Industry News

Developments in AI within the Chemical Process Industry

Rich data sets have accumulated within the chemical process industry (CPI) throughout many years of production, and now, because of this data, there is a push to start utilizing artificial intelligence (AI) in modeling, optimization, advanced control, debottlenecking, troubleshooting, and more. AI/CPI is being looked at if a first-principles-based approach cannot efficiently solve the problem.

However, it’s not necessarily easy to interpret the AI data models, which hinders acceptance and adoption from most of the community. Developing an effective process is not an easy feat for the CPI. Budget constraints, time, human resources, and limited pilot tests are roadblocks for AI/CPI development. 

Design engineers can use process simulation packages to run different scenarios or build in-house models; however, the process’s inevitability becomes inefficient and suboptimal while also losing control of essential variables. 

Additionally, workable issues may not be uncovered until the mature product development stage. As the AI/CPI market conditions change, the original design purpose may not fit the intended use-case for the final product, making it hard to forecast reliable system processes. These factors make the developing AI/CPI industry challenging to predict and indicates that it’s only just developing. 

To learn more about specific discoveries such as models and datasets, explainable AI, and case studies within the AI/CPI industry, read the article here