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 manufacturing industry is one of the most energy-intensive industries and is a major source of global greenhouse gas emissions. These environmental considerations are spurring the development and adoption of emerging technologies in the chemical manufacturing industry with the goal of reducing 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 manufacturing 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 sought to address the data and analysis gaps that are critical for encouraging the adoption of artificial intelligence in chemical manufacturing. The researchers developed a metric-based framework to quantify the energy and environmental impacts of artificial intelligence applications in the industry.
The specific objectives of the research project were to (1) analyze and develop baseline energy and environmental footprints for commodity chemical manufacturing; (2) identify unit operations, processes, and product lines that would be amenable to the short- and long-term adoption of artificial intelligence; (3) develop future use scenarios and estimate the potential impacts of artificial intelligence deployment on industry-wide energy consumption and environmental impacts; and (4) perform uncertainty and sensitivity analysis to identify the risks and drivers of the impacts.