Process Engineer

The Future
of Refining

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Explainer Video

The Why

As a process engineer in the refining industry, you know that achieving your goals can be challenging. You may be struggling with manual work, difficulty working with operations, and the lack of the proper tools and applications to monitor and analyze unit performance efficiently and effectively. But Imubit has the solution you need to overcome these challenges and succeed.

Case Study

The How

Learn how a refiner applied Imubit’s Deep Learning Process Control technology to a hydrocracker unit for yield improvement while respecting safe operating limits.


Kick-off to
closed loop

DLPC Development, which includes Scoping, Data Cleaning, Model Training, and client approvals throughout the entire process, took 5
months. With 1 month of commissioning the model at the plant, the total timeline till Closed Loop was 6 months.


The AI Journey: Optimizing FCCUs

The implementation journey of a customer’s use of Imubit’s AI optimization solution in a plant is the focus of this webinar. The entire process is covered, from defining the plant’s objective to achieving improved yield performance while managing key constraints through direct control. The webinar also showcases the benefits of using deep learning models to understand unmeasured disturbances. Watch to gain insight into the process of applying AI to capture a plant’s full value potential.


How does the DLPC model account for process dynamics?

DLPC uses historical data to learn the dynamic relationships between model variables in order to predict and control constraints effectively when running in a closed loop. During model training, DLPC learns a multitude of gains (process relationships) and understands time dynamics for each independent-dependent relationship through the process of predicting dependent variables dynamically. Through this process, DLPC now has a process model that reflects the reality of the plant. Because DLPC is learning relationships based on actual process historical data, the process dynamics and nonlinearity is captured during training.

How does the DLPC model account for process upsets or shutdowns?

During model development, time periods that include process upsets or unit shutdowns/startups are removed from the training data. Minimum and maximum threshold values that are outside of normal operating range for different process variables (e.g. flow, temperature, pressure) are also excluded from the training data if deemed unrepresentative of the process system.

How does the DLPC model account for major operational changes?

For major operational changes or shifts, the DLPC model will likely need to be retrained with data that includes the new regime. On average, 2-3 months of high-quality data is required, but the actual amount of data depends on the change. If the operational shift has occurred in history, and the model was trained with the relevant historical data, it is likely that the model performance will not degrade. If the operational change or strategy is new, the Imubit team will evaluate the scope of the change and align with the client to determine the path forward.

How much data is required to train a DLPC model?

The data required to train a DLPC model needs to adequately represent the process, which can be highly dependent on data compression rates or recent operational changes. Imubit recommends using at least 3-4 months of high quality (ideally, uncompressed) data to train and develop a robust model. While there is no limit on the maximum amount of data, models are typically trained with 2-5 years of historical data. If there is a significant process shift in the unit (i.e. turnaround, modifications to equipment), an average of 2-3 months of high-quality data with the new operating regime is recommended for a retrain. For inferentials, Imubit recommends a minimum of 200 lab sample data points, but this also depends on data quality.

How does DLPC respect constraints?

DLPC is trained to not violate Manipulated Variable (MV) operating limits and to avoid Control Variable (CV) operating limit violation. The on-site DLPC Application provides a second layer of protection that will prevent the DLPC from violating MV bounds during closed loop. DLPC will only write out a setpoint within the specified upper and lower bounds. CV bounds are respected and will take precedence over optimization objectives.

How does DLPC respect mass balances?

Booking Tunes

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