Investigating the Energy and Environmental Implications of Artificial Intelligence Applications in the Chemical Manufacturing Industry
Assistant Professor of Industrial Ecology and Sustainable Systems, Yale School of the Environment
The chemical industry is one of the most energy-intensive manufacturing industries and a major source of global greenhouse gas emissions. These environmental problems are spurring the development and adoption of emerging technologies in the chemical industry to reduce energy consumption and carbon emissions.
Artificial Intelligence is one of the emerging technologies that has the potential to greatly reduce the energy consumption and carbon emissions of the chemical industry. However, the lack of credible performance analysis data and baseline information can deter early adopters, whose investments are crucial for accelerating deployment.
This research project aims to address the data and analysis gaps that are so critical for encouraging the adoption of artificial intelligence in chemical manufacturing. The researchers will develop a metric-based framework to quantify the energy and environmental impacts of artificial intelligence applications in the chemical industry.
The specific objectives of the project include (1) analyzing and developing baseline energy and environmental footprints for commodity chemical manufacturing; (2) identifying unit operations, processes, and product lines that will be amenable to the short- and long-term adoption of artificial intelligence; (3) developing future use scenarios and estimating the potential impacts of artificial intelligence deployment on industry-wide energy consumption and environmental impacts; and (4) performing uncertainty and sensitivity analysis to identify the risks and drivers of the impacts.