To adapt to this continuously changing macroeconomic environment chemical producers are focusing on operational efficiency. AI is paving a flexible path to grow margins by shifting yields, reducing variability, and improving quality.
When you’re starting with costly catalyst and raw materials, identifying and running at the optimum reactor temperature to maximize conversion for the current fouling level is a 7-figure task.
Imubit AI models learn these complex, dynamic relationships and put them to work in closed loop to take this million dollar weight off your shoulders.
Off-spec accounts for 5-15% of total production, and can be even higher in specialty units. In polymers production, where so many products involve slow-sampled properties tailored to specific end applications, physics-based simulators simply aren’t good enough.
Imubit AI models learn the relationships first principles struggle to represent and put them into closed loop to reduce off-spec losses by more than 2% for most polymers processes.
Running more efficiently, reducing downtime and maximizing yields, requires less energy input per pound of finished product. Imubit’s closed loop AIO models have helped companies reduce natural gas consumption by 15% while improving product yields.
The models also learn the relationships between operating strategies and energy consumption, and help build carbon reduction into the justification basis of every future process debottlenecking project.
“It’s very exciting to see a plant that’s been around for 60 some years changing the way that it operates fundamentally. This is a new level of optimization that we’re embarking on with Imubit’s help.”
Discover how industry leaders like Marathon Petroleum are democratizing AI across operations.
Discover how flexibility is the secret to process optimization in Hydrocarbon Engineering.
Prove the value of AI optimization at your plant—at no cost. Uncover AI’s potential, based on your unit and your site-specific economics, in this free assessment.