Refineries
AI technology for the most complex refinery processes
The Imubit Closed Loop Neural Network™ can optimize the most complicated – and profitable – processes at your refinery
Are you looking for a competitive edge in today’s low-margin refining market? Whether your feedstock is relatively consistent or varies wildly, your economic and operation teams make weekly, daily and hourly adjustments to each unit in an effort to optimize each process. But there are some processes that are just too hard to optimize with the current tools available to you.
Today’s market presents so many ever-changing factors, from varying feedstock composition and equipment disturbances, to regulatory and environmental variations – it’s become impossible to capture all the dynamic relationships with your current models. That’s exactly why we built Imubit – the first artificial intelligence (AI) closed-loop neural network technology developed specifically for oil refineries and other hydrocarbon processors.
Our AI process optimization technology helps refiners break through these optimization challenges. It’s not a generic AI solution targeting all industries, but next-generation artificial intelligence that focuses on the nuances of how oil refineries operate and can be run in closed-loop driven by planners with full operator controllability.
Featured deep learning application for refineries
Over the last few years, we have developed 6 types of strategic refining applications and field proven them with our tier-1 refining clients. These applications are uniquely tailored to our Closed Loop Neural Network™ technology, and we consider them highly proprietary.
Request access to our strategic DLPC refining application catalog ->
Learn about how Imubit’s AI technology helps refineries discover, engineer and monetize new facility margin opportunities:
Generalized first-principle economic models
for key chemical processes like blending, fractionation, conversion and reforming. The models consider unit constraints, operation modes, feeds, products, and fundamental behaviors of process units.
Steady-state baseline models
that estimate in real time the potential benefit from engaging closed-loop DLPC control. Once DLPC is commissioned in closed-loop, we use the baseline models to assess the value it created over time as well as the potential loss if it were disengaged.
Performance dashboards
let you track the unique KPIs for each AI application, perform economic debottlenecking and analyze constraints. Our dashboards help you devise strategies to adapt to feedstock, equipment and global or regional economic changes.
Process modeling platform
that leverages your process understanding, historical and ongoing data to analyze your process and regulatory control and train your deep learning prediction models.
Deep learning process models
capture the hidden governing dynamics between variables in all process states and model the relationships between feed properties, key process variables, operational constraints, and economic objectives.
Dynamic relationships visualization
of Monte Carlo simulations on the trained models show the learnt relationships between process model variables as well as model prediction errors, so your process engineers can see and understand how the model works.
