extract them,” says Grossman. “The number of ways you can engineer this collection of molecules — it’s profound, ranging from where and how the coffee’s grown to how the cherries are then treated to get the beans to how the beans are roasted and ground to the brewing method you use.”Dinesen learned firsthand, discovering, for example, that darker roasts have less caffeine than lighter roasts, puncturing a common misconception. “You can vary coffee so much — just with the roast of the bean, the size of the ground,” she says. “It’s so easily manipulatable, if that’s a word.”In addition to learning about the science and chemistry behind coffee, Dinesen and McDonald gained new brewing techniques, like using a pour-over cone. The pair even incorporated coffee making and testing into their study routine, brewing coffee while tackling problem sets for another class.“I would put my pour-over cone in my backpack with a Ziploc bag full of grounds, and we would go to the Student Center and pull out the cone, a filter, and the coffee grounds,” McDonald says. “And then we would make pour-overs while doing a P-set. We tested different amounts of water, too. It was fun.”Tony Chen, a materials science and engineering major, reflected on the 3.000’s title — “Using the Breakerspace to Make the Perfect Cup” — and whether making a perfect cup is possible. “I don’t think there’s one perfect cup because each person has their own preferences. I don’t think I’ve gotten to mine yet,” he says.Enthusiasm for coffee’s complexity and the discovery process was exactly what Grossman hoped to inspire in his students. “The best part for me was also just seeing them developing their own sense of curiosity,” he says.He recalled a moment early in the class when students, after being given a demo of the optical microscope, saw the surface texture of a magnified coffee bean, the mottled shades of color, and the honeycomb-like pattern of tiny irregular cells.“They’re like, ‘Wait a second. What if we add hot water to the grounds while it’s under the microscope? Would we see the extraction?’ So, they got hot water and some ground coffee beans, and lo and behold, it looked different. They could see the extraction right there,” Grossman says. “It’s like they have an idea that’s inspired by the learning, and they go and try it. I saw that happen many, many times throughout the semester.” More

    • 138 Shares82 Views

      in Energy

      How hard is it to prevent recurring blackouts in Puerto Rico?

      by

      Researchers at MIT’s Laboratory for Information and Decision Systems (LIDS) have shown that using decision-making software and dynamic monitoring of weather and energy use can significantly improve resiliency in the face of weather-related outages, and can also help to efficiently integrate renewable energy sources into the grid.The researchers point out that the system they suggest might have prevented or at least lessened the kind of widespread power outage that Puerto Rico experienced last week by providing analysis to guide rerouting of power through different lines and thus limit the spread of the outage.The computer platform, which the researchers describe as DyMonDS, for Dynamic Monitoring and Decision Systems, can be used to enhance the existing operating and planning practices used in the electric industry. The platform supports interactive information exchange and decision-making between the grid operators and grid-edge users — all the distributed power sources, storage systems and software that contribute to the grid. It also supports optimization of available resources and controllable grid equipment as system conditions vary. It further lends itself to implementing cooperative decision-making by different utility- and non-utility-owned electric power grid users, including portfolios of mixed resources, users, and storage. Operating and planning the interactions of the end-to-end high-voltage transmission grid with local distribution grids and microgrids represents another major potential use of this platform.This general approach was illustrated using a set of publicly-available data on both meteorology and details of electricity production and distribution in Puerto Rico. An extended AC Optimal Power Flow software developed by SmartGridz Inc. is used for system-level optimization of controllable equipment. This provides real-time guidance for deciding how much power, and through which transmission lines, should be channeled by adjusting plant dispatch and voltage-related set points, and in extreme cases, where to reduce or cut power in order to maintain physically-implementable service for as many customers as possible. The team found that the use of such a system can help to ensure that the greatest number of critical services maintain power even during a hurricane, and at the same time can lead to a substantial decrease in the need for construction of new power plants thanks to more efficient use of existing resources.The findings are described in a paper in the journal Foundations and Trends in Electric Energy Systems, by MIT LIDS researchers Marija Ilic and Laurentiu Anton, along with recent alumna Ramapathi Jaddivada.“Using this software,” Ilic says, they show that “even during bad weather, if you predict equipment failures, and by using that information exchange, you can localize the effect of equipment failures and still serve a lot of customers, 50 percent of customers, when otherwise things would black out.”Anton says that “the way many grids today are operated is sub-optimal.” As a result, “we showed how much better they could do even under normal conditions, without any failures, by utilizing this software.” The savings resulting from this optimization, under everyday conditions, could be in the tens of percents, they say.The way utility systems plan currently, Ilic says, “usually the standard is that they have to build enough capacity and operate in real time so that if one large piece of equipment fails, like a large generator or transmission line, you still serve customers in an uninterrupted way. That’s what’s called N-minus-1.” Under this policy, if one major component of the system fails, they should be able to maintain service for at least 30 minutes. That system allows utilities to plan for how much reserve generating capacity they need to have on hand. That’s expensive, Ilic points out, because it means maintaining this reserve capacity all the time, even under normal operating conditions when it’s not needed.In addition, “right now there are no criteria for what I call N-minus-K,” she says. If bad weather causes five pieces of equipment to fail at once, “there is no software to help utilities decide what to schedule” in terms of keeping the most customers, and the most important services such as hospitals and emergency services, provided with power. They showed that even with 50 percent of the infrastructure out of commission, it would still be possible to keep power flowing to a large proportion of customers.Their work on analyzing the power situation in Puerto Rico started after the island had been devastated by hurricanes Irma and Maria. Most of the electric generation capacity is in the south, yet the largest loads are in San Juan, in the north, and Mayaguez in the west. When transmission lines get knocked down, a lot of rerouting of power needs to happen quickly.With the new systems, “the software finds the optimal adjustments for set points,” for example, changing voltages can allow for power to be redirected through less-congested lines, or can be increased to lessen power losses, Anton says.The software also helps in the long-term planning for the grid. As many fossil-fuel power plants are scheduled to be decommissioned soon in Puerto Rico, as they are in many other places, planning for how to replace that power without having to resort to greenhouse gas-emitting sources is a key to achieving carbon-reduction goals. And by analyzing usage patterns, the software can guide the placement of new renewable power sources where they can most efficiently provide power where and when it’s needed.As plants are retired or as components are affected by weather, “We wanted to ensure the dispatchability of power when the load changes,” Anton says, “but also when crucial components are lost, to ensure the robustness at each step of the retirement schedule.”One thing they found was that “if you look at how much generating capacity exists, it’s more than the peak load, even after you retire a few fossil plants,” Ilic says. “But it’s hard to deliver.” Strategic planning of new distribution lines could make a big difference.Jaddivada, director of innovation at SmartGridz, says that “we evaluated different possible architectures in Puerto Rico, and we showed the ability of this software to ensure uninterrupted electricity service. This is the most important challenge utilities have today. They have to go through a computationally tedious process to make sure the grid functions for any possible outage in the system. And that can be done in a much more efficient way through the software that the company  developed.”The project was a collaborative effort between the MIT LIDS researchers and others at MIT Lincoln Laboratory, the Pacific Northwest National Laboratory, with overall help of SmartGridz software.  More