Abdulaziz El-Haram

Life cycle assessment of renewable chemical production with AI

The project: 

Sustainability is an ever-increasing concern within the chemical industry, making the best use of materials, reducing unnecessary waste and carbon emissions are a huge factor in the production of new chemicals. While life cycle inventory (LCI) data is available for a wide range of chemicals and is typically used to identify assess environmental performance, identify “hot spots” in manufacturing, and enhance efficiency, there are many gaps within current datasets that need to be filled that are critical to a more accurate evaluation. Current methods for filling in these blanks is through the use of generic proxies which can be inaccurate and lead to poor decisions, and in turn lead to an unoptimised process.

One solution is to, with the help of Artificial intelligence (AI) create data that is more accurate than the proxies currently being used. This works by using AI in combination with chemical process simulators such as Aspen Plus or UNISIM to produce data that is relevant to that specific chemical process, instead of simple regression techniques. This can help when targeting certain “hot spot” chemicals that can be very energy and/or resource intensive, and looking at ways to reduce the environmental impact, thus reducing the overall impact of chemicals on the environment.

This PhD will build upon current smaller scale models - with help from the above methodology - to create a model that can rapidly assess chemicals, provide precise and accurate LCI data, and help with environmental assessment.