Sayandeep Biswas | May 12, 12-1PM | 3-001
Long-haul trucking accounts for 7.1% of the total GHG emissions and is classified as a tough sector to decarbonize given the lack of alternatives to diesel. Unlike the light-duty market, battery electric options are unsuitable for long-haul trucks, primarily due to the high energy requirements. Hydrogen has been identified as a promising alternative fuel, however it needs to be transported as a cryogenic liquid or compressed gas adding significant cost to the delivered product. LOHCs have the advantage of being room temperature liquids that integrate with existing fuel infrastructure and provide means to move hydrogen cheaply. The major point for LOHCs is the endothermic dehydrogenation step required to release the hydrogen which requires energy equivalent to 30% of the lower heating value of the released hydrogen. in this talk
Sayandeep Biswas is a third year graduate student pursuing a PhD in chemical engineering. Currently, he works with Prof. William H. Green and his focus is to develop hydrogen carriers and utilization methods to aid decarbonization efforts. Previously, he also worked with Prof. Karthish Manthiram where he explored the use of ammonium formats as means of storing and transporting energy as a high-energy density, stable solid which could be decomposed to release hydrogen and ammonia at low voltages. During his undergrad, Sayandeep worked with Prof. Alon McCormick at the University of Minnesota, where he worked on developing absorbent materials that captured ammonia from an ammonia, hydrogen gas mixture. The intended application was to reduce the energy required to separate ammonia during the Haber-Bosch process, especially for small-medium scale plants.
PAST SPEAKERS
Mollie Wilkinson | April 21, 12-1PM | 3-001
Methane is a powerful and threatening greenhouse gas with a 20-year global warming potential 84 times that of carbon dioxide. Its emissions represent 20% of global annual emissions and it is the second-most abundant greenhouse gas. Despite its rapidly increasing emissions, it is generally present in low ambient concentrations of <2000 parts per billion (.0002%), making atmospheric elimination strategies extremely difficult. However, elevated methane levels of .5%-2% are commonly reached in industrial settings (eg dairy barns and coal mines). The Plata Lab at MIT has developed a highly promising ambient concentration, low temperature methane oxidation catalyst for these industrial conditions. This talk contexualizes the methane problem, the Plata Lab catalyst as a potential solution,
Mollie Wilkinson is a 2023 Climate, Environment, and Sustainability Masters Candidate in Professor Desiree Plata's lab. Her research is focused on developing a copper-zeolite methane removal catalyst. Previously, she has also worked as a deeptech and sustainability investor at Anzu Partners, and worked as a materials engineer at various institutions developing: aerogel-based insulating materials at AeroShield Materials; a photocatalytic chemical reactor at Syzygy Plasmonics; and low-cost heat-resistant polymer adhesive formulations at SpaceX. While she is not formulating new sustainable materials for decarbonizing industry, she can often be found formulating new cocktail and cooking recipes for her friends.
Thomas Lee and Leonard Boussioux | April 28, 12-1PM | 3-001
This is a special session featuring two speakers involved in data science methods for sustainability. For Thomas's portion of the talk: Long duration energy storage may play a crucial role in future energy systems, in order to alleviate renewable energy shortages on multi-day and even seasonal timescales. However, most system planning models assume perfect foresight and socially optimal dispatch for storage resources, due to computational tractability limits. This part of the talk will reexamine the realism of this perfect foresight assumption, by using a stochastic optimization model with multistage uncertainty (stochastic dual dynamic programming) along with a multi-decade climate reanalysis dataset. For Leonard's : This talk presents two methodologies integrating optimization, machine learning, and AI to tackle sustainability challenges. First, we introduce adaptive robust optimization for ensemble modeling in time series forecasting, improving performance and decision-making under uncertainty. Second, we explore transforming high-dimensional, noisy, unstructured data into predictive and prescriptive analytics using a data-driven framework - Gather, Extract, Predict. We demonstrate the superiority of these multimodal systems over single-modality approaches in real-world applications, such as healthcare operations and hurricane forecasting.
Thomas Lee is a 2nd year PhD student in MIT's Institute for Data, Systems, and Society, focused on optimization methods for energy systems and markets. He conducts research in Professor Andy Sun's lab in the Operations Research Center and through the MIT Energy Initiative. Prior to MIT, Thomas worked in power trading covering PJM and CAISO at Boston Energy Trading and Marketing (part of Mitsubishi Corporation), and developed energy storage valuation models at Amp Energy for a global investment portfolio.
Léonard Boussioux , a final year Ph.D. student at MIT under Prof. Dimitris Bertsimas, is an incoming Assistant Professor at the University of Washington in Information Systems and Operations Management. His research addresses healthcare and environmental issues using operations research, machine learning, and deep learning. He creates multimodal and prescriptive tools for integrating sustainability in business and policy. Collaborating with Google X, MIT Jameel Clinic, Harvard Medical School, OCP Group, and others, he's deployed AI and analytics models for healthcare, pollution management, and ecosystem conservation.
Luis Gonzalez-Portillo | May 5, 12-1PM | 3-333
Concentrating Solar Power (CSP) is a promising source of renewable energy due to its ability to integrate storage and generate electricity at a low cost. Next-generation CSP plants utilize central receivers to achieve high temperatures required by high-efficiency power cycles, and alternative heat transfer fluids such as particles are being explored to achieve these hightemperatures without degradation. This seminar explains the potential cost of electricity for particle-based systems, using a techno-economic model developed by Universidad Politécnicade Madrid and Sandia National Laboratories. Trade-offs, interaction between components, and optimum configurations are discussed, with a focus on the work of Professor González-Portillo.
Luis González-Portillo is Associate Professor at Universidad Politécnica de Madrid and currently Visiting Researcher at Massachusetts Institute of Technology (MIT). He has dedicated his research to Concentrating Solar Power (CSP) since 2016, focusing on the techno-economic analysis of next-generation plants. Professor González-Portillo has participated in several international projects in this field, including the European H2020 project “Application of Solar Thermal Energy to Processes” and the US project “Generation 3 Concentrating Solar Power Systems” funded by DOE. Currently, he is engaged in developing a new heat exchanger capable of handling ultra-high temperatures at MIT. While not teaching or researching renewable energies, he enjoys climbing and playing guitar (although not both at the same time).
