Project 3: Efficient Product Yield Thanks to AI
Another project is being led by data scientists Hessam Ramezani and Rasit Faller under the title “AI-Driven Production Optimization.” As part of FORWARD!, the production process for the raw material NaMBT in Kallo was also put to the test. This raw material is required for the production of intermediates by the Rhein Chemie BU. In an initial analysis, the data scientists, together with the process experts, determined that a significant percentage of the expensive raw material aniline doesn’t end up in the target product during production. “It is bound in the form of resins in an unwanted byproduct and ultimately burned up,” explains Faller.In light of the fact that the raw material aniline is the most expensive component in the manufacturing process, this was simply unacceptable. “With an AI-assisted model that we built using DIAP, we have now been able to optimize the production process so that we lose a third less aniline,” says Ramezani. But that’s not all: “We’re now trying to find further process parameters with the help of AI in order to get even more aniline into the end product,” Ramezani explains.