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      in Energy

      Processing waste biomass to reduce airborne emissions

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      To prepare fields for planting, farmers the world over often burn corn stalks, rice husks, hay, straw, and other waste left behind from the previous harvest. In many places, the practice creates huge seasonal clouds of smog, contributing to air pollution that kills 7 million people globally a year, according to the World Health Organization.

      Annually, $120 billion worth of crop and forest residues are burned in the open worldwide — a major waste of resources in an energy-starved world, says Kevin Kung SM ’13, PhD ’17. Kung is working to transform this waste biomass into marketable products — and capitalize on a billion-dollar global market — through his MIT spinoff company, Takachar.

      Founded in 2015, Takachar develops small-scale, low-cost, portable equipment to convert waste biomass into solid fuel using a variety of thermochemical treatments, including one known as oxygen-lean torrefaction. The technology emerged from Kung’s PhD project in the lab of Ahmed Ghoniem, the Ronald C. Crane (1972) Professor of Mechanical Engineering at MIT.

      Biomass fuels, including wood, peat, and animal dung, are a major source of carbon emissions — but billions of people rely on such fuels for cooking, heating, and other household needs. “Currently, burning biomass generates 10 percent of the primary energy used worldwide, and the process is used largely in rural, energy-poor communities. We’re not going to change that overnight. There are places with no other sources of energy,” Ghoniem says.

      What Takachar’s technology provides is a way to use biomass more cleanly and efficiently by concentrating the fuel and eliminating contaminants such as moisture and dirt, thus creating a “clean-burning” fuel — one that generates less smoke. “In rural communities where biomass is used extensively as a primary energy source, torrefaction will address air pollution head-on,” Ghoniem says.

      Thermochemical treatment densifies biomass at elevated temperatures, converting plant materials that are typically loose, wet, and bulky into compact charcoal. Centralized processing plants exist, but collection and transportation present major barriers to utilization, Kung says. Takachar’s solution moves processing into the field: To date, Takachar has worked with about 5,500 farmers to process 9,000 metric tons of crops.

      Takachar estimates its technology has the potential to reduce carbon dioxide equivalent emissions by gigatons per year at scale. (“Carbon dioxide equivalent” is a measure used to gauge global warming potential.) In recognition, in 2021 Takachar won the first-ever Earthshot Prize in the clean air category, a £1 million prize funded by Prince William and Princess Kate’s Royal Foundation.

      Roots in Kenya

      As Kung tells the story, Takachar emerged from a class project that took him to Kenya — which explains the company’s name, a combination of takataka, which mean “trash” in Swahili, and char, for the charcoal end product.

      It was 2011, and Kung was at MIT as a biological engineering grad student focused on cancer research. But “MIT gives students big latitude for exploration, and I took courses outside my department,” he says. In spring 2011, he signed up for a class known as 15.966 (Global Health Delivery Lab) in the MIT Sloan School of Management. The class brought Kung to Kenya to work with a nongovernmental organization in Nairobi’s Kibera, the largest urban slum in Africa.

      “We interviewed slum households for their views on health, and that’s when I noticed the charcoal problem,” Kung says. The problem, as Kung describes it, was that charcoal was everywhere in Kibera — piled up outside, traded by the road, and used as the primary fuel, even indoors. Its creation contributed to deforestation, and its smoke presented a serious health hazard.

      Eager to address this challenge, Kung secured fellowship support from the MIT International Development Initiative and the Priscilla King Gray Public Service Center to conduct more research in Kenya. In 2012, he formed Takachar as a team and received seed money from the MIT IDEAS Global Challenge, MIT Legatum Center for Development and Entrepreneurship, and D-Lab to produce charcoal from household organic waste. (This work also led to a fertilizer company, Safi Organics, that Kung founded in 2016 with the help of MIT IDEAS. But that is another story.)

      Meanwhile, Kung had another top priority: finding a topic for his PhD dissertation. Back at MIT, he met Alexander Slocum, the Walter M. May and A. Hazel May Professor of Mechanical Engineering, who on a long walk-and-talk along the Charles River suggested he turn his Kenya work into a thesis. Slocum connected him with Robert Stoner, deputy director for science and technology at the MIT Energy Initiative (MITEI) and founding director of MITEI’s Tata Center for Technology and Design. Stoner in turn introduced Kung to Ghoniem, who became his PhD advisor, while Slocum and Stoner joined his doctoral committee.

      Roots in MIT lab

      Ghoniem’s telling of the Takachar story begins, not surprisingly, in the lab. Back in 2010, he had a master’s student interested in renewable energy, and he suggested the student investigate biomass. That student, Richard Bates ’10, SM ’12, PhD ’16, began exploring the science of converting biomass to more clean-burning charcoal through torrefaction.

      Most torrefaction (also known as low-temperature pyrolysis) systems use external heating sources, but the lab’s goal, Ghoniem explains, was to develop an efficient, self-sustained reactor that would generate fewer emissions. “We needed to understand the chemistry and physics of the process, and develop fundamental scaling models, before going to the lab to build the device,” he says.

      By the time Kung joined the lab in 2013, Ghoniem was working with the Tata Center to identify technology suitable for developing countries and largely based on renewable energy. Kung was able to secure a Tata Fellowship and — building on Bates’ research — develop the small-scale, practical device for biomass thermochemical conversion in the field that launched Takachar.

      This device, which was patented by MIT with inventors Kung, Ghoniem, Stoner, MIT research scientist Santosh Shanbhogue, and Slocum, is self-contained and scalable. It burns a little of the biomass to generate heat; this heat bakes the rest of the biomass, releasing gases; the system then introduces air to enable these gases to combust, which burns off the volatiles and generates more heat, keeping the thermochemical reaction going.

      “The trick is how to introduce the right amount of air at the right location to sustain the process,” Ghoniem explains. “If you put in more air, that will burn the biomass. If you put in less, there won’t be enough heat to produce the charcoal. That will stop the reaction.”

      About 10 percent of the biomass is used as fuel to support the reaction, Kung says, adding that “90 percent is densified into a form that’s easier to handle and utilize.” He notes that the research received financial support from the Abdul Latif Jameel Water and Food Systems Lab and the Deshpande Center for Technological Innovation, both at MIT. Sonal Thengane, another postdoc in Ghoniem’s lab, participated in the effort to scale up the technology at the MIT Bates Lab (no relation to Richard Bates).

      The charcoal produced is more valuable per ton and easier to transport and sell than biomass, reducing transportation costs by two-thirds and giving farmers an additional income opportunity — and an incentive not to burn agricultural waste, Kung says. “There’s more income for farmers, and you get better air quality.”

      Roots in India

      When Kung became a Tata Fellow, he joined a program founded to take on the biggest challenges of the developing world, with a focus on India. According to Stoner, Tata Fellows, including Kung, typically visit India twice a year and spend six to eight weeks meeting stakeholders in industry, the government, and in communities to gain perspective on their areas of study.

      “A unique part of Tata is that you’re considering the ecosystem as a whole,” says Kung, who interviewed hundreds of smallholder farmers, met with truck drivers, and visited existing biomass processing plants during his Tata trips to India. (Along the way, he also connected with Indian engineer Vidyut Mohan, who became Takachar’s co-founder.)

      “It was very important for Kevin to be there walking about, experimenting, and interviewing farmers,” Stoner says. “He learned about the lives of farmers.”

      These experiences helped instill in Kung an appreciation for small farmers that still drives him today as Takachar rolls out its first pilot programs, tinkers with the technology, grows its team (now up to 10), and endeavors to build a revenue stream. So, while Takachar has gotten a lot of attention and accolades — from the IDEAS award to the Earthshot Prize — Kung says what motivates him is the prospect of improving people’s lives.

      The dream, he says, is to empower communities to help both the planet and themselves. “We’re excited about the environmental justice perspective,” he says. “Our work brings production and carbon removal or avoidance to rural communities — providing them with a way to convert waste, make money, and reduce air pollution.”

      This article appears in the Spring 2022 issue of Energy Futures, the magazine of the MIT Energy Initiative. More

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      in Energy

      Design’s new frontier

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      In the 1960s, the advent of computer-aided design (CAD) sparked a revolution in design. For his PhD thesis in 1963, MIT Professor Ivan Sutherland developed Sketchpad, a game-changing software program that enabled users to draw, move, and resize shapes on a computer. Over the course of the next few decades, CAD software reshaped how everything from consumer products to buildings and airplanes were designed.

      “CAD was part of the first wave in computing in design. The ability of researchers and practitioners to represent and model designs using computers was a major breakthrough and still is one of the biggest outcomes of design research, in my opinion,” says Maria Yang, Gail E. Kendall Professor and director of MIT’s Ideation Lab.

      Innovations in 3D printing during the 1980s and 1990s expanded CAD’s capabilities beyond traditional injection molding and casting methods, providing designers even more flexibility. Designers could sketch, ideate, and develop prototypes or models faster and more efficiently. Meanwhile, with the push of a button, software like that developed by Professor Emeritus David Gossard of MIT’s CAD Lab could solve equations simultaneously to produce a new geometry on the fly.

      In recent years, mechanical engineers have expanded the computing tools they use to ideate, design, and prototype. More sophisticated algorithms and the explosion of machine learning and artificial intelligence technologies have sparked a second revolution in design engineering.

      Researchers and faculty at MIT’s Department of Mechanical Engineering are utilizing these technologies to re-imagine how the products, systems, and infrastructures we use are designed. These researchers are at the forefront of the new frontier in design.

      Computational design

      Faez Ahmed wants to reinvent the wheel, or at least the bicycle wheel. He and his team at MIT’s Design Computation & Digital Engineering Lab (DeCoDE) use an artificial intelligence-driven design method that can generate entirely novel and improved designs for a range of products — including the traditional bicycle. They create advanced computational methods to blend human-driven design with simulation-based design.

      “The focus of our DeCoDE lab is computational design. We are looking at how we can create machine learning and AI algorithms to help us discover new designs that are optimized based on specific performance parameters,” says Ahmed, an assistant professor of mechanical engineering at MIT.

      For their work using AI-driven design for bicycles, Ahmed and his collaborator Professor Daniel Frey wanted to make it easier to design customizable bicycles, and by extension, encourage more people to use bicycles over transportation methods that emit greenhouse gases.

      To start, the group gathered a dataset of 4,500 bicycle designs. Using this massive dataset, they tested the limits of what machine learning could do. First, they developed algorithms to group bicycles that looked similar together and explore the design space. They then created machine learning models that could successfully predict what components are key in identifying a bicycle style, such as a road bike versus a mountain bike.

      Once the algorithms were good enough at identifying bicycle designs and parts, the team proposed novel machine learning tools that could use this data to create a unique and creative design for a bicycle based on certain performance parameters and rider dimensions.

      Ahmed used a generative adversarial network — or GAN — as the basis of this model. GAN models utilize neural networks that can create new designs based on vast amounts of data. However, using GAN models alone would result in homogeneous designs that lack novelty and can’t be assessed in terms of performance. To address these issues in design problems, Ahmed has developed a new method which he calls “PaDGAN,” performance augmented diverse GAN.

      “When we apply this type of model, what we see is that we can get large improvements in the diversity, quality, as well as novelty of the designs,” Ahmed explains.

      Using this approach, Ahmed’s team developed an open-source computational design tool for bicycles freely available on their lab website. They hope to further develop a set of generalizable tools that can be used across industries and products.

      Longer term, Ahmed has his sights set on loftier goals. He hopes the computational design tools he develops could lead to “design democratization,” putting more power in the hands of the end user.

      “With these algorithms, you can have more individualization where the algorithm assists a customer in understanding their needs and helps them create a product that satisfies their exact requirements,” he adds.

      Using algorithms to democratize the design process is a goal shared by Stefanie Mueller, an associate professor in electrical engineering and computer science and mechanical engineering.

      Personal fabrication

      Platforms like Instagram give users the freedom to instantly edit their photographs or videos using filters. In one click, users can alter the palette, tone, and brightness of their content by applying filters that range from bold colors to sepia-toned or black-and-white. Mueller, X-Window Consortium Career Development Professor, wants to bring this concept of the Instagram filter to the physical world.

      “We want to explore how digital capabilities can be applied to tangible objects. Our goal is to bring reprogrammable appearance to the physical world,” explains Mueller, director of the HCI Engineering Group based out of MIT’s Computer Science and Artificial Intelligence Laboratory.

      Mueller’s team utilizes a combination of smart materials, optics, and computation to advance personal fabrication technologies that would allow end users to alter the design and appearance of the products they own. They tested this concept in a project they dubbed “Photo-Chromeleon.”

      First, a mix of photochromic cyan, magenta, and yellow dies are airbrushed onto an object — in this instance, a 3D sculpture of a chameleon. Using software they developed, the team sketches the exact color pattern they want to achieve on the object itself. An ultraviolet light shines on the object to activate the dyes.

      To actually create the physical pattern on the object, Mueller has developed an optimization algorithm to use alongside a normal office projector outfitted with red, green, and blue LED lights. These lights shine on specific pixels on the object for a given period of time to physically change the makeup of the photochromic pigments.

      “This fancy algorithm tells us exactly how long we have to shine the red, green, and blue light on every single pixel of an object to get the exact pattern we’ve programmed in our software,” says Mueller.

      Giving this freedom to the end user enables limitless possibilities. Mueller’s team has applied this technology to iPhone cases, shoes, and even cars. In the case of shoes, Mueller envisions a shoebox embedded with UV and LED light projectors. Users could put their shoes in the box overnight and the next day have a pair of shoes in a completely new pattern.

      Mueller wants to expand her personal fabrication methods to the clothes we wear. Rather than utilize the light projection technique developed in the PhotoChromeleon project, her team is exploring the possibility of weaving LEDs directly into clothing fibers, allowing people to change their shirt’s appearance as they wear it. These personal fabrication technologies could completely alter consumer habits.

      “It’s very interesting for me to think about how these computational techniques will change product design on a high level,” adds Mueller. “In the future, a consumer could buy a blank iPhone case and update the design on a weekly or daily basis.”

      Computational fluid dynamics and participatory design

      Another team of mechanical engineers, including Sili Deng, the Brit (1961) & Alex (1949) d’Arbeloff Career Development Professor, are developing a different kind of design tool that could have a large impact on individuals in low- and middle-income countries across the world.

      As Deng walked down the hallway of Building 1 on MIT’s campus, a monitor playing a video caught her eye. The video featured work done by mechanical engineers and MIT D-Lab on developing cleaner burning briquettes for cookstoves in Uganda. Deng immediately knew she wanted to get involved.

      “As a combustion scientist, I’ve always wanted to work on such a tangible real-world problem, but the field of combustion tends to focus more heavily on the academic side of things,” explains Deng.

      After reaching out to colleagues in MIT D-Lab, Deng joined a collaborative effort to develop a new cookstove design tool for the 3 billion people across the world who burn solid fuels to cook and heat their homes. These stoves often emit soot and carbon monoxide, leading not only to millions of deaths each year, but also worsening the world’s greenhouse gas emission problem.

      The team is taking a three-pronged approach to developing this solution, using a combination of participatory design, physical modeling, and experimental validation to create a tool that will lead to the production of high-performing, low-cost energy products.

      Deng and her team in the Deng Energy and Nanotechnology Group use physics-based modeling for the combustion and emission process in cookstoves.

      “My team is focused on computational fluid dynamics. We use computational and numerical studies to understand the flow field where the fuel is burned and releases heat,” says Deng.

      These flow mechanics are crucial to understanding how to minimize heat loss and make cookstoves more efficient, as well as learning how dangerous pollutants are formed and released in the process.

      Using computational methods, Deng’s team performs three-dimensional simulations of the complex chemistry and transport coupling at play in the combustion and emission processes. They then use these simulations to build a combustion model for how fuel is burned and a pollution model that predicts carbon monoxide emissions.

      Deng’s models are used by a group led by Daniel Sweeney in MIT D-Lab to test the experimental validation in prototypes of stoves. Finally, Professor Maria Yang uses participatory design methods to integrate user feedback, ensuring the design tool can actually be used by people across the world.

      The end goal for this collaborative team is to not only provide local manufacturers with a prototype they could produce themselves, but to also provide them with a tool that can tweak the design based on local needs and available materials.

      Deng sees wide-ranging applications for the computational fluid dynamics her team is developing.

      “We see an opportunity to use physics-based modeling, augmented with a machine learning approach, to come up with chemical models for practical fuels that help us better understand combustion. Therefore, we can design new methods to minimize carbon emissions,” she adds.

      While Deng is utilizing simulations and machine learning at the molecular level to improve designs, others are taking a more macro approach.

      Designing intelligent systems

      When it comes to intelligent design, Navid Azizan thinks big. He hopes to help create future intelligent systems that are capable of making decisions autonomously by using the enormous amounts of data emerging from the physical world. From smart robots and autonomous vehicles to smart power grids and smart cities, Azizan focuses on the analysis, design, and control of intelligent systems.

      Achieving such massive feats takes a truly interdisciplinary approach that draws upon various fields such as machine learning, dynamical systems, control, optimization, statistics, and network science, among others.

      “Developing intelligent systems is a multifaceted problem, and it really requires a confluence of disciplines,” says Azizan, assistant professor of mechanical engineering with a dual appointment in MIT’s Institute for Data, Systems, and Society (IDSS). “To create such systems, we need to go beyond standard approaches to machine learning, such as those commonly used in computer vision, and devise algorithms that can enable safe, efficient, real-time decision-making for physical systems.”

      For robot control to work in the complex dynamic environments that arise in the real world, real-time adaptation is key. If, for example, an autonomous vehicle is going to drive in icy conditions or a drone is operating in windy conditions, they need to be able to adapt to their new environment quickly.

      To address this challenge, Azizan and his collaborators at MIT and Stanford University have developed a new algorithm that combines adaptive control, a powerful methodology from control theory, with meta learning, a new machine learning paradigm.

      “This ‘control-oriented’ learning approach outperforms the existing ‘regression-oriented’ methods, which are mostly focused on just fitting the data, by a wide margin,” says Azizan.

      Another critical aspect of deploying machine learning algorithms in physical systems that Azizan and his team hope to address is safety. Deep neural networks are a crucial part of autonomous systems. They are used for interpreting complex visual inputs and making data-driven predictions of future behavior in real time. However, Azizan urges caution.

      “These deep neural networks are only as good as their training data, and their predictions can often be untrustworthy in scenarios not covered by their training data,” he says. Making decisions based on such untrustworthy predictions could lead to fatal accidents in autonomous vehicles or other safety-critical systems.

      To avoid these potentially catastrophic events, Azizan proposes that it is imperative to equip neural networks with a measure of their uncertainty. When the uncertainty is high, they can then be switched to a “safe policy.”

      In pursuit of this goal, Azizan and his collaborators have developed a new algorithm known as SCOD — Sketching Curvature of Out-of-Distribution Detection. This framework could be embedded within any deep neural network to equip them with a measure of their uncertainty.

      “This algorithm is model-agnostic and can be applied to neural networks used in various kinds of autonomous systems, whether it’s drones, vehicles, or robots,” says Azizan.

      Azizan hopes to continue working on algorithms for even larger-scale systems. He and his team are designing efficient algorithms to better control supply and demand in smart energy grids. According to Azizan, even if we create the most efficient solar panels and batteries, we can never achieve a sustainable grid powered by renewable resources without the right control mechanisms.

      Mechanical engineers like Ahmed, Mueller, Deng, and Azizan serve as the key to realizing the next revolution of computing in design.

      “MechE is in a unique position at the intersection of the computational and physical worlds,” Azizan says. “Mechanical engineers build a bridge between theoretical, algorithmic tools and real, physical world applications.”

      Sophisticated computational tools, coupled with the ground truth mechanical engineers have in the physical world, could unlock limitless possibilities for design engineering, well beyond what could have been imagined in those early days of CAD. More

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      in Environment

      J-WAFS announces 2021 Solutions Grants for commercializing water and food technologies

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      The Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) recently announced the 2021 J-WAFS Solutions grant recipients. The J-WAFS Solutions program aims to propel MIT water- and food-related research toward commercialization. Grant recipients receive one year of financial support, as well as mentorship, networking, and guidance from industry experts, to begin their journey into the commercial world — whether that be in the form of bringing innovative products to market or launching cutting-edge startup companies. 

      This year, three projects will receive funding across water, food, and agriculture spaces. The winning projects will advance nascent technologies for off-grid refrigeration, portable water filtration, and dairy waste recycling. Each provides an efficient, accessible solution to the respective challenge being addressed.

      Since the start of the J-WAFS Solutions program in 2015, grants have provided instrumental support in creating a number of key MIT startups that focus on major water and food challenges. A 2015-16 grant helped the team behind Via Separations develop their business plan to massively decarbonize industrial separations processes. Other successful J-WAFS Solutions alumni include researchers who created a low-cost water filter made from tree branches and the team that launched the startup Xibus Systems, which is developing a handheld food safety sensor.

      “New technological advances are being made at MIT every day, and J-WAFS Solutions grants provide critical resources and support for these technologies to make it to market so that they can transform our local and global water and food systems,” says J-WAFS Executive Director Renee Robins. “This year’s grant recipients offer innovative tools that will provide more accessible food storage for smallholder farmers in places like Africa, safer drinking water, and a new approach to recycling food waste,” Robins notes. She adds, “J-WAFS is excited to work with these teams, and we look forward to seeing their impact on the water and food sectors.”

      The J-WAFS Solutions program is implemented in collaboration with Community Jameel, the global philanthropic organization founded by Mohammed Jameel ’78, and is supported by the MIT Venture Mentoring Service and the iCorps New England Regional Innovation Node at MIT.

      Mobile evaporative cooling rooms for vegetable preservation

      Food waste is a persistent problem across food systems supply chains, as 30-50 percent of food produced is lost before it reaches the table. The problem is compounded in areas without access to the refrigeration necessary to store food after it is harvested. Hot and dry climates in particular struggle to preserve food before it reaches consumers. A team led by Daniel Frey, faculty director for research at MIT D-Lab and professor of mechanical engineering, has pioneered a new approach to enable farmers to better preserve their produce and improve access to nutritious food in the community. The team includes Leon Glicksman, professor of building technology and mechanical engineering, and Eric Verploegen, a research engineer in MIT D-Lab.

      Instead of relying on traditional refrigeration with high energy and cost requirements, the team is utilizing forced-air evaporative cooling chambers. Their design, based on retrofitting shipping containers, will provide a lower-cost, better-performing solution enabling farmers to chill their produce without access to power. The research team was previously funded by J-WAFS through two different grants in 2019 to develop the off-grid technology in collaboration with researchers at the University of Nairobi and the Collectives for Integrated Livelihood Initiatives (CInI), Jamshedpur. Now, the cooling rooms are ready for pilot testing, which the MIT team will conduct with rural farmers in Kenya and India. The MIT team will deploy and test the storage chambers through collaborations with two Kenyan social enterprises and a nongovernmental organization in Gujarat, India. 

      Off-grid portable ion concentration polarization desalination unit

      Shrinking aquifers, polluted rivers, and increased drought are making fresh drinking water increasingly scarce, driving the need for improved desalination technologies. The water purifiers market, which was $45 billion in 2019, is expected to grow to $90.1 billion in 2025. However, current products on the market are limited in scope, in that they are designed to treat water that is already relatively low in salinity, and do not account for lead contamination or other technical challenges. A better solution is required to ensure access to clean and safe drinking water in the face of water shortages. 

      A team led by Jongyoon Han, professor of biological engineering and electrical engineering at MIT, has developed a portable desalination unit that utilizes an ion concentration polarization process. The compact and lightweight unit has the ability to remove dissolved and suspended solids from brackish water at a rate of one liter per hour, both in installed and remote field settings. The unit was featured in an award-winning video in the 2021 J-WAFS World Water Day Video Competition: MIT Research for a Water Secure Future. The team plans to develop the next-generation prototype of the desalination unit alongside a mass-production strategy and business model.

      Converting dairy industry waste into food and feed ingredients

      One of the trendiest foods in the last decade, Greek yogurt, has a hidden dark side: acid whey. This low-pH, liquid by-product of yogurt production has been a growing problem for producers, as untreated disposal of the whey can pose environmental risks due to its high organic content and acidic odor.

      With an estimated 3 million tons of acid whey generated in the United States each year, MIT researchers saw an opportunity to turn waste into a valuable resource for our food systems. Led by the Willard Henry Dow Professor in Chemical Engineering, Gregory Stephanopoulos, and Anthony J. Sinskey, professor of microbiology, the researchers are utilizing metabolic engineering to turn acid whey into carotenoids, the yellow and orange organic pigments found naturally in carrots, autumn leaves, and salmon. The team is hoping that these carotenoids can be utilized as food supplements or feed additives to make the most of what otherwise would have been wasted. More