GAS PROCESSING AND NGL

AI software for gas processing plants

Deep Learning Process Control® can discover new optimization opportunities on your most dynamic processes
Natural gas processing is a complex industrial process that produces a pipeline quality dry gas from field produced gas or other feedstocks. Small changes in a process – from temperature to flow to composition – must be constantly monitored and controlled.

Natural gas liquids (NGL) are a valuable by-product of gas processing. NGL products fuel every major industry – from construction and industrial manufacturing, to transportation, heating and cooling. The transformation of natural gas liquids into products ranging from plastics and rubbers to fuel additives, refrigerants and heating fuel is a complex process with constant adjustments to accommodate different inputs and optimize in the face of constantly changing market dynamics.

Turning the various ethane, propane, butane, isobutane and pentane inputs into these standardized NGL products isn’t easy. The current relational shifts in energy prices and chemical markets makes economic modelling, optimizing NGL recovery, and improving margins more important than ever.

It is difficult to identify and model optimal processing conditions – especially given the long-time dynamics between the feed and the downstream columns. Add in the ever-changing feed composition and it’s not a surprise that traditional modelling hits a breaking point with NGL and gas plants.

That’s exactly why we built Imubit – the first AI software built for natural gas liquids processing plants.

Our process optimization artificial intelligence (AI) software – helps NGL and gas processors break through these optimization challenges. It’s not a generic AI software offered to all industries, but next-level artificial intelligence that understands the nuances of how NGL processing plants work. Imubit is built to handle the wide range of NGL processing complexities by enabling economic modelling and truly dynamic process optimization to accurately predict and set optimal target points.

Working with our gas processing clients, we have figured out how to optimally apply Deep Learning Process Control® to their NGL fractionators.

Request Access to our DLPC NGL fractionator application sheet >

Learn about how Imubit’s AI software helps gas processing plants discover, engineer and monetize new by-product 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.

Pre-optimized truly dynamic controller

is designed, trained and continuously improving based on your manipulated variables, constraints, objective function, and real-time stream pricing.

Open-loop simulations

let your team visualize the dynamic controller moves and predictions in every desired historical period, run what-if simulations for various operating scenarios, and fully validate the controller’s behavior before commissioning the controller in closed-loop.

Process control network software

is installed, configured and continually supported to run your controller inside your air-gapped secure environment. Our on-premise software is fully compliant to process control network cybersecurity and reliability requirements and supports all major DCS vendors.

Control room application

gives operators the ability to directly update controller constraints and move limits, while engineers can update constraint operator ranges and control priorities.

Customizable dashboards

provide chart and matrix views for operators to understand controller status, projected moves, and prediction trajectories. Multi-unit control room dashboards show data across all controllers, all units and every process in one place.

Remote monitoring

via our cyber-secure proprietary network protocols enables those not in the control room to run safe cloud-based simulations and analyses on your production data. No data is ever transmitted into your control network.
Imubit’s team understands what we’re trying to do from an operating and an economics perspective. Their Deep Learning Process Control® technology and their approach have aligned everyone from planning and operations around a shared goal of maximizing production profits.”

-Process control and optimization manager

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