How AI Brings Precision Manufacturing to Chemical Processes

Presented at the AIChE Spring Meeting & 18th Global Congress on Process Safety

In this case study presentation, learn how a food manufacturer has implemented AI to accurately predict when the production of its animal feed additive product was completed in order to reduce batch-to-batch variability and maximize asset utilization.

The customer is a Fortune 100 Food Production company, serving clients in more than 160 countries. With a global value chain that includes over 400 crop procurement locations, 250 ingredient manufacturing facilities and numerous innovation centers, the company's products are used for food, animal feed, industrial and energy uses. As part of their strategy to improve profitability, one of the company's Ag Processing business units was looking for ways to reduce costs and increase yield in order to improve profit margins for a particular animal feed additive. However, this is a process whereby the yield and production time are vastly impacted by a number of variables that fluctuate depending on the operating conditions, process knowledge, and individual operator. The business unit wanted to leverage predictive analytics to reduce the variability in the process, ultimately lowering production costs and increasing asset utilization.

This presentation will show that AI-powered predictions are being utilized to manage chemical processes today. The audience will learn how to overcome the key barriers to AI success and why the three Ps: people, process, and AI product are critical to success.