Flipping the switch: carbon-negative and water-positive data centers through waste heat utilization

IF 30.8 1区 材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Carlos D. Díaz-Marín and Zachary J. Berquist
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Abstract

Artificial intelligence (AI) growth poses major electricity, emissions, and water challenges. Globally, AI data centers are projected to demand Gigawatts of electricity, leading to Gigatons of carbon dioxide emissions and trillions of gallons of water consumed per year. With increasing deployment of high efficiency chip cooling, which in turn raises the waste heat temperature, data center waste heat could become a Gigawatt-scale energy resource. In this perspective, we analyze the various options for using data center heat from a thermodynamic, revenue, and emissions perspective. We show that direct air capture and thermal water purification are highly promising due to their ability to efficiently capture/avoid CO2 while producing a valuable product. Using data center heat for other purposes such as heating, cooling, electricity conversion, or atmospheric water production, are shown to have lower potential for emissions reduction and economic benefit. We then discuss the advantages of waste heat-powered direct air capture and water production compared to incumbent carbon capture and desalination approaches. Importantly, we highlight key technological and scientific opportunities that can enable these impactful end uses. Lastly, we propose a new data center metric, the Energy Use Efficiency (EUE), which incentivizes waste heat reuse and shows that data centers with heat utilization can be carbon-negative and water-positive, addressing major sustainability challenges of AI.

Abstract Image

翻转开关:通过废热利用实现碳负和水正数据中心
人工智能(AI)的发展给电力、排放和水资源带来了重大挑战。在全球范围内,人工智能数据中心预计将需要千兆瓦的电力,导致每年数十亿吨的二氧化碳排放和数万亿加仑的水消耗。随着越来越多的高效芯片冷却系统的部署,这反过来又提高了废热温度,数据中心的废热可能成为千兆瓦级的能源资源。从这个角度来看,我们将从热力学、收入和排放的角度分析使用数据中心热量的各种选择。我们表明,直接空气捕获和热水净化是非常有前途的,因为它们能够有效地捕获/避免二氧化碳,同时产生有价值的产品。将数据中心的热量用于其他目的,如加热、冷却、电力转换或大气制水,在减少排放和经济效益方面的潜力较低。然后,我们讨论了与现有的碳捕获和海水淡化方法相比,废热驱动的直接空气捕获和水生产的优势。重要的是,我们强调了能够实现这些有影响力的最终用途的关键技术和科学机会。最后,我们提出了一个新的数据中心指标,即能源利用效率(EUE),它激励废热再利用,并表明热利用的数据中心可以是碳负和水正的,解决了人工智能的主要可持续性挑战。
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来源期刊
Energy & Environmental Science
Energy & Environmental Science 化学-工程:化工
CiteScore
50.50
自引率
2.20%
发文量
349
审稿时长
2.2 months
期刊介绍: Energy & Environmental Science, a peer-reviewed scientific journal, publishes original research and review articles covering interdisciplinary topics in the (bio)chemical and (bio)physical sciences, as well as chemical engineering disciplines. Published monthly by the Royal Society of Chemistry (RSC), a not-for-profit publisher, Energy & Environmental Science is recognized as a leading journal. It boasts an impressive impact factor of 8.500 as of 2009, ranking 8th among 140 journals in the category "Chemistry, Multidisciplinary," second among 71 journals in "Energy & Fuels," second among 128 journals in "Engineering, Chemical," and first among 181 scientific journals in "Environmental Sciences." Energy & Environmental Science publishes various types of articles, including Research Papers (original scientific work), Review Articles, Perspectives, and Minireviews (feature review-type articles of broad interest), Communications (original scientific work of an urgent nature), Opinions (personal, often speculative viewpoints or hypotheses on current topics), and Analysis Articles (in-depth examination of energy-related issues).
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