Research talks: Research partners on innovation for carbon neutralization

Опубликовано 8 февраля 2022, 16:10

Research talks: Research partners on innovation for carbon neutralization
0:00 Session overview
Speaker: Beibei Shi, Senior Research Program Manager, Microsoft Research Asia

3:52 Mimicking atmospheric processes of CO2 with a physical-informed deep neural network
Speaker: Jia Xing, Associate Professor, Tsinghua University

It’s crucial to reduce CO2 emissions worldwide to mitigate and avoid the risks stemming from global climate change. Observations from satellite images can directly measure the global CO2 concentration at a high resolution, and they enable us to track the progress of CO2 controls globally. In this talk, we propose novel deep neural networks (DNNs) to capture the nonlinear relationship between ambient CO2 concentration and CO2 emissions.

17:47 Real-time monitoring of global CO2 emissions and the negative carbon computing
Speaker: Zhu Liu, Associate Professor, Tsinghua University

The diurnal cycle of CO2 emissions from fossil fuel combustion and cement production reflect seasonality, weather conditions, working days, and more recently the impact of the COVID-19 pandemic. In this research talk, we discuss how we’re able to provide, for the first time, a daily CO2 emission dataset for all of 2020 calculated from inventory and near real-time activity data for power generation (29 countries), industry (73 countries), road transportation (406 cities), aviation and maritime transportation, and residential fuel use sectors (estimated for 206 countries).

34:43 Forest fire prediction in the developing world: The power of machine learning
Speaker: Kuldeep S. Meel, Presidential Young Professor, National University of Singapore

Deforestation and climate change have dramatically increased the number of forest fires across the globe. In Southeast Asia, Indonesia has been most affected by tropical peatland forest fires. These fires have a significant impact on the climate, which results in extensive health, social, and economic problems in societies. In this talk, I will discuss a cost-effective machine learning-based approach that uses remote sensing data to predict forest fires in Indonesia.

47:05 Offshore CO2 storage in the form of gas hydrate
Speaker: Toru Sato, Professor, University of Tokyo

Carbon capture and storage (CCS) is a promising technique for reducing significant amount of CO2. In this talk, we’ll discuss our research goals of developing a multi-scale simulator based on a reservoir-scale simulator using neural networks trained with large numbers of data resulting from pore-scale simulations.

1:00:29 Low carbon transformation pathway for China’s coal-powered plants
Speaker: Xinran Wei, Researcher, Microsoft Research Asia

In China, about 4,600 coal-powered plants need to make a retrofitting decision between 2025 and 2060 to realize the country’s carbon-neutrality goal. However, the heterogeneity of the power plants requires that these decisions be made plant-by-plant to achieve their respective emission reduction target in the most cost-effective way. In this talk, we discuss how the computing resources and advanced machine learning algorithms of Microsoft Research Asia can greatly facilitate finding the optimal solution for this problem.

1:11:43 The next generation of GNN: From molecular modeling to carbon removal
Speaker: Shuxing Zheng, Senior Researcher, Microsoft Research Asia

In this talk, we’ll introduce Graphormer, the next generation graph neural network (GNN), which won the first prize in KDD Cup 2021 for the OGB-LSC challenge (quantum chemistry track). We’ll briefly introduce the background of carbon removal material discovery. We’ll then discuss the Graphormer, a powerful GNN built on standard transformer architecture and equipped with three simple and effective structural encodings. We’ll also explore the potential impacts of the many graph-related applications of sustainability.

Learn more about the 2021 Microsoft Research Summit: Aka.ms/researchsummit