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    Using deep learning to image the Earth’s planetary boundary layer

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    Although the troposphere is often thought of as the closest layer of the atmosphere to the Earth’s surface, the planetary boundary layer (PBL) — the lowest layer of the troposphere — is actually the part that most significantly influences weather near the surface. In the 2018 planetary science decadal survey, the PBL was raised as an important scientific issue that has the potential to enhance storm forecasting and improve climate projections.  

    “The PBL is where the surface interacts with the atmosphere, including exchanges of moisture and heat that help lead to severe weather and a changing climate,” says Adam Milstein, a technical staff member in Lincoln Laboratory’s Applied Space Systems Group. “The PBL is also where humans live, and the turbulent movement of aerosols throughout the PBL is important for air quality that influences human health.” 

    Although vital for studying weather and climate, important features of the PBL, such as its height, are difficult to resolve with current technology. In the past four years, Lincoln Laboratory staff have been studying the PBL, focusing on two different tasks: using machine learning to make 3D-scanned profiles of the atmosphere, and resolving the vertical structure of the atmosphere more clearly in order to better predict droughts.  

    This PBL-focused research effort builds on more than a decade of related work on fast, operational neural network algorithms developed by Lincoln Laboratory for NASA missions. These missions include the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission as well as Aqua, a satellite that collects data about Earth’s water cycle and observes variables such as ocean temperature, precipitation, and water vapor in the atmosphere. These algorithms retrieve temperature and humidity from the satellite instrument data and have been shown to significantly improve the accuracy and usable global coverage of the observations over previous approaches. For TROPICS, the algorithms help retrieve data that are used to characterize a storm’s rapidly evolving structures in near-real time, and for Aqua, it has helped increase forecasting models, drought monitoring, and fire prediction. 

    These operational algorithms for TROPICS and Aqua are based on classic “shallow” neural networks to maximize speed and simplicity, creating a one-dimensional vertical profile for each spectral measurement collected by the instrument over each location. While this approach has improved observations of the atmosphere down to the surface overall, including the PBL, laboratory staff determined that newer “deep” learning techniques that treat the atmosphere over a region of interest as a three-dimensional image are needed to improve PBL details further.

    “We hypothesized that deep learning and artificial intelligence (AI) techniques could improve on current approaches by incorporating a better statistical representation of 3D temperature and humidity imagery of the atmosphere into the solutions,” Milstein says. “But it took a while to figure out how to create the best dataset — a mix of real and simulated data; we needed to prepare to train these techniques.”

    The team collaborated with Joseph Santanello of the NASA Goddard Space Flight Center and William Blackwell, also of the Applied Space Systems Group, in a recent NASA-funded effort showing that these retrieval algorithms can improve PBL detail, including more accurate determination of the PBL height than the previous state of the art. 

    While improved knowledge of the PBL is broadly useful for increasing understanding of climate and weather, one key application is prediction of droughts. According to a Global Drought Snapshot report released last year, droughts are a pressing planetary issue that the global community needs to address. Lack of humidity near the surface, specifically at the level of the PBL, is the leading indicator of drought. While previous studies using remote-sensing techniques have examined the humidity of soil to determine drought risk, studying the atmosphere can help predict when droughts will happen.  

    In an effort funded by Lincoln Laboratory’s Climate Change Initiative, Milstein, along with laboratory staff member Michael Pieper, are working with scientists at NASA’s Jet Propulsion Laboratory (JPL) to use neural network techniques to improve drought prediction over the continental United States. While the work builds off of existing operational work JPL has done incorporating (in part) the laboratory’s operational “shallow” neural network approach for Aqua, the team believes that this work and the PBL-focused deep learning research work can be combined to further improve the accuracy of drought prediction. 

    “Lincoln Laboratory has been working with NASA for more than a decade on neural network algorithms for estimating temperature and humidity in the atmosphere from space-borne infrared and microwave instruments, including those on the Aqua spacecraft,” Milstein says. “Over that time, we have learned a lot about this problem by working with the science community, including learning about what scientific challenges remain. Our long experience working on this type of remote sensing with NASA scientists, as well as our experience with using neural network techniques, gave us a unique perspective.”

    According to Milstein, the next step for this project is to compare the deep learning results to datasets from the National Oceanic and Atmospheric Administration, NASA, and the Department of Energy collected directly in the PBL using radiosondes, a type of instrument flown on a weather balloon. “These direct measurements can be considered a kind of ‘ground truth’ to quantify the accuracy of the techniques we have developed,” Milstein says.

    This improved neural network approach holds promise to demonstrate drought prediction that can exceed the capabilities of existing indicators, Milstein says, and to be a tool that scientists can rely on for decades to come. More

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      in Environment

      Advancing technology for aquaculture

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      According to the National Oceanic and Atmospheric Administration, aquaculture in the United States represents a $1.5 billion industry annually. Like land-based farming, shellfish aquaculture requires healthy seed production in order to maintain a sustainable industry. Aquaculture hatchery production of shellfish larvae — seeds — requires close monitoring to track mortality rates and assess health from the earliest stages of life. 

      Careful observation is necessary to inform production scheduling, determine effects of naturally occurring harmful bacteria, and ensure sustainable seed production. This is an essential step for shellfish hatcheries but is currently a time-consuming manual process prone to human error. 

      With funding from MIT’s Abdul Latif Jameel Water and Food Systems Lab (J-WAFS), MIT Sea Grant is working with Associate Professor Otto Cordero of the MIT Department of Civil and Environmental Engineering, Professor Taskin Padir and Research Scientist Mark Zolotas at the Northeastern University Institute for Experiential Robotics, and others at the Aquaculture Research Corporation (ARC), and the Cape Cod Commercial Fishermen’s Alliance, to advance technology for the aquaculture industry. Located on Cape Cod, ARC is a leading shellfish hatchery, farm, and wholesaler that plays a vital role in providing high-quality shellfish seed to local and regional growers.

      Two MIT students have joined the effort this semester, working with Robert Vincent, MIT Sea Grant’s assistant director of advisory services, through the Undergraduate Research Opportunities Program (UROP). 

      First-year student Unyime Usua and sophomore Santiago Borrego are using microscopy images of shellfish seed from ARC to train machine learning algorithms that will help automate the identification and counting process. The resulting user-friendly image recognition tool aims to aid aquaculturists in differentiating and counting healthy, unhealthy, and dead shellfish larvae, improving accuracy and reducing time and effort.

      Vincent explains that AI is a powerful tool for environmental science that enables researchers, industry, and resource managers to address challenges that have long been pinch points for accurate data collection, analysis, predictions, and streamlining processes. “Funding support from programs like J-WAFS enable us to tackle these problems head-on,” he says. 

      ARC faces challenges with manually quantifying larvae classes, an important step in their seed production process. “When larvae are in their growing stages they are constantly being sized and counted,” explains Cheryl James, ARC larval/juvenile production manager. “This process is critical to encourage optimal growth and strengthen the population.” 

      Developing an automated identification and counting system will help to improve this step in the production process with time and cost benefits. “This is not an easy task,” says Vincent, “but with the guidance of Dr. Zolotas at the Northeastern University Institute for Experiential Robotics and the work of the UROP students, we have made solid progress.” 

      The UROP program benefits both researchers and students. Involving MIT UROP students in developing these types of systems provides insights into AI applications that they might not have considered, providing opportunities to explore, learn, and apply themselves while contributing to solving real challenges.

      Borrego saw this project as an opportunity to apply what he’d learned in class 6.390 (Introduction to Machine Learning) to a real-world issue. “I was starting to form an idea of how computers can see images and extract information from them,” he says. “I wanted to keep exploring that.”

      Usua decided to pursue the project because of the direct industry impacts it could have. “I’m pretty interested in seeing how we can utilize machine learning to make people’s lives easier. We are using AI to help biologists make this counting and identification process easier.” While Usua wasn’t familiar with aquaculture before starting this project, she explains, “Just hearing about the hatcheries that Dr. Vincent was telling us about, it was unfortunate that not a lot of people know what’s going on and the problems that they’re facing.”

      On Cape Cod alone, aquaculture is an $18 million per year industry. But the Massachusetts Division of Marine Fisheries estimates that hatcheries are only able to meet 70–80 percent of seed demand annually, which impacts local growers and economies. Through this project, the partners aim to develop technology that will increase seed production, advance industry capabilities, and help understand and improve the hatchery microbiome.

      Borrego explains the initial challenge of having limited data to work with. “Starting out, we had to go through and label all of the data, but going through that process helped me learn a lot.” In true MIT fashion, he shares his takeaway from the project: “Try to get the best out of what you’re given with the data you have to work with. You’re going to have to adapt and change your strategies depending on what you have.”

      Usua describes her experience going through the research process, communicating in a team, and deciding what approaches to take. “Research is a difficult and long process, but there is a lot to gain from it because it teaches you to look for things on your own and find your own solutions to problems.”

      In addition to increasing seed production and reducing the human labor required in the hatchery process, the collaborators expect this project to contribute to cost savings and technology integration to support one of the most underserved industries in the United States. 

      Borrego and Usua both plan to continue their work for a second semester with MIT Sea Grant. Borrego is interested in learning more about how technology can be used to protect the environment and wildlife. Usua says she hopes to explore more projects related to aquaculture. “It seems like there’s an infinite amount of ways to tackle these issues.” More

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      in Environment

      MIT-derived algorithm helps forecast the frequency of extreme weather

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      To assess a community’s risk of extreme weather, policymakers rely first on global climate models that can be run decades, and even centuries, forward in time, but only at a coarse resolution. These models might be used to gauge, for instance, future climate conditions for the northeastern U.S., but not specifically for Boston.

      To estimate Boston’s future risk of extreme weather such as flooding, policymakers can combine a coarse model’s large-scale predictions with a finer-resolution model, tuned to estimate how often Boston is likely to experience damaging floods as the climate warms. But this risk analysis is only as accurate as the predictions from that first, coarser climate model.

      “If you get those wrong for large-scale environments, then you miss everything in terms of what extreme events will look like at smaller scales, such as over individual cities,” says Themistoklis Sapsis, the William I. Koch Professor and director of the Center for Ocean Engineering in MIT’s Department of Mechanical Engineering.

      Sapsis and his colleagues have now developed a method to “correct” the predictions from coarse climate models. By combining machine learning with dynamical systems theory, the team’s approach “nudges” a climate model’s simulations into more realistic patterns over large scales. When paired with smaller-scale models to predict specific weather events such as tropical cyclones or floods, the team’s approach produced more accurate predictions for how often specific locations will experience those events over the next few decades, compared to predictions made without the correction scheme.

      Play video

      This animation shows the evolution of storms around the northern hemisphere, as a result of a high-resolution storm model, combined with the MIT team’s corrected global climate model. The simulation improves the modeling of extreme values for wind, temperature, and humidity, which typically have significant errors in coarse scale models. Credit: Courtesy of Ruby Leung and Shixuan Zhang, PNNL

      Sapsis says the new correction scheme is general in form and can be applied to any global climate model. Once corrected, the models can help to determine where and how often extreme weather will strike as global temperatures rise over the coming years. 

      “Climate change will have an effect on every aspect of human life, and every type of life on the planet, from biodiversity to food security to the economy,” Sapsis says. “If we have capabilities to know accurately how extreme weather will change, especially over specific locations, it can make a lot of difference in terms of preparation and doing the right engineering to come up with solutions. This is the method that can open the way to do that.”

      The team’s results appear today in the Journal of Advances in Modeling Earth Systems. The study’s MIT co-authors include postdoc Benedikt Barthel Sorensen and Alexis-Tzianni Charalampopoulos SM ’19, PhD ’23, with Shixuan Zhang, Bryce Harrop, and Ruby Leung of the Pacific Northwest National Laboratory in Washington state.

      Over the hood

      Today’s large-scale climate models simulate weather features such as the average temperature, humidity, and precipitation around the world, on a grid-by-grid basis. Running simulations of these models takes enormous computing power, and in order to simulate how weather features will interact and evolve over periods of decades or longer, models average out features every 100 kilometers or so.

      “It’s a very heavy computation requiring supercomputers,” Sapsis notes. “But these models still do not resolve very important processes like clouds or storms, which occur over smaller scales of a kilometer or less.”

      To improve the resolution of these coarse climate models, scientists typically have gone under the hood to try and fix a model’s underlying dynamical equations, which describe how phenomena in the atmosphere and oceans should physically interact.

      “People have tried to dissect into climate model codes that have been developed over the last 20 to 30 years, which is a nightmare, because you can lose a lot of stability in your simulation,” Sapsis explains. “What we’re doing is a completely different approach, in that we’re not trying to correct the equations but instead correct the model’s output.”

      The team’s new approach takes a model’s output, or simulation, and overlays an algorithm that nudges the simulation toward something that more closely represents real-world conditions. The algorithm is based on a machine-learning scheme that takes in data, such as past information for temperature and humidity around the world, and learns associations within the data that represent fundamental dynamics among weather features. The algorithm then uses these learned associations to correct a model’s predictions.

      “What we’re doing is trying to correct dynamics, as in how an extreme weather feature, such as the windspeeds during a Hurricane Sandy event, will look like in the coarse model, versus in reality,” Sapsis says. “The method learns dynamics, and dynamics are universal. Having the correct dynamics eventually leads to correct statistics, for example, frequency of rare extreme events.”

      Climate correction

      As a first test of their new approach, the team used the machine-learning scheme to correct simulations produced by the Energy Exascale Earth System Model (E3SM), a climate model run by the U.S. Department of Energy, that simulates climate patterns around the world at a resolution of 110 kilometers. The researchers used eight years of past data for temperature, humidity, and wind speed to train their new algorithm, which learned dynamical associations between the measured weather features and the E3SM model. They then ran the climate model forward in time for about 36 years and applied the trained algorithm to the model’s simulations. They found that the corrected version produced climate patterns that more closely matched real-world observations from the last 36 years, not used for training.

      “We’re not talking about huge differences in absolute terms,” Sapsis says. “An extreme event in the uncorrected simulation might be 105 degrees Fahrenheit, versus 115 degrees with our corrections. But for humans experiencing this, that is a big difference.”

      When the team then paired the corrected coarse model with a specific, finer-resolution model of tropical cyclones, they found the approach accurately reproduced the frequency of extreme storms in specific locations around the world.

      “We now have a coarse model that can get you the right frequency of events, for the present climate. It’s much more improved,” Sapsis says. “Once we correct the dynamics, this is a relevant correction, even when you have a different average global temperature, and it can be used for understanding how forest fires, flooding events, and heat waves will look in a future climate. Our ongoing work is focusing on analyzing future climate scenarios.”

      “The results are particularly impressive as the method shows promising results on E3SM, a state-of-the-art climate model,” says Pedram Hassanzadeh, an associate professor who leads the Climate Extremes Theory and Data group at the University of Chicago and was not involved with the study. “It would be interesting to see what climate change projections this framework yields once future greenhouse-gas emission scenarios are incorporated.”

      This work was supported, in part, by the U.S. Defense Advanced Research Projects Agency. More

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      in Environment

      Q&A: A blueprint for sustainable innovation

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      Atacama Biomaterials is a startup combining architecture, machine learning, and chemical engineering to create eco-friendly materials with multiple applications. Passionate about sustainable innovation, its co-founder Paloma Gonzalez-Rojas SM ’15, PhD ’21 highlights here how MIT has supported the project through several of its entrepreneurship initiatives, and reflects on the role of design in building a holistic vision for an expanding business.

      Q: What role do you see your startup playing in the sustainable materials space?

      A: Atacama Biomaterials is a venture dedicated to advancing sustainable materials through state-of-the-art technology. With my co-founder Jose Tomas Dominguez, we have been working on developing our technology since 2019. We initially started the company in 2020 under another name and received Sandbox funds the next year. In 2021, we went through The Engine’s accelerator, Blueprint, and changed our name to Atacama Biomaterials in 2022 during the MITdesignX program. 

      This technology we have developed allows us to create our own data and material library using artificial intelligence and machine learning, and serves as a platform applicable to various industries horizontally — biofuels, biological drugs, and even mining. Vertically, we produce inexpensive, regionally sourced, and environmentally friendly bio-based polymers and packaging — that is, naturally compostable plastics as a flagship product, along with AI products.

      Q: What motivated you to venture into biomaterials and found Atacama?

      A: I’m from Chile, a country with a beautiful, rich geography and nature where we can see all the problems stemming from industry, waste management, and pollution. We named our company Atacama Biomaterials because the Atacama Desert in Chile — one of the places where you can best see the stars in the world — is becoming a plastic dump, as many other places on Earth. I care deeply about sustainability, and I have an emotional attachment to stop these problems. Considering that manufacturing accounts for 29 percent of global carbon emissions, it is clear that sustainability has a role in how we define technology and entrepreneurship, as well as a socio-economic dimension.

      When I first came to MIT, it was to develop software in the Department of Architecture’s Design and Computation Group, with MIT professors Svafa Gronfeldt as co-advisor and Regina Barzilay as committee member. During my PhD, I studied machine-learning methods simulating pedestrian motion to understand how people move in space. In my work, I would use lots of plastics for 3D printing and I couldn’t stop thinking about sustainability and climate change, so I reached out to material science and mechanical engineering professors to look into biopolymers and degradable bio-based materials. This is how I met my co-founder, as we were both working with MIT Professor Neil Gershenfeld. Together, we were part of one of the first teams in the world to 3D print wood fibers, which is difficult — it’s slow and expensive — and quickly pivoted to sustainable packaging. 

      I then won a fellowship from MCSC [the MIT Climate and Sustainability Consortium], which gave me freedom to explore further, and I eventually got a postdoc in MIT chemical engineering, guided by MIT Professor Gregory Rutledge, a polymer physicist. This was unexpected in my career path. Winning Nucleate Eco Track 2022 and the MITdesignX Innovation Award in 2022 profiled Atacama Biomaterials as one of the rising startups in Boston’s biotechnology and climate-tech scene.

      Q: What is your process to develop new biomaterials?

      A: My PhD research, coupled with my background in material development and molecular dynamics, sparked the realization that principles I studied simulating pedestrian motion could also apply to molecular engineering. This connection may seem unconventional, but for me, it was a natural progression. Early in my career, I developed an intuition for materials, understanding their mechanics and physics.

      Using my experience and skills, and leveraging machine learning as a technology jump, I applied a similar conceptual framework to simulate the trajectories of molecules and find potential applications in biomaterials. Making that parallel and shift was amazing. It allowed me to optimize a state-of-the-art molecular dynamic software to run twice as fast as more traditional technologies through my algorithm presented at the International Conference of Machine Learning this year. This is very important, because this kind of simulation usually takes a week, so narrowing it down to two days has major implications for scientists and industry, in material science, chemical engineering, computer science and related fields. Such work greatly influenced the foundation of Atacama Biomaterials, where we developed our own AI to deploy our materials. In an effort to mitigate the environmental impact of manufacturing, Atacama is targeting a 16.7 percent reduction in carbon dioxide emissions associated with the manufacturing process of its polymers, through the use of renewable energy. 

      Another thing is that I was trained as an architect in Chile, and my degree had a design component. I think design allows me to understand problems at a very high level, and how things interconnect. It contributed to developing a holistic vision for Atacama, because it allowed me to jump from one technology or discipline to another and understand broader applications on a conceptual level. Our design approach also meant that sustainability came to the center of our work from the very beginning, not just a plus or an added cost.

      Q: What was the role of MITdesignX in Atacama’s development?

      A: I have known Svafa Grönfeldt, MITdesignX’s faculty director, for almost six years. She was the co-advisor of my PhD, and we had a mentor-mentee relationship. I admire the fact that she created a space for people interested in business and entrepreneurship to grow within the Department of Architecture. She and Executive Director Gilad Rosenzweig gave us fantastic advice, and we received significant support from mentors. For example, Daniel Tsai helped us with intellectual property, including a crucial patent for Atacama. And we’re still in touch with the rest of the cohort. I really like this “design your company” approach, which I find quite unique, because it gives us the opportunity to reflect on who we want to be as designers, technologists, and entrepreneurs. Studying user insights also allowed us to understand the broad applicability of our research, and align our vision with market demands, ultimately shaping Atacama into a company with a holistic perspective on sustainable material development.

      Q: How does Atacama approach scaling, and what are the immediate next steps for the company?

      A: When I think about accomplishing our vision, I feel really inspired by my 3-year-old daughter. I want her to experience a world with trees and wildlife when she’s 100 years old, and I hope Atacama will contribute to such a future.

      Going back to the designer’s perspective, we designed the whole process holistically, from feedstock to material development, incorporating AI and advanced manufacturing. Having proved that there is a demand for the materials we are developing, and having tested our products, manufacturing process, and technology in critical environments, we are now ready to scale. Our level of technology-readiness is comparable to the one used by NASA (level 4).

      We have proof of concept: a biodegradable and recyclable packaging material which is cost- and energy-efficient as a clean energy enabler in large-scale manufacturing. We have received pre-seed funding, and are sustainably scaling by taking advantage of available resources around the world, like repurposing machinery from the paper industry. As presented in the MIT Industrial Liaison and STEX Program’s recent Sustainability Conference, unlike our competitors, we have cost-parity with current packaging materials, as well as low-energy processes. And we also proved the demand for our products, which was an important milestone. Our next steps involve strategically expanding our manufacturing capabilities and research facilities and we are currently evaluating building a factory in Chile and establishing an R&D lab plus a manufacturing plant in the U.S. More

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      in Environment

      AI pilot programs look to reduce energy use and emissions on MIT campus

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      Smart thermostats have changed the way many people heat and cool their homes by using machine learning to respond to occupancy patterns and preferences, resulting in a lower energy draw. This technology — which can collect and synthesize data — generally focuses on single-dwelling use, but what if this type of artificial intelligence could dynamically manage the heating and cooling of an entire campus? That’s the idea behind a cross-departmental effort working to reduce campus energy use through AI building controls that respond in real-time to internal and external factors. 

      Understanding the challenge

      Heating and cooling can be an energy challenge for campuses like MIT, where existing building management systems (BMS) can’t respond quickly to internal factors like occupancy fluctuations or external factors such as forecast weather or the carbon intensity of the grid. This results in using more energy than needed to heat and cool spaces, often to sub-optimal levels. By engaging AI, researchers have begun to establish a framework to understand and predict optimal temperature set points (the temperature at which a thermostat has been set to maintain) at the individual room level and take into consideration a host of factors, allowing the existing systems to heat and cool more efficiently, all without manual intervention. 

      “It’s not that different from what folks are doing in houses,” explains Les Norford, a professor of architecture at MIT, whose work in energy studies, controls, and ventilation connected him with the effort. “Except we have to think about things like how long a classroom may be used in a day, weather predictions, time needed to heat and cool a room, the effect of the heat from the sun coming in the window, and how the classroom next door might impact all of this.” These factors are at the crux of the research and pilots that Norford and a team are focused on. That team includes Jeremy Gregory, executive director of the MIT Climate and Sustainability Consortium; Audun Botterud, principal research scientist for the Laboratory for Information and Decision Systems; Steve Lanou, project manager in the MIT Office of Sustainability (MITOS); Fran Selvaggio, Department of Facilities Senior Building Management Systems engineer; and Daisy Green and You Lin, both postdocs.

      The group is organized around the call to action to “explore possibilities to employ artificial intelligence to reduce on-campus energy consumption” outlined in Fast Forward: MIT’s Climate Action Plan for the Decade, but efforts extend back to 2019. “As we work to decarbonize our campus, we’re exploring all avenues,” says Vice President for Campus Services and Stewardship Joe Higgins, who originally pitched the idea to students at the 2019 MIT Energy Hack. “To me, it was a great opportunity to utilize MIT expertise and see how we can apply it to our campus and share what we learn with the building industry.” Research into the concept kicked off at the event and continued with undergraduate and graduate student researchers running differential equations and managing pilots to test the bounds of the idea. Soon, Gregory, who is also a MITOS faculty fellow, joined the project and helped identify other individuals to join the team. “My role as a faculty fellow is to find opportunities to connect the research community at MIT with challenges MIT itself is facing — so this was a perfect fit for that,” Gregory says. 

      Early pilots of the project focused on testing thermostat set points in NW23, home to the Department of Facilities and Office of Campus Planning, but Norford quickly realized that classrooms provide many more variables to test, and the pilot was expanded to Building 66, a mixed-use building that is home to classrooms, offices, and lab spaces. “We shifted our attention to study classrooms in part because of their complexity, but also the sheer scale — there are hundreds of them on campus, so [they offer] more opportunities to gather data and determine parameters of what we are testing,” says Norford. 

      Developing the technology

      The work to develop smarter building controls starts with a physics-based model using differential equations to understand how objects can heat up or cool down, store heat, and how the heat may flow across a building façade. External data like weather, carbon intensity of the power grid, and classroom schedules are also inputs, with the AI responding to these conditions to deliver an optimal thermostat set point each hour — one that provides the best trade-off between the two objectives of thermal comfort of occupants and energy use. That set point then tells the existing BMS how much to heat up or cool down a space. Real-life testing follows, surveying building occupants about their comfort. Botterud, whose research focuses on the interactions between engineering, economics, and policy in electricity markets, works to ensure that the AI algorithms can then translate this learning into energy and carbon emission savings. 

      Currently the pilots are focused on six classrooms within Building 66, with the intent to move onto lab spaces before expanding to the entire building. “The goal here is energy savings, but that’s not something we can fully assess until we complete a whole building,” explains Norford. “We have to work classroom by classroom to gather the data, but are looking at a much bigger picture.” The research team used its data-driven simulations to estimate significant energy savings while maintaining thermal comfort in the six classrooms over two days, but further work is needed to implement the controls and measure savings across an entire year. 

      With significant savings estimated across individual classrooms, the energy savings derived from an entire building could be substantial, and AI can help meet that goal, explains Botterud: “This whole concept of scalability is really at the heart of what we are doing. We’re spending a lot of time in Building 66 to figure out how it works and hoping that these algorithms can be scaled up with much less effort to other rooms and buildings so solutions we are developing can make a big impact at MIT,” he says.

      Part of that big impact involves operational staff, like Selvaggio, who are essential in connecting the research to current operations and putting them into practice across campus. “Much of the BMS team’s work is done in the pilot stage for a project like this,” he says. “We were able to get these AI systems up and running with our existing BMS within a matter of weeks, allowing the pilots to get off the ground quickly.” Selvaggio says in preparation for the completion of the pilots, the BMS team has identified an additional 50 buildings on campus where the technology can easily be installed in the future to start energy savings. The BMS team also collaborates with the building automation company, Schneider Electric, that has implemented the new control algorithms in Building 66 classrooms and is ready to expand to new pilot locations. 

      Expanding impact

      The successful completion of these programs will also open the possibility for even greater energy savings — bringing MIT closer to its decarbonization goals. “Beyond just energy savings, we can eventually turn our campus buildings into a virtual energy network, where thousands of thermostats are aggregated and coordinated to function as a unified virtual entity,” explains Higgins. These types of energy networks can accelerate power sector decarbonization by decreasing the need for carbon-intensive power plants at peak times and allowing for more efficient power grid energy use.

      As pilots continue, they fulfill another call to action in Fast Forward — for campus to be a “test bed for change.” Says Gregory: “This project is a great example of using our campus as a test bed — it brings in cutting-edge research to apply to decarbonizing our own campus. It’s a great project for its specific focus, but also for serving as a model for how to utilize the campus as a living lab.” More

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      in Energy

      An interdisciplinary approach to fighting climate change through clean energy solutions

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      In early 2021, the U.S. government set an ambitious goal: to decarbonize its power grid, the system that generates and transmits electricity throughout the country, by 2035. It’s an important goal in the fight against climate change, and will require a switch from current, greenhouse-gas producing energy sources (such as coal and natural gas), to predominantly renewable ones (such as wind and solar).

      Getting the power grid to zero carbon will be a challenging undertaking, as Audun Botterud, a principal research scientist at the MIT Laboratory for Information and Decision Systems (LIDS) who has long been interested in the problem, knows well. It will require building lots of renewable energy generators and new infrastructure; designing better technology to capture, store, and carry electricity; creating the right regulatory and economic incentives; and more. Decarbonizing the grid also presents many computational challenges, which is where Botterud’s focus lies. Botterud has modeled different aspects of the grid — the mechanics of energy supply, demand, and storage, and electricity markets — where economic factors can have a huge effect on how quickly renewable solutions get adopted.

      On again, off again

      A major challenge of decarbonization is that the grid must be designed and operated to reliably meet demand. Using renewable energy sources complicates this, as wind and solar power depend on an infamously volatile system: the weather. A sunny day becomes gray and blustery, and wind turbines get a boost but solar farms go idle. This will make the grid’s energy supply variable and hard to predict. Additional resources, including batteries and backup power generators, will need to be incorporated to regulate supply. Extreme weather events, which are becoming more common with climate change, can further strain both supply and demand. Managing a renewables-driven grid will require algorithms that can minimize uncertainty in the face of constant, sometimes random fluctuations to make better predictions of supply and demand, guide how resources are added to the grid, and inform how those resources are committed and dispatched across the entire United States.

      “The problem of managing supply and demand in the grid has to happen every second throughout the year, and given how much we rely on electricity in society, we need to get this right,” Botterud says. “You cannot let the reliability drop as you increase the amount of renewables, especially because I think that will lead to resistance towards adopting renewables.”

      That is why Botterud feels fortunate to be working on the decarbonization problem at LIDS — even though a career here is not something he had originally planned. Botterud’s first experience with MIT came during his time as a graduate student in his home country of Norway, when he spent a year as a visiting student with what is now called the MIT Energy Initiative. He might never have returned, except that while at MIT, Botterud met his future wife, Bilge Yildiz. The pair both ended up working at the Argonne National Laboratory outside of Chicago, with Botterud focusing on challenges related to power systems and electricity markets. Then Yildiz got a faculty position at MIT, where she is a professor of nuclear and materials science and engineering. Botterud moved back to the Cambridge area with her and continued to work for Argonne remotely, but he also kept an eye on local opportunities. Eventually, a position at LIDS became available, and Botterud took it, while maintaining his connections to Argonne.

      “At first glance, it may not be an obvious fit,” Botterud says. “My work is very focused on a specific application, power system challenges, and LIDS tends to be more focused on fundamental methods to use across many different application areas. However, being at LIDS, my lab [the Energy Analytics Group] has access to the most recent advances in these fundamental methods, and we can apply them to power and energy problems. Other people at LIDS are working on energy too, so there is growing momentum to address these important problems.”

      Weather, space, and time

      Much of Botterud’s research involves optimization, using mathematical programming to compare alternatives and find the best solution. Common computational challenges include dealing with large geographical areas that contain regions with different weather, different types and quantities of renewable energy available, and different infrastructure and consumer needs — such as the entire United States. Another challenge is the need for granular time resolution, sometimes even down to the sub-second level, to account for changes in energy supply and demand.

      Often, Botterud’s group will use decomposition to solve such large problems piecemeal and then stitch together solutions. However, it’s also important to consider systems as a whole. For example, in a recent paper, Botterud’s lab looked at the effect of building new transmission lines as part of national decarbonization. They modeled solutions assuming coordination at the state, regional, or national level, and found that the more regions coordinate to build transmission infrastructure and distribute electricity, the less they will need to spend to reach zero carbon.

      In other projects, Botterud uses game theory approaches to study strategic interactions in electricity markets. For example, he has designed agent-based models to analyze electricity markets. These assume each actor will make strategic decisions in their own best interest and then simulate interactions between them. Interested parties can use the models to see what would happen under different conditions and market rules, which may lead companies to make different investment decisions, or governing bodies to issue different regulations and incentives. These choices can shape how quickly the grid gets decarbonized.

      Botterud is also collaborating with researchers in MIT’s chemical engineering department who are working on improving battery storage technologies. Batteries will help manage variable renewable energy supply by capturing surplus energy during periods of high generation to release during periods of insufficient generation. Botterud’s group models the sort of charge cycles that batteries are likely to experience in the power grid, so that chemical engineers in the lab can test their batteries’ abilities in more realistic scenarios. In turn, this also leads to a more realistic representation of batteries in power system optimization models.

      These are only some of the problems that Botterud works on. He enjoys the challenge of tackling a spectrum of different projects, collaborating with everyone from engineers to architects to economists. He also believes that such collaboration leads to better solutions. The problems created by climate change are myriad and complex, and solving them will require researchers to cooperate and explore.

      “In order to have a real impact on interdisciplinary problems like energy and climate,” Botterud says, “you need to get outside of your research sweet spot and broaden your approach.” More

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      Integrating humans with AI in structural design

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      Modern fabrication tools such as 3D printers can make structural materials in shapes that would have been difficult or impossible using conventional tools. Meanwhile, new generative design systems can take great advantage of this flexibility to create innovative designs for parts of a new building, car, or virtually any other device.

      But such “black box” automated systems often fall short of producing designs that are fully optimized for their purpose, such as providing the greatest strength in proportion to weight or minimizing the amount of material needed to support a given load. Fully manual design, on the other hand, is time-consuming and labor-intensive.

      Now, researchers at MIT have found a way to achieve some of the best of both of these approaches. They used an automated design system but stopped the process periodically to allow human engineers to evaluate the work in progress and make tweaks or adjustments before letting the computer resume its design process. Introducing a few of these iterations produced results that performed better than those designed by the automated system alone, and the process was completed more quickly compared to the fully manual approach.

      The results are reported this week in the journal Structural and Multidisciplinary Optimization, in a paper by MIT doctoral student Dat Ha and assistant professor of civil and environmental engineering Josephine Carstensen.

      The basic approach can be applied to a broad range of scales and applications, Carstensen explains, for the design of everything from biomedical devices to nanoscale materials to structural support members of a skyscraper. Already, automated design systems have found many applications. “If we can make things in a better way, if we can make whatever we want, why not make it better?” she asks.

      “It’s a way to take advantage of how we can make things in much more complex ways than we could in the past,” says Ha, adding that automated design systems have already begun to be widely used over the last decade in automotive and aerospace industries, where reducing weight while maintaining structural strength is a key need.

      “You can take a lot of weight out of components, and in these two industries, everything is driven by weight,” he says. In some cases, such as internal components that aren’t visible, appearance is irrelevant, but for other structures aesthetics may be important as well. The new system makes it possible to optimize designs for visual as well as mechanical properties, and in such decisions the human touch is essential.

      As a demonstration of their process in action, the researchers designed a number of structural load-bearing beams, such as might be used in a building or a bridge. In their iterations, they saw that the design has an area that could fail prematurely, so they selected that feature and required the program to address it. The computer system then revised the design accordingly, removing the highlighted strut and strengthening some other struts to compensate, and leading to an improved final design.

      The process, which they call Human-Informed Topology Optimization, begins by setting out the needed specifications — for example, a beam needs to be this length, supported on two points at its ends, and must support this much of a load. “As we’re seeing the structure evolve on the computer screen in response to initial specification,” Carstensen says, “we interrupt the design and ask the user to judge it. The user can select, say, ‘I’m not a fan of this region, I’d like you to beef up or beef down this feature size requirement.’ And then the algorithm takes into account the user input.”

      While the result is not as ideal as what might be produced by a fully rigorous yet significantly slower design algorithm that considers the underlying physics, she says it can be much better than a result generated by a rapid automated design system alone. “You don’t get something that’s quite as good, but that was not necessarily the goal. What we can show is that instead of using several hours to get something, we can use 10 minutes and get something much better than where we started off.”

      The system can be used to optimize a design based on any desired properties, not just strength and weight. For example, it can be used to minimize fracture or buckling, or to reduce stresses in the material by softening corners.

      Carstensen says, “We’re not looking to replace the seven-hour solution. If you have all the time and all the resources in the world, obviously you can run these and it’s going to give you the best solution.” But for many situations, such as designing replacement parts for equipment in a war zone or a disaster-relief area with limited computational power available, “then this kind of solution that catered directly to your needs would prevail.”

      Similarly, for smaller companies manufacturing equipment in essentially “mom and pop” businesses, such a simplified system might be just the ticket. The new system they developed is not only simple and efficient to run on smaller computers, but it also requires far less training to produce useful results, Carstensen says. A basic two-dimensional version of the software, suitable for designing basic beams and structural parts, is freely available now online, she says, as the team continues to develop a full 3D version.

      “The potential applications of Prof Carstensen’s research and tools are quite extraordinary,” says Christian Málaga-Chuquitaype, a professor of civil and environmental engineering at Imperial College London, who was not associated with this work. “With this work, her group is paving the way toward a truly synergistic human-machine design interaction.”

      “By integrating engineering ‘intuition’ (or engineering ‘judgement’) into a rigorous yet computationally efficient topology optimization process, the human engineer is offered the possibility of guiding the creation of optimal structural configurations in a way that was not available to us before,” he adds. “Her findings have the potential to change the way engineers tackle ‘day-to-day’ design tasks.” More

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      Computers that power self-driving cars could be a huge driver of global carbon emissions

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      In the future, the energy needed to run the powerful computers on board a global fleet of autonomous vehicles could generate as many greenhouse gas emissions as all the data centers in the world today.

      That is one key finding of a new study from MIT researchers that explored the potential energy consumption and related carbon emissions if autonomous vehicles are widely adopted.

      The data centers that house the physical computing infrastructure used for running applications are widely known for their large carbon footprint: They currently account for about 0.3 percent of global greenhouse gas emissions, or about as much carbon as the country of Argentina produces annually, according to the International Energy Agency. Realizing that less attention has been paid to the potential footprint of autonomous vehicles, the MIT researchers built a statistical model to study the problem. They determined that 1 billion autonomous vehicles, each driving for one hour per day with a computer consuming 840 watts, would consume enough energy to generate about the same amount of emissions as data centers currently do.

      The researchers also found that in over 90 percent of modeled scenarios, to keep autonomous vehicle emissions from zooming past current data center emissions, each vehicle must use less than 1.2 kilowatts of power for computing, which would require more efficient hardware. In one scenario — where 95 percent of the global fleet of vehicles is autonomous in 2050, computational workloads double every three years, and the world continues to decarbonize at the current rate — they found that hardware efficiency would need to double faster than every 1.1 years to keep emissions under those levels.

      “If we just keep the business-as-usual trends in decarbonization and the current rate of hardware efficiency improvements, it doesn’t seem like it is going to be enough to constrain the emissions from computing onboard autonomous vehicles. This has the potential to become an enormous problem. But if we get ahead of it, we could design more efficient autonomous vehicles that have a smaller carbon footprint from the start,” says first author Soumya Sudhakar, a graduate student in aeronautics and astronautics.

      Sudhakar wrote the paper with her co-advisors Vivienne Sze, associate professor in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Research Laboratory of Electronics (RLE); and Sertac Karaman, associate professor of aeronautics and astronautics and director of the Laboratory for Information and Decision Systems (LIDS). The research appears today in the January-February issue of IEEE Micro.

      Modeling emissions

      The researchers built a framework to explore the operational emissions from computers on board a global fleet of electric vehicles that are fully autonomous, meaning they don’t require a back-up human driver.

      The model is a function of the number of vehicles in the global fleet, the power of each computer on each vehicle, the hours driven by each vehicle, and the carbon intensity of the electricity powering each computer.

      “On its own, that looks like a deceptively simple equation. But each of those variables contains a lot of uncertainty because we are considering an emerging application that is not here yet,” Sudhakar says.

      For instance, some research suggests that the amount of time driven in autonomous vehicles might increase because people can multitask while driving and the young and the elderly could drive more. But other research suggests that time spent driving might decrease because algorithms could find optimal routes that get people to their destinations faster.

      In addition to considering these uncertainties, the researchers also needed to model advanced computing hardware and software that doesn’t exist yet.

      To accomplish that, they modeled the workload of a popular algorithm for autonomous vehicles, known as a multitask deep neural network because it can perform many tasks at once. They explored how much energy this deep neural network would consume if it were processing many high-resolution inputs from many cameras with high frame rates, simultaneously.

      When they used the probabilistic model to explore different scenarios, Sudhakar was surprised by how quickly the algorithms’ workload added up.

      For example, if an autonomous vehicle has 10 deep neural networks processing images from 10 cameras, and that vehicle drives for one hour a day, it will make 21.6 million inferences each day. One billion vehicles would make 21.6 quadrillion inferences. To put that into perspective, all of Facebook’s data centers worldwide make a few trillion inferences each day (1 quadrillion is 1,000 trillion).

      “After seeing the results, this makes a lot of sense, but it is not something that is on a lot of people’s radar. These vehicles could actually be using a ton of computer power. They have a 360-degree view of the world, so while we have two eyes, they may have 20 eyes, looking all over the place and trying to understand all the things that are happening at the same time,” Karaman says.

      Autonomous vehicles would be used for moving goods, as well as people, so there could be a massive amount of computing power distributed along global supply chains, he says. And their model only considers computing — it doesn’t take into account the energy consumed by vehicle sensors or the emissions generated during manufacturing.

      Keeping emissions in check

      To keep emissions from spiraling out of control, the researchers found that each autonomous vehicle needs to consume less than 1.2 kilowatts of energy for computing. For that to be possible, computing hardware must become more efficient at a significantly faster pace, doubling in efficiency about every 1.1 years.

      One way to boost that efficiency could be to use more specialized hardware, which is designed to run specific driving algorithms. Because researchers know the navigation and perception tasks required for autonomous driving, it could be easier to design specialized hardware for those tasks, Sudhakar says. But vehicles tend to have 10- or 20-year lifespans, so one challenge in developing specialized hardware would be to “future-proof” it so it can run new algorithms.

      In the future, researchers could also make the algorithms more efficient, so they would need less computing power. However, this is also challenging because trading off some accuracy for more efficiency could hamper vehicle safety.

      Now that they have demonstrated this framework, the researchers want to continue exploring hardware efficiency and algorithm improvements. In addition, they say their model can be enhanced by characterizing embodied carbon from autonomous vehicles — the carbon emissions generated when a car is manufactured — and emissions from a vehicle’s sensors.

      While there are still many scenarios to explore, the researchers hope that this work sheds light on a potential problem people may not have considered.

      “We are hoping that people will think of emissions and carbon efficiency as important metrics to consider in their designs. The energy consumption of an autonomous vehicle is really critical, not just for extending the battery life, but also for sustainability,” says Sze.

      This research was funded, in part, by the National Science Foundation and the MIT-Accenture Fellowship. More

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      Manufacturing a cleaner future

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      Manufacturing had a big summer. The CHIPS and Science Act, signed into law in August, represents a massive investment in U.S. domestic manufacturing. The act aims to drastically expand the U.S. semiconductor industry, strengthen supply chains, and invest in R&D for new technological breakthroughs. According to John Hart, professor of mechanical engineering and director of the Laboratory for Manufacturing and Productivity at MIT, the CHIPS Act is just the latest example of significantly increased interest in manufacturing in recent years.

      “You have multiple forces working together: reflections from the pandemic’s impact on supply chains, the geopolitical situation around the world, and the urgency and importance of sustainability,” says Hart. “This has now aligned incentives among government, industry, and the investment community to accelerate innovation in manufacturing and industrial technology.”

      Hand-in-hand with this increased focus on manufacturing is a need to prioritize sustainability.

      Roughly one-quarter of greenhouse gas emissions came from industry and manufacturing in 2020. Factories and plants can also deplete local water reserves and generate vast amounts of waste, some of which can be toxic.

      To address these issues and drive the transition to a low-carbon economy, new products and industrial processes must be developed alongside sustainable manufacturing technologies. Hart sees mechanical engineers as playing a crucial role in this transition.

      “Mechanical engineers can uniquely solve critical problems that require next-generation hardware technologies, and know how to bring their solutions to scale,” says Hart.

      Several fast-growing companies founded by faculty and alumni from MIT’s Department of Mechanical Engineering offer solutions for manufacturing’s environmental problem, paving the path for a more sustainable future.

      Gradiant: Cleantech water solutions

      Manufacturing requires water, and lots of it. A medium-sized semiconductor fabrication plant uses upward of 10 million gallons of water a day. In a world increasingly plagued by droughts, this dependence on water poses a major challenge.

      Gradiant offers a solution to this water problem. Co-founded by Anurag Bajpayee SM ’08, PhD ’12 and Prakash Govindan PhD ’12, the company is a pioneer in sustainable — or “cleantech” — water projects.

      As doctoral students in the Rohsenow Kendall Heat Transfer Laboratory, Bajpayee and Govindan shared a pragmatism and penchant for action. They both worked on desalination research — Bajpayee with Professor Gang Chen and Govindan with Professor John Lienhard.

      Inspired by a childhood spent during a severe drought in Chennai, India, Govindan developed for his PhD a humidification-dehumidification technology that mimicked natural rainfall cycles. It was with this piece of technology, which they named Carrier Gas Extraction (CGE), that the duo founded Gradiant in 2013.

      The key to CGE lies in a proprietary algorithm that accounts for variability in the quality and quantity in wastewater feed. At the heart of the algorithm is a nondimensional number, which Govindan proposes one day be called the “Lienhard Number,” after his doctoral advisor.

      “When the water quality varies in the system, our technology automatically sends a signal to motors within the plant to adjust the flow rates to bring back the nondimensional number to a value of one. Once it’s brought back to a value of one, you’re running in optimal condition,” explains Govindan, who serves as chief operating officer of Gradiant.

      This system can treat and clean the wastewater produced by a manufacturing plant for reuse, ultimately conserving millions of gallons of water each year.

      As the company has grown, the Gradiant team has added new technologies to their arsenal, including Selective Contaminant Extraction, a cost-efficient method that removes only specific contaminants, and a brine-concentration method called Counter-Flow Reverse Osmosis. They now offer a full technology stack of water and wastewater treatment solutions to clients in industries including pharmaceuticals, energy, mining, food and beverage, and the ever-growing semiconductor industry.

      “We are an end-to-end water solutions provider. We have a portfolio of proprietary technologies and will pick and choose from our ‘quiver’ depending on a customer’s needs,” says Bajpayee, who serves as CEO of Gradiant. “Customers look at us as their water partner. We can take care of their water problem end-to-end so they can focus on their core business.”

      Gradiant has seen explosive growth over the past decade. With 450 water and wastewater treatment plants built to date, they treat the equivalent of 5 million households’ worth of water each day. Recent acquisitions saw their total employees rise to above 500.

      The diversity of Gradiant’s solutions is reflected in their clients, who include Pfizer, AB InBev, and Coca-Cola. They also count semiconductor giants like Micron Technology, GlobalFoundries, Intel, and TSMC among their customers.

      “Over the last few years, we have really developed our capabilities and reputation serving semiconductor wastewater and semiconductor ultrapure water,” says Bajpayee.

      Semiconductor manufacturers require ultrapure water for fabrication. Unlike drinking water, which has a total dissolved solids range in the parts per million, water used to manufacture microchips has a range in the parts per billion or quadrillion.

      Currently, the average recycling rate at semiconductor fabrication plants — or fabs — in Singapore is only 43 percent. Using Gradiant’s technologies, these fabs can recycle 98-99 percent of the 10 million gallons of water they require daily. This reused water is pure enough to be put back into the manufacturing process.

      “What we’ve done is eliminated the discharge of this contaminated water and nearly eliminated the dependence of the semiconductor fab on the public water supply,” adds Bajpayee.

      With new regulations being introduced, pressure is increasing for fabs to improve their water use, making sustainability even more important to brand owners and their stakeholders.

      As the domestic semiconductor industry expands in light of the CHIPS and Science Act, Gradiant sees an opportunity to bring their semiconductor water treatment technologies to more factories in the United States.

      Via Separations: Efficient chemical filtration

      Like Bajpayee and Govindan, Shreya Dave ’09, SM ’12, PhD ’16 focused on desalination for her doctoral thesis. Under the guidance of her advisor Jeffrey Grossman, professor of materials science and engineering, Dave built a membrane that could enable more efficient and cheaper desalination.

      A thorough cost and market analysis brought Dave to the conclusion that the desalination membrane she developed would not make it to commercialization.

      “The current technologies are just really good at what they do. They’re low-cost, mass produced, and they worked. There was no room in the market for our technology,” says Dave.

      Shortly after defending her thesis, she read a commentary article in the journal Nature that changed everything. The article outlined a problem. Chemical separations that are central to many manufacturing processes require a huge amount of energy. Industry needed more efficient and cheaper membranes. Dave thought she might have a solution.

      After determining there was an economic opportunity, Dave, Grossman, and Brent Keller PhD ’16 founded Via Separations in 2017. Shortly thereafter, they were chosen as one of the first companies to receive funding from MIT’s venture firm, The Engine.

      Currently, industrial filtration is done by heating chemicals at very high temperatures to separate compounds. Dave likens it to making pasta by boiling all of the water off until it evaporates and all you are left with is the pasta noodles. In manufacturing, this method of chemical separation is extremely energy-intensive and inefficient.

      Via Separations has created the chemical equivalent of a “pasta strainer.” Rather than using heat to separate, their membranes “strain” chemical compounds. This method of chemical filtration uses 90 percent less energy than standard methods.

      While most membranes are made of polymers, Via Separations’ membranes are made with graphene oxide, which can withstand high temperatures and harsh conditions. The membrane is calibrated to the customer’s needs by altering the pore size and tuning the surface chemistry.

      Currently, Dave and her team are focusing on the pulp and paper industry as their beachhead market. They have developed a system that makes the recovery of a substance known as “black liquor” more energy efficient.

      “When tree becomes paper, only one-third of the biomass is used for the paper. Currently the most valuable use for the remaining two-thirds not needed for paper is to take it from a pretty dilute stream to a pretty concentrated stream using evaporators by boiling off the water,” says Dave.

      This black liquor is then burned. Most of the resulting energy is used to power the filtration process.

      “This closed-loop system accounts for an enormous amount of energy consumption in the U.S. We can make that process 84 percent more efficient by putting the ‘pasta strainer’ in front of the boiler,” adds Dave.

      VulcanForms: Additive manufacturing at industrial scale

      The first semester John Hart taught at MIT was a fruitful one. He taught a course on 3D printing, broadly known as additive manufacturing (AM). While it wasn’t his main research focus at the time, he found the topic fascinating. So did many of the students in the class, including Martin Feldmann MEng ’14.

      After graduating with his MEng in advanced manufacturing, Feldmann joined Hart’s research group full time. There, they bonded over their shared interest in AM. They saw an opportunity to innovate with an established metal AM technology, known as laser powder bed fusion, and came up with a concept to realize metal AM at an industrial scale.

      The pair co-founded VulcanForms in 2015.

      “We have developed a machine architecture for metal AM that can build parts with exceptional quality and productivity,” says Hart. “And, we have integrated our machines in a fully digital production system, combining AM, postprocessing, and precision machining.”

      Unlike other companies that sell 3D printers for others to produce parts, VulcanForms makes and sells parts for their customers using their fleet of industrial machines. VulcanForms has grown to nearly 400 employees. Last year, the team opened their first production factory, known as “VulcanOne,” in Devens, Massachusetts.

      The quality and precision with which VulcanForms produces parts is critical for products like medical implants, heat exchangers, and aircraft engines. Their machines can print layers of metal thinner than a human hair.

      “We’re producing components that are difficult, or in some cases impossible to manufacture otherwise,” adds Hart, who sits on the company’s board of directors.

      The technologies developed at VulcanForms may help lead to a more sustainable way to manufacture parts and products, both directly through the additive process and indirectly through more efficient, agile supply chains.

      One way that VulcanForms, and AM in general, promotes sustainability is through material savings.

      Many of the materials VulcanForms uses, such as titanium alloys, require a great deal of energy to produce. When titanium parts are 3D-printed, substantially less of the material is used than in a traditional machining process. This material efficiency is where Hart sees AM making a large impact in terms of energy savings.

      Hart also points out that AM can accelerate innovation in clean energy technologies, ranging from more efficient jet engines to future fusion reactors.

      “Companies seeking to de-risk and scale clean energy technologies require know-how and access to advanced manufacturing capability, and industrial additive manufacturing is transformative in this regard,” Hart adds.

      LiquiGlide: Reducing waste by removing friction

      There is an unlikely culprit when it comes to waste in manufacturing and consumer products: friction. Kripa Varanasi, professor of mechanical engineering, and the team at LiquiGlide are on a mission to create a frictionless future, and substantially reduce waste in the process.

      Founded in 2012 by Varanasi and alum David Smith SM ’11, LiquiGlide designs custom coatings that enable liquids to “glide” on surfaces. Every last drop of a product can be used, whether it’s being squeezed out of a tube of toothpaste or drained from a 500-liter tank at a manufacturing plant. Making containers frictionless substantially minimizes wasted product, and eliminates the need to clean a container before recycling or reusing.

      Since launching, the company has found great success in consumer products. Customer Colgate utilized LiquiGlide’s technologies in the design of the Colgate Elixir toothpaste bottle, which has been honored with several industry awards for design. In a collaboration with world- renowned designer Yves Béhar, LiquiGlide is applying their technology to beauty and personal care product packaging. Meanwhile, the U.S. Food and Drug Administration has granted them a Device Master Filing, opening up opportunities for the technology to be used in medical devices, drug delivery, and biopharmaceuticals.

      In 2016, the company developed a system to make manufacturing containers frictionless. Called CleanTanX, the technology is used to treat the surfaces of tanks, funnels, and hoppers, preventing materials from sticking to the side. The system can reduce material waste by up to 99 percent.

      “This could really change the game. It saves wasted product, reduces wastewater generated from cleaning tanks, and can help make the manufacturing process zero-waste,” says Varanasi, who serves as chair at LiquiGlide.

      LiquiGlide works by creating a coating made of a textured solid and liquid lubricant on the container surface. When applied to a container, the lubricant remains infused within the texture. Capillary forces stabilize and allow the liquid to spread on the surface, creating a continuously lubricated surface that any viscous material can slide right down. The company uses a thermodynamic algorithm to determine the combinations of safe solids and liquids depending on the product, whether it’s toothpaste or paint.

      The company has built a robotic spraying system that can treat large vats and tanks at manufacturing plants on site. In addition to saving companies millions of dollars in wasted product, LiquiGlide drastically reduces the amount of water needed to regularly clean these containers, which normally have product stuck to the sides.

      “Normally when you empty everything out of a tank, you still have residue that needs to be cleaned with a tremendous amount of water. In agrochemicals, for example, there are strict regulations about how to deal with the resulting wastewater, which is toxic. All of that can be eliminated with LiquiGlide,” says Varanasi.

      While the closure of many manufacturing facilities early in the pandemic slowed down the rollout of CleanTanX pilots at plants, things have picked up in recent months. As manufacturing ramps up both globally and domestically, Varanasi sees a growing need for LiquiGlide’s technologies, especially for liquids like semiconductor slurry.

      Companies like Gradiant, Via Separations, VulcanForms, and LiquiGlide demonstrate that an expansion in manufacturing industries does not need to come at a steep environmental cost. It is possible for manufacturing to be scaled up in a sustainable way.

      “Manufacturing has always been the backbone of what we do as mechanical engineers. At MIT in particular, there is always a drive to make manufacturing sustainable,” says Evelyn Wang, Ford Professor of Engineering and former head of the Department of Mechanical Engineering. “It’s amazing to see how startups that have an origin in our department are looking at every aspect of the manufacturing process and figuring out how to improve it for the health of our planet.”

      As legislation like the CHIPS and Science Act fuels growth in manufacturing, there will be an increased need for startups and companies that develop solutions to mitigate the environmental impact, bringing us closer to a more sustainable future. More