Data Center and AI Energy Reduction Vijay N. Gadepally Senior Scientist and Principal Investigator, Supercomputing Center at MIT Lincoln Laboratory
The energy requirements of data centers in the United States is on the order of millions of tons of carbon dioxide annually, and the demand is forecasted to increase significantly over the coming years. In this presentation, Dr. Vijay Gadepally of MIT’s Lincoln Laboratory will share strategies for reducing energy use of high-performance computing applications, improving energy transparency, and incentivizing data center users to reduce their carbon footprint.
Decarbonizing Chemical Manufacturing Yogesh Surendranath Donner Professor of Science, MIT Department of Chemistry
The chemical industry is the major source of carbon emissions, requiring new technologies for disruptive decarbonization. The direct and selectivity electrochemical synthesis of commodity chemicals from CO2 could play a key role in decarbonizing chemical manufacturing. However, many key chemicals are accessible over a narrow range in electrochemical potential, requiring general design principles for controlling kinetic branching in these reactions. We have uncovered the central role of the reaction environment in facilitating selective CO2 reduction at electrode surfaces and have employed electrolyte design to alter the mechanistic profile of chemical synthesis. Our latest findings in this area will be discussed.
Fireside Chat: The Business of Sustainability Moderator: Jason Jay Senior Lecturer, MIT Sloan School of Management Director, Sustainability Initiative at MIT Sloan
Panelists: Mahesh Jayakumar Research Analyst, ESG, MFS Investment Management
Dimitris Bountolos Chief Information And Innovation Officer (CIIO), Ferrovial
MIT Climate & Sustainability Consortium Project Highlights Introduction and Update Jeremy Gregory Executive Director, MIT Climate & Sustainability Consortium
The Climate and Sustainability Implications of Generative AI Noman Bashir Computing & Climate Impact Fellow, MIT Climate & Sustainability Consortium
The rapid expansion of generative artificial intelligence (Gen-AI) neglects consideration of negative effects alongside expected benefits. This incomplete cost calculation promotes unchecked growth and a risk of unjustified techno-optimism with potential environmental consequences, including expanding demand for computing power, larger carbon footprints, and an accelerated depletion of natural resources. The current siloed focus on efficiency improvements results instead in increased adoption without fundamentally considering the vast sustainability implications of Gen-AI.
In this talk, I will propose that responsible development of Gen-AI requires a focus on sustainability beyond only efficiency improvements and necessitates benefit-cost evaluation frameworks that encourage (or require) Gen-AI to develop in ways that support social and environmental sustainability goals alongside economic opportunity. However, a comprehensive value consideration is complex and requires detailed analysis, coordination, innovation, and adoption across diverse stakeholders. Engaging stakeholders, including technical and sociotechnical experts, corporate entities, policymakers, and civil society, in a benefit-cost analysis would foster development in the most urgent and impactful directions while reducing unsustainable practices. More details are in our white paper, which is accessible at MIT Gen-AI Sustainability White Paper.
A Cautionary Tale about Deep Learning-based Climate Emulators Björn Lütjens Postdoctoral Associate, MIT Department of Earth, Atmospheric, and Planetary Sciences
Climate models are computationally very expensive for exploring the impacts of climate policies. For example, simulating the impacts of a single policy emission scenario can take multiple weeks and cost hundreds of thousands of USD in computing. Compellingly, deep learning models can now forecast the weather in seconds rather than hours in comparison to conventional weather models and are being proposed to achieve similar reductions by approximating climate models. Climate approximations or emulators, however, have already been developed since the 1990s and I will present how we implemented a linear regression-based emulator that outperforms a novel 100M-parameter transformer-based deep learning emulator on the most common climate emulation benchmark. I will use our results to discuss more nuanced insights highlighting how chaotic dynamics influence emulator performance and use cases where deep-learning emulators can improve existing linear emulators.
Collaborative Development of an Interactive Decision Support Tool for Trucking Fleet Decarbonization Danika MacDonell Impact Fellow, MIT Climate & Sustainability Consortium
This presentation shares the journey of creating an interactive geospatial decision support tool in close collaboration with industry and academic partners of the MIT Climate & Sustainability Consortium. The tool leverages comprehensive public data on freight flows, costs, emissions, infrastructure, and regulatory incentives. Integrating key insights and methodologies from our partners, it aims to assist trucking industry stakeholders in identifying and assessing strategies to transition fleets to low-carbon energy carriers.
Decarbonizing Industry Lightning Talks Envisioning Commercial Air Transportation With Near-Zero Environmental Impacts Florian Allroggen Executive Director, MIT Laboratory for Aviation and the Environment Senior Strategic Advisor, MIT’s Department of Aeronautics and Astronautics
To meet ambitious environmental goals while continuing to connect the world, the air transportation sector needs to increase the level of ambition in mitigating its environmental impacts. In this talk, Dr. Allroggen outlines what an air transportation system with near-zero impact on global warming and air pollution could look like. For this purpose, he first provides a strategic perspective on the key impacts which need to be mitigated to meet near-zero goals. He then connects such mitigation measures to new technologies and operational measures which will target the most significant impacts. The analysis concludes by providing insights into the technical feasibility and economic viability of the resulting air transportation system which can achieve near-zero environmental impacts.
Sustainable Steel Cem Tasan POSCO Professor of Metallurgy, Department of Materials Science and Engineering
Solid state consolidation has tremendous potential for steel making from steel scrap, without remelting. In this talk, the scientific fundamentals and engineering solutions associated with a particular process invented at MIT will be introduced, focusing on the successful examples of several different ferrous and non-ferrous alloys.
Design and Computational Strategies for Reusable Building Components Caitlin Mueller Associate Professor, MIT Civil and Environmental Engineering Associate Professor, MIT Architecture
New computational design and digital fabrication methods for innovative, high-performance buildings and structures will enable a more sustainable and equitable future. By focusing on the creative interface of architecture, structural engineering, and computation, Prof. Mueller’s research group has developed strategies for unconventional material use in building structures.
This presentation will focus on algorithmic design approaches, such as those incorporating underutilized wood sources and reassembleable concrete parts. The PixelFrame system, for example, targets circularity strategies for reducing the material footprint of concrete. Connections are dry-jointed, avoiding the use of grout or mortar. The conventionally fused assembly of steel and concrete is separated, allowing each material to respond independently to tensile and compressive forces without impeding the longevity or function of the other. Through structural element reuse, PixelFrame can achieve more than 50% embodied carbon savings up-front.
Sustainable Transportation: Low Carbon Trucking Sayandeep Biswas PhD Graduate Student, MIT
Hydrogen is a promising fuel to drive the decarbonization of long-haul trucking. However, the high cost of distribution as a compressed gas or cryogenic liquid has stunted its wide-scale adoption. Liquid Organic Hydrogen Carriers (LOHCs) can be a cost-competitive option but have inefficiencies from endothermic dehydrogenation and compression needs. We are building a novel powertrain system to mitigate these drawbacks and establish LOHC as a cost-competitive diesel alternative.
Formate Economy and AI-Assisted Catalyst Search Ju Li Battelle Energy Alliance Professor, MIT Department of Nuclear Science & Engineering Professor, MIT Department of Materials Science and Engineering
Carbon efficiency is one of the most pressing problems of carbon dioxide electroreduction today. While there have been studies on anion exchange membrane electrolyzers with carbon dioxide (gas) and bipolar membrane electrolyzers with bicarbonate (aqueous) feedstocks, both suffer from low carbon efficiency. In anion exchange membrane electrolyzers, this is due to carbonate anion crossover, whereas in bipolar membrane electrolyzers, the exsolution of carbon dioxide (gas) from the bicarbonate solution is the culprit. Here, we first elucidate the root cause of the low carbon efficiency of liquid bicarbonate electrolyzers with thermodynamic calculations and then achieve carbon-efficient carbon dioxide electro- reduction by adopting a near-neutral-pH cation exchange membrane, a glass fiber intermediate layer, and carbon dioxide (gas) partial pressure management. We convert highly concentrated bicarbonate solution to solid formate fuel with a yield (carbon efficiency) of greater than 96%. A device test is demonstrated at 100 mA cmÀ2 with a full-cell voltage of 3.1 V for over 200 h. ["A carbon-efficient bicarbonate electrolyzer," Cell Reports Physical Science 4 (2023) 101662]
Extracting Value Where Others See Waste Kent S. Sorenson, Jr. Chief Technology Officer, Allonnia
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